Drug Discovery with Quantum Pharmaceuticals

The Blog has moved

2009-12-02T22:45:00.000-08:00

to another location. The blog feed is now merged with the Quantum Pharmaceuticals official site http://q-pharm.com. See you there!

The nature of phospholipid membranes repulsion at nm-distances

2009-08-31T01:14:00.000-07:00

Why hydrophobic membranes repel at small distances? We apply recently developed phenomenological theory of polar liquids to calculate the repulsive pressure between two hydrophilic membranes at nm-distances. We find that the repulsion does show up in the model and the solution to the problem fits the published experimental data well both qualitatively and quantitatively. Moreover, we find that the repulsion is practically independent on temperature, and thus put some extra weight in favor of the so called hydration over entropic hypothesis for the membranes interactions explanation. The calculation is a good “proof of concept” example a continuous water model application to non-trivial interactions on nm-size bodies in water arising from long-range correlations between the water molecules.
More details can be found here:

arXiv:0908.0632 [pdf, other]

The nature of phospholipid membranes repulsion at nm-distances

Authors: P. O. Fedichev, L.I. Menshikov

hERG binding correlates with LogP?

2009-06-20T01:18:00.000-07:00

Here is a very good analysis from human ERG blockers article:

The plot below (created usingVortex)shows pIC50 calculated from the literature IC50 data versus calculated logP determined usingalogPS, the colour coding shows the overall general tend of increasing hERG activity as logP increases, it also highlights the reduced liability seen with acids (green) and zwitterions (red).

Whilst the majority of data is derived from radioligand binding experiments (using either Dofetilide or MK-499), there is a substantial amount of data from patch clamp experiments, I collated enough data now (covering 4 orders of magnitude) to give an idea of how the assays compare. As you can see there is a reasonable correlation between the assays, but there are one or two outliers. Which is more predictive of in vivo activity is an excellent question that I don’t have the data to answer yet.

I still need to increase the size of the data-set, and if anyone can direct me to any publicly available data, or to publications that contain data i'd much appreciate it.
Worth reading, Medicinal Chemistry of hERG Optimizations: Highlights and Hang-Ups, Jamieson, Journal of Medicinal Chemistry, 2006, 5029

Molecular polarization on a polar liquid interface: the structure of a water surface

2009-04-06T11:45:00.001-07:00

The orientations of water molecules within the liquid depend on interplay of long-range dipole-dipole interaction and short range hydrogen-bonding interactions. At room temperature water is in paraelectric (disordered) phase and thus average dipole moment of any large enough fraction of the liquid vanishes.

This observation is not necessarily true at the liquid boundary. Since no molecules "like" to point at a hydrophobic interface direction (this would imply many uncompensated hydrogen bonds), most of the molecules orient along the liquid surface, the boundary between the liquid and a hydrophobic may become polarized (become essentially ferroelectric). Practically this amounts to a formation of stable network of hydrogen bonds on the interface.

The solution corresponding to such polarized boundary can be obtained in the very latest "incarnation" of the QUANTUM water model (See the attached figures). The first one demonstrates the density of the liquid starting from the gas phase on the left (model liquid density 0.3) and to the liquid phase (model density 1). The transition between the two phases is similar to that in classical model of van der Waals liquid.

The second graph corresponds to the polarization density (mean dipole moment of a liquid volume). Comparing the two graphs we see that the interface is indeed polarized and the polarization decays quickly into the bulk both of the gas and the liquid phase and, as the detailed calculation shows, contributes to the surface tension coefficient considerably.

In short we observe that depending on the ordering state of the water layer next to a molecular surface the effective surface tension may be different by a large number. Another observation suggests that the water density depletion next to a fully hydrophobic (and thus ordering) surface can be large (up to 30-50%)

New Quantum Water Model helps find stable ss DNA conformation in solution

2009-03-11T02:24:00.000-07:00

ss DNA Molecular dynamics trajectory using the latest Quantum force field helps to find a perfectly stable conformation of the biomolecule in solution. The outcome of the simulation is very reasonable, given the fact that ss DNA (such as telomers) tend indeed to form such loops (see the Figure on the right). The Figure shows the structure of a DNA quadruplex formed by telomere repeats. The conformation of the DNA backbone diverges significantly from the typical helical structure

Solvation energy of a large atom cluster: continuous solvation energy test - II

2009-01-23T12:16:00.000-08:00

As it has been already stated here, binding energy calculation of a small molecule to a large protein poses a difficult problem: a method for molecular electrostatic energy calculation should work well both for the protein ligand complex, the protein and the ligand at infinite separation. The protein and the complex are large molecules, whereas the ligand is, by definition, small.

Not every computational approach for the solvation energy calculation is fit for the job though. To elucidate the nature of the problems at hand we performed the following model calculation:
- we prepared a spherical "protein" of a large (but realistic) radius
- we placed a single-atom ligand with a charge at a given distance from the "protein" center (see the Figure)
- we calculated the solvation energy of the system as a function of the ligand distance both when the protein is neutral and charged (in the latter case the protein charge is opposite to that of the "ligand")

We used four different methods for the electrostatic contribution to the solvation energy calculation: Poisson equation solver (in its surface electrostatic incarnation, blue) QUANTUM MGB (cyan) and the two "classic" GB methods, based on the Coulomb approximation: HCT (magenta) and AGBNP (yellow).

The first Figure, corresponding to an overall electrically neutral cluster, shows absolute deficiency of HCT approach deep enough inside the "protein". The problem is caused by unrealistic assumptions with regard to the overlap integrals calculations is occurs pretty frequently in realistic proteins. AGBNP method represents one of the latest GB approaches and is practically free of these difficulties. However, AGBNP is based on Coulomb approximation and thus fails to recover correct behavior of the solvation energy close to the "protein" boundary: AGBNP energy is off by a large number from both QMGB and the exact solution. QMGB and Poisson solutions agree very well everywhere!

The last Figure shows the same calculation for a charged model "protein-ligand" complex. Once again, HCT fails entirely, AGBNP does not work properly at the "protein" boundary and both Poisson solver and QMGB agree very well, though QMGB is about one order of magnitude faster than the Poisson solver!

Practically all this means that QMGB represents a fast and accurate approximation to the Poisson equation solution. QMGB approach does not rely on Coulomb approximation and is shown to work both for small molecules and large molecular clusters involving molecules of very different sizes. Therefore, with QMGB one can find a single transferable set of GB parameters capable of describing large and small molecules on the same footing.

Solvation energy of a diatomic molecule: continuous solvation energy test - I

2009-01-23T01:06:00.000-08:00

Diatomic molecule is the simplest example of a realistic solvation energy calculation. Indeed, any reasonable solvation energy model gives exact value for a single atom.

Depending on the radii of the atoms involved the solvation energy of a pair may be a very good test of a solvation energy model and transferability of its parameters. In what follows we show the results of our Modified GB (MGB) approach for the test system. The graph below represents the solvation energies for similar and opposite charges pairs.

The upper (blue) solid curve represents the atom pair with opposite charges, whereas the lower (red) curve corresponds to the atoms with similar charges. First of all, the behavior of the two energies is easy to understand. At infinite separation both curves saturate at -0.125 (which is the Born solvation energy of the two charges of 0.5 and bare radii 2.). If the total charge is 0 (the opposite charges case, blue curve), at r=0 we have Es=0 as well. If the total charge is 2x0.5=1 (the red curve), then at r=0 we have Es=-0.25, as it should be for a combined charge within the sphere of radius 2.

Although the asymptotic values are OK, this does not mean the whole curve is fine. To compare our approach with true electrostatic we performed the calculation of the model system solving the Poisson equation as well as by two "classic" GB models (that of HCT and AGNP). The results for a diatomic molecule with zero total charge are represented on the last graph.

The electrostatic part of the solvation energy corresponds to the blue curve of the previous graph and is calculated either by a (surface-electrostatic) Poisson equation solver (blue), QUANTUM's MGB (cyan), AGBNP (yellow) and HCT GB model (yellow). As it is clear from here, all the approaches give very similar results for the "small" molecule and are practically indistinguishable. Indeed, it is well known that practically any sort of GB approximation gives good results for solvation energies of small molecules.

The difference between QMGB method and "classic" GB approaches and its relation to the exact solution becomes more obvious if we consider a charged diatomic molecule (a molecular ion with total charge, say, 1 placed on one of the atoms). The exact (blue) and QMGB (cyan), once again, are both in agreement with each other, whereas both "classic" GB approaches, HCT and AGBNP fail to recover correct asymptotic value at zero inter-atomic separation. The latter difference between GB solutions and the exact value of the solvation energy is not important for small molecules (low atom density) but is extremely important for ligand binding calculations (to be explained).

Self-consistent solvation energy contribution calculation for protein-ligand complexes

2009-01-13T03:20:00.000-08:00

Solvation energy is a major contribution to a ligand binding energy and is the interaction pretty much responsible for binding selectivity. Actual calculation of the solvation energy requires a method valid both for small molecule ligands and large proteins (and protein-ligand complexes).

Calculation of the electrostatic contributions for the binding energies in a continuous solvation energy approach imposes different problems for large and small molecules. Normally people use some kind of Generalized Born (GB) approximation. The latter is only exact for a charge in the center of a spherical cavity and thus can only be valid for a small molecule with most of the charge located within a few atoms.

If the molecule of interest is large, most of the charges are close to the molecular surface, instead. GB approximation in its most commonly accepted form fails next to a molecular surface: the Born radius is missed by a factor of 2. This means that there can be no "classic" GB model working good both for small and large molecules!

Binding affinity calculation requires calculation of differences between the energy of the complex (a large molecule) and the energies of the protein (another large molecule) and the ligand (a small molecule) at infinite separation.

If a GB model is made working by careful adjusting of "bare" Born radii to fit experimental IC50 of complexes, a good sanity check would require reproduction of experimentally known solvation energies of small molecules and ions (and the other way around). The two graphs in this post show, that this is indeed possible. A relatively large error in the small molecules solvation energies shows that although the resulting model is reasonable, the obtained GB parameters are only quantitatively transferable between large and small molecules.

How much water is in a Generalized Born protein?

2008-12-30T04:15:00.000-08:00

Born approximation is a weapon of choice for a (relatively) fast calculation of solvation energies in modeling. Although the approach is conceptually simple, it can not be correctly derived from first principles (i.e. does not correspond to a solution of electrostatic problem in a strict or even variational sense).

In practice applications of Generalized Born models are further complicated by various approximations for calculating volume (or surface) integrals, removing atom overlaps etc. What remains left is some sort of approximation to molecular volume (surface) and the so called Born Radii for every atom.

Each of the Born radii quantitatively shows a degree to which an atom is "buried" within the protein. The presented graph gives a simple idea to a which extent GB can even be used for description of solvation energies of a simple, model spherical protein containing approx. 1000 atoms of carbon.

The red squares give the dependence of the Born Radii on the atom positions. The points are obtained using our own implementation of AGBNP, one of the best realizations of GB procedures available in the literature.

The yellow curve represents exact result for a spherical protein, where GB and exact analytical expressions coinside. As one can see, AGBNP result fails to grow inwards and saturates at a very small value at r=0.

The reason for this behaviour is two-fold: first AGBNP is based on the so-called Coulomb approximation and thus can not be exact. Indeed, Coulomb approximation fails at the protein boundary and gives d(Born Radius)/dr twice as large as the exact result. This is a true problem, but it can not explain fundamentally wrong results in the protein center!

The other problem of AGBNP (and in fact any GB model), is that the model implies a certain approximation for molecular surface and the surface may have water filled cavities inside the protein! The cavities represent (within the same model) a medium with high dielectric constant and decrease the value of the Born radii.

To check the last assumption we searched for the water filled cavities removed them (to a certain adjustable extent). The result is represented by the blue circles and shows a clear improvement towards reproducing the exact analytical result.

Conclusion? Dry your protein up before even attempting to use GB approximation to get a good solvation energy for a large molecule!

Video recorded from "Water as a ferroelectric..." presentation at MIPT, November 5th, 2008

2008-12-16T01:29:00.000-08:00

Water as a ferroelectric: anomalous properties, long range order and interactions of nano-particles in solution (in Russian)

2008 Quantum's technology platform update and software releases

2008-11-25T03:26:00.000-08:00

It has been an exciting year here in Quantum Pharmaceuticals, another great year for our highly effective small molecule drug discovery and ADMET platform development. Our work is firmly based in basic science: QUANTUM science team developed a vector field theory of water capable of describing numerous anomalous thermodynamic and dielectric of water, as well as interactions of biomolecules in aqueous environments (arXiv:0808.0991).

The progress in our understanding of biomolecules interactions led to further accuracy improvements in our major calculation routines (IC50, solvation energy, etc.). Speed increase and sophistication of the models employed in our simulations provided better ways for false positive elimination. Direct application of our software brought up novel inhibitors of HIV integrase and gp120 proteins, human neutrophyle elastase (HNE) (see collaborations). Massive computations made using Amazon EC2 computing platform let us develop new and refind existing ADMET models (see drug absorbtion prediction (arXiv:0810.2617) as an example).

All the scientific advances are plugged in and available through the following releases of Quantum sofware (sold separately and in packages at discount prices):

q-TOX - enables researches to compute toxic effects of chemicals solely from their molecular structure (LD50, MRDD, side effects) . The robust model is based on completely new approaches. While there are numerous commercially available toxicity prediction software, none offers the depth, scope and precision comparing to q-TOX. The paradigm in the q-TOX approach is based on the premise that biological activity results from the capacity of small molecules to modulate the activity of the proteome.

q-Mol - calculates such physicochemical parameters as Solubility in H2O (g/l); Solubility in DMSO (g/l); LogP, water/octanol; Mol weight; H-bond donors; H-bond acceptors; The number of rotatable bonds;Lipinski-rule-of-5.

q-ADME - For the first time we identified proteins, binding to which correlates well with FA and T1/2. This enabled us to simulate the active component of the ADME properties that has been the heel of Achilles for existing computational approaches still. The software predicts the following properties: Drug half-life (T1/2); Fraction of oral dose absorbed (FA); Caco-2 permeability; Volume of distribution (VD); Octanol/water distribution coefficient (LogP)

q-hERG - a unique and innovative software, which allows you to predict from a molecule structures of compounds their inhibition constants (IC50) for hERG channels.

q-Albumin software takes a molecular structure and calculates HSA binding constant by docking the molecule to both of the HSA active sites (Sudlow site I and Sudlow site II).

5th of November talk@MIPT Interdisciplinary Seminar "Water as a ferroelectric: anomalous properties, long range order and interactions of nano-par....

2008-11-03T05:50:00.000-08:00

Moscow Instutite of Physics and Tehcnology, November 5th, 2008. "Water as a ferroelectric: anomalous properties, long range order and interactions of nano-particles in solution" (in russian)

The presentation will be held in room 202НК, 18:35 (read full announcement here).

Quantum Pharmaceuticals enters collaboration with Children's Cancer Institute Australia

2008-10-28T07:46:00.000-07:00

Moscow, October, 28 2008

Quantum Pharmaceuticals announce drug discovery collaboration with Children's Cancer Institute Australia's (CCIA). Under the terms of the agreement Quantum Pharmaceuticals gets access to CCIA in-house disease target data. Quantum Pharmaceuticals will contribute its technological breakthroughs and expertise in small molecule drug discovery to feed the portfolio of CCIA with new drug candidates. CCIA is to further develop the discovered inhibitors. The targets and financial terms were not disclosed.

About Quantum Pharmaceuticals

Quantum Pharmaceuticals is a drug discovery company based in Moscow, Russia specializing in small molecule screening and design through the use of its proprietary technology platform.

About CCIA

Children's Cancer Institute Australia's (CCIA) vision is to save the lives of all children with cancer and eliminate their suffering.Our mission is to be a leader in preventing cancer, to find new ways of curing cancer in children through world-class research, to ensure the best possible quality of life for these children and their families, to share the vision with others and to increase awareness, participation and funding.

Quantum Pharmaceuticals collaborates with University of Pittsburgh on HIV drug discovery.

2008-10-28T07:09:00.000-07:00

Moscow, October, 20 2008

Quantum Pharmaceuticals and University of Pittsburgh announced a drug discovery collaboration in HIV sphere. Under the terms of agreement Quantum Pharmaceuticals gets access to the target data from University of Pittsburgh. Quantum Pharmaceuticals will apply its industry leading computational technology to discover novel small molecule inhibitors for this target. The University is to provide biological expertise and further develop the discovered inhibitors. The financial terms of the deal were not disclosed.
About Quantum Pharmaceuticals
Quantum Pharmaceuticals is a drug discovery company based in Moscow, Russia specializing in small molecule screening and design through the use of its proprietary technology platform.
About University of Pittsburgh
Founded in 1787 the University of Pittsburgh has evolved into an internationally recognized center of learning and research. The University’s 12,000 employees, including 3,800 full-time faculty members, serve about 34,000 students through the programs of 15 undergraduate, graduate, and professional schools.

q-hERG: QUANTUM's innovative approach to hERG binding calculations is updated to v 2.0

2008-10-10T00:16:00.000-07:00

Quantum Pharmaceuticals, the owner of this blog, is proud to release version 2.0 of its innovativ HERG protein binding prediction software.

QUANTUM hERG (q-hERG) screening assays is a unique and innovative computational approach, which allows you to predict from a molecule structures of compounds their inhibition constants (IC50) for hERG channels.

Making a good water model: Molecules do conformationally change when cross from a gas to water solution

2008-10-08T01:25:00.000-07:00

Solvation energy calculation is absolutely crucial for a successful binding free energy (IC50) determination. Quantum Pharmaceuticals develops aqueous solvation models and tests them against available experimental data to validate the theoretical approaches.

The graph on the left represents two types of solvation energy calculations compared with experiments. The first series (small circles) are the energy differences on solvation for a set of molecules without conformational changes taken into account. The second set (large squares) is obtained after a single optimization run.

The correlation with the experiment clearly improves after conformational changes calculations. Apparently this does not only mean that the model is good, it also means that the molecules do change structure when inserted into water from the gas phase.

From Biological Spectra (multiple protein binding data) to pharmacological profiling!

2008-09-25T05:53:00.000-07:00

An ideal drug cures a decease and does not kill a patient (or even lab animals in the course of preclinical testing). Usual drug discovery paradigm is based on studying a compound's properties against a specific, normally decease-related (protein) target. The ability of a compound to bind (inhibit) a specific target is called efficacy.

Even if the efficacy is good, another important property of a compound is its toxicity. Toxicity is related to the compound physical properties, such as solubility etc, as well by its ability to bind to and hence inhibit various vital human proteins (and may be even DNA and RNA).

Common sense suggests that an ideal compound binds its specific drug related target and does not bind to anything else. Anything in between is toxic, at least to a some extent. For example, most of important properties utilize ATP molecules, which means that human body contains a lot of ATP-bindig proteins. If you make a drug attacking an ATP-binding site of a "bad" protein, most probably, a lot of "good" and useful proteins will be also affected. In that case your compound should be toxic. This is indeed the case for many cancer drugs attacking ATP-binding sites of kinases.

The latter statement is the foundation of our approach. Although it's quite conceptually simple, it's useless unless it can be supplemented by a meaningful mathematical model. Let us dwell into some more details to see how the whole thing can be made working.

Let us overview important properties of a drug candidate. First there is a bunch of physical properties, such as solubility, differential solubility, LogP (namely the difference between water and lipid solubility) etc. These quantities are easy to measure, are of direct physical meaning and can be pretty easily calculated (with or without QUANTUM software).

Another set of characteristics defines a compound ability to penetrate through cell membranes and its biochemical in liver. These are quantities deturmining bioavailability, half life, volume of distribution etc. None of such quantities can be evaluated using the simple physical properties alone. For example, drug absorbtion depends on the molecule interaction with proteins actively transporting the molecules through the cell membranes.

The bottom line: bioavailability and other quantities require understanding of a compound binding properties to a selected number of proteins participating in a compound transport and metabolism.

So the conclusion is that IF YOU KNOW WHICH PROTEINS ARE IMPORTANT, AND IF YOU CAN CALCULATE HOW YOUR COMPOUND BINDS TO THEM, YOU KNOW THE COMPOUND PHARMACOLOGICAL AND TOXICOLOGICAL PROPERTIES

Now the only problem how to identify those "important" proteins.

Fortunately, there are thousands of molecules with known properties. What we can do is the following:

- take a molecule
- calculate its binding to any human protein with known 3d structure
- use the obtained binding affinities (numbers) as a molecule's binding profile fingerprint (the Biological Spectrum), characterizing the ability of the molecule to interact with the whole human proteome

Now assume we know such Biological Spectra for 1000s molecules with well known properties. This means we can now datamine the fingerprints->known properites relations. The basic premise is, of course, that the molecules with similar fingerprints have similar properties.

We have a number of proofs of such technology working. The most recent one is the prediction of active transport drug absorption properties for drug like molecues based on the binding data against human brain hexokinase type I-related protein. We prove that the binding energy of a compound against the protein may serve to distinguish between the passively and actively transported molecules and even help to calculated the drug absorbtion quantitatevely.

From binding data to pharmacokinetics: a novel approach to active drug absorbtion prediction

2008-09-25T05:07:00.000-07:00

Oral administered drugs are mainly absorbed in the small intestine. Here, depending on drug composition and size, absorption can happen through a variety of processes . Through the epithelial cells and the lamina pro- pria the drug passes from the lumen into the blood stream in the capillaries. On its way it might be metabolised, transported away from the tract where absorption is possible or accumulate in organs other than those of treatment. Besides a fundamental interest in understanding the basic mechanisms by which a drug is assimilated by the human body, the kinetics of drug absorption is also a topic of much practical interest. A detailed knowledge of this process, resulting in the prediction of the drug absorption profile, can be of much help in the drug development stage .

To this end, several kinetic models for drug absorption within the body have been introduced (see e.g. ). They necessarily introduce some simplifications belonging to the category of the so-called three-compartment models where the substances (such as drugs or nutrients) move between three volumes (e.g. the human organs). In fact the models require two kinds of molecular properties. First are purely physical characteristics, such as solubility, differential solubility, LogP etc. These quantities are easy to measure or to calcualte, have direct physical meaning and sufficient to predict absorbtion profile of passively absorbed drugs. Actively transported molecules interact with protein transporters and therefore prediction for actively transporting compounds require a lot of separate knowledge of binding to and kinetics of the transporting proteins.

The major objective of this investigation was to develop a drug absorbtion prediction approach based on entirely different paradigm, thus avoiding difficulties of both knowledge-based and QSAR-based models, and therefore capable of better predictions. Recently it was observed that experimental values of molecular activities against a large proteins set can be used for predicting broad biological effects . In this investigation we take advantage of this concept and develop a novel quntitative method for identification of actively transported drugs. To do that we performed a docking study of a few hundreds small molecules (mostly drugs) against a diversified 510 proteins set representing human proteom. Using available absorbtion data for each of the molecules we obtained a support vector classifier capable to identify proteins which affinity for drugs correlates well with the active absorption of these drugs in 81% cases. The observation helped us improve our passive absorbtion model by adding non-liner fluxes associated with the transporting protein to obtain also a quantitative model of the passively absorbed drugs.

Ref: arXiv:0810.2617 [ps, pdf, other]

Title: From protein binding to pharmacokinetics: a novel approach to active drug absorption prediction

Authors: P.O. Fedichev, T.V. Kolesnikova, A.A. Vinnik

Comments: 9 pages, 5 eps figures

Subjects: Quantitative Methods (q-bio.QM); Biomolecules (q-bio.BM)

The nature of percolation phase transition in films of hydration water around immersed bodies.

2008-09-25T04:22:00.000-07:00

In a set of molecular dynamics calculations (MD) the percolation phase transition in water layer absorbed on a body surface was revealed at definite temperature. Below this temperature the infinite network of unbroken hydrogen bonds exists. Above it this network decays on islands. This conclusion corresponds also with measurements of conduction of moisture contained disperse materials as quartz, for example: the conductivity drops almost to zero value while heating the specimens up to definite temperature. It is known that the water conductance dominates by the “estafette” mechanism in which protons are transferred over the hydrogen bonds. The breakdown of network means the conductivity drop. These phenomena are explained in the paper in frames of early published continuous vector model of polar liquids. It is shown that the immersed bodies are surrounded by the ferroelectric film, in which the dipole moments of water molecules are ordered, arranged in one direction parallel to the interface. It is the physics behind above mentioned MD results. In addition of our previous papers the stability of this ferroelectric order is proved. The character of phase transition to the paraelectric phase is discussed and its temperature is estimated that is in agreement with MD results. Below the critical temperature the polarization vector field contains the structures as “vortex-antivortex pairs”. These pairs dissociate above this temperature that means the order breaking. The boundary conditions for the polarization vector field of molecular dipole moments are derived that is necessary to enclose the vector model equations.

Reference: accepted for publication to Journal of Structual Chemistry (Russian Journal of), 2008

Spontaneous polarization of a polar liquid next to nano-scale impurities

2008-09-25T03:42:00.001-07:00

Numerous properties of water are determined by the hydrogen bonds between its molecules. Water does not form hydrogen bonds with hydrophobic materials, henceforth, dipole moments of its molecules are arranged mainly parallel to the interfaces with such substances. According to molecular dynamics calculations (MD) at such orientation molecules save the maximal number of hydrogen bonds: three of fourth. It is shown in this Letter that in the layer of water or ice next to surface the long-range order spontaneously forms: remaining parallel to the surface dipole moment vectors arrange in one direction. Some fraction of dipole moments form the vortex structures on the surface. At low temperatures the ordered state has small admixture of vortex-antivortex pairs. The interaction energy of vortexes in this pairs arises proportional to the distance between them. A definite temperature the phase transition takes place: pairs suffer the dissociation, the molecular dipole moments order disappears. This conclusion agrees with he results of MD calculations, in which the percolation phase transition was revealed in the hydrogen bond network of water molecules absorbed on a surface.

The spontaneous polarization of liquid induced by the immersed in it nano-size bodies (proteins, peptides, …) results in the additional long-range interaction between them that depends on their relative orientation. Polarization of liquid in this case looks like that presented in Fig.1 in agreement with MD. All mentioned MD results can not be explained in frames of standard continuous scalar theory of water. These phenomena were analyzed here in frames of continuous vector model of polar liquids applications of which looks like promising to speed the simulations of macromolecular complexes.

Reference: arXiv:cond-mat/0601129 [ps, pdf, other]

Title: Long-Range Order and Interactions of Macroscopic Objects in Polar Liquids

Authors: P.O. Fedichev, L.I. Men'shikov

Comments: 11 pages, 6 figures

Subjects: Soft Condensed Matter (cond-mat.soft); Chemical Physics (physics.chem-ph); Biomolecules (q-bio.BM)

Accepted for publication in Journal of Physical Chemistry A (Russian Journal of), 2009

What's an ultimate value of reversible drug binding constant?

2008-09-25T03:12:00.000-07:00

Traditional opinion is that a good drug must have a high value of the absolute meaning of the binding energy with target protein in order to prevent the thermal dissociation of the drug-protein complex. In this case an essential deformation of protein arises, which has to be taken into account in developing different models of protein-small molecule and protein-protein interaction, and computing affinity constants in drug discovery in-silico methods. The effect of essential perturbation of protein molecule is ignored in standard computational methods of drug design that can contribute a large mistake to results of calculation, to binding energy, for example.
To demonstrate the existence of the ultimate value of the binding energy two models are considered: macroscopic and microscopic, both giving the same conclusions: the critical value of absolute meaning of binding energy is 50-100kJ/M. If the binding energy exceeds this value, then drug essentially perturbs protein configuration. In a microscopic picture this perturbation is a sequence of irreversible conformational transitions in protein body. In a macroscopic one it is an inelastic deformation of a protein substance. Our estimation agrees with the experimental value (50 kJ /M) of the ultimate energy that can be stored in a protein molecule without its destruction.
The existence of the critical value of binding energy should be accounted in structure based drug design methods where protein molecule is considered in an elastic deformation approximation.

Reference: accepted in Russian Journal of Biophysics, 2008

Quantum LogP module (part of q-Mol package) has been benchmarked by vcclab.org

2008-08-20T05:10:00.001-07:00

Quantum LogP module (part of q-Mol package) has been reviewed by R. Mannhold et al. (vcclab.org) in "Calculation of Molecular Lipophilicity: State-of-the-Art and Comparison of Log P Methods on More Than 96,000 Compounds". From the manuscript:

"Quantum LogP, developed by Quantum Pharmaceuticals, uses another quantum-chemical model to calculate the solvation energy. Like in COSMO-RS, the authors do not explicitly consider water molecules but use a continuum solvation model. However, while the COSMO-RS model simplifies solvation to interaction of molecular surfaces, the new vector-field model of polar liquids accounts for short-range (H-bond formation) and long-range dipole–dipole interactions of target and solute molecules Quantum LogP calculated log P for over 900 molecules with an RMSE of 0.7 and R2 of 0.94".

Ferro-electric phase transition in a polar liquid and the nature of lambda-transition in supercooled water

2008-08-18T00:01:00.000-07:00

Water is a major and all-important example of a strongly interacting polar liquid. Dielectric properties of water surrounding nano-scale objects pose a fundamentally important problem in physics, chemistry, structural biology and in silica drug design. The issue of temperature dependence of dielectric constant, the role of fluctuations and a possibility of a ferro-electric phase transition in a polar liquid is fairly old . It attracted a new attention when a new phase transition (so called lambda-transition) was observed in supercooled water at critical temperatures between T_{c}=228K and T_{c}=231.4K . Isothermal compressibility, density, diffusion coefficient, viscosity and static dielectric constant \epsilon and other quantities diverge as T_{c} is approached, which is signature of a second order phase transition. The singularity of \epsilon is a feature of a ferro-electric transition . However, given a complexity of interactions between water molecules, the physical picture behind this phenomenon is not entirely clear . In the phase transition is explained as a formation of a rigid network of hydrogen bonds. On the other hand a ferro-electric hypothesis was also proposed and supported by molecular-dynamics simulations (MD). For example, a ferro-electric liquid phase was observed in a model of the so called ``soft spheres'' with static dipole moments . In fact, the existence of a ferro-electric phase appears to be model independent: domains where formed both in MD calculations with hard spheres with point dipoles and with soft spheres with extended dipoles .

In the our latest publication, Ferro-electric phase transition in a polar liquid and the nature of lambda-transition in supercooled water, we develop two related approaches to calculate free energy of a polar liquid. We show that long range nature of dipole interactions between the molecules leads to para-electric state instability at sufficiently low temperatures and to a second-order phase transition. We establish the transition temperature, T_{c}, both within mean field and ring diagrams approximation and demonstrate that the ferro-electric transition is a sound physical explanation behind the experimentally observed \lambda-transition in supercooled water. Finally we discuss dielectric properties, the role of fluctuations and establish connections with earlier phenomenological models of polar liquids.

Reference: arXiv:0808.0991 [ps, pdf, other]

Title: Ferro-electric phase transition in a polar liquid and the nature of \lambda-transition in supercooled water

Authors: P.O. Fedichev, L.I. Menshikov

Comments: 4 pages, 1 eps figure

Subjects: Statistical Mechanics (cond-mat.stat-mech); Soft Condensed Matter (cond-mat.soft)

Quantum Pharmaceuticals announce collaboration with University of Colorado at Boulder

2008-06-15T08:11:00.000-07:00

Moscow, July 15 2008
Quantum Pharmaceuticals announce drug discovery collaboration with University of Colorado at Boulder. Under the terms of agreement Quantum Pharmaceuticals will apply its state-of-the-art in-house drug discovery technology to discover novel small molecule inhibitors in inflammation area. CU-Boulder is to further develop the discovered inhibitors. The targets and financial terms were not disclosed.
About Quantum Pharmaceuticals
Quantum Pharmaceuticals is a drug discovery company based in Moscow, Russia specializing in small molecule screening and design through the use of its proprietary technology platform.
About CU-Boulder
As the flagship university of the state of Colorado, CU-Boulder is a dynamic community of scholars and learners. As one of 34 U.S. public institutions belonging to the prestigious Association of American Universities (AAU) – and the only member in the Rocky Mountain region – we have a proud tradition of academic excellence, with four Nobel laureates and more than 50 members of prestigious academic academies. CU-Boulder has blossomed in size and quality since we opened our doors in 1877 – attracting superb faculty, staff, and students and building strong programs in the sciences, engineering, business, law, arts, humanities, education, music, and many other disciplines. Today, with our sights set on becoming the standard for the great comprehensive public research universities of the new century, we strive to serve the people of Colorado and to engage with the world through excellence in our teaching, research, creative work, and service.

Docking validation study: PDK1-kinase (oncology)

2008-06-06T22:02:00.001-07:00

Following the classic thrombine study, we catch up with a more important target: PDK1 kinase.

Pyruvate dehydrogenase kinase, isozyme 1, also known as PDK1, is a human gene.It codes for an isozyme of pyruvate dehydrogenase kinase (PDK).Pyruvate dehydrogenase (PDH) is a part of a mitochondrial multienzyme complex that catalyzes the oxidative decarboxylation of pyruvate and is one of the major enzymes responsible for the regulation of homeostasis of carbohydrate fuels in mammals. The enzymatic activity is regulated by a phosphorylation/dephosphorylation cycle. Phosphorylation of PDH by a specific pyruvate dehydrogenase kinase (PDK) results in inactivation.

There are no as much known inhibitors as for thrombine. BindingDB gives a few more than 70 compounds with measured binding affinities, all relatively strong binders, many of them similar to each other. We run our QUANTUM software to perform docking and the affinity calculations. The results are represented on the graph and demonstrate a solid correlation. In fact the correlation shows QUANTUM's ability to identify strong binders and distinguish between similar compounds (selectivity).

Docking validation study: classic example, thrombine

2008-06-05T03:58:00.000-07:00

The Figure on the left represents a docking study of more than 200 molecules with known activity on thrombin. The protein is a well known target ....

We have extracted the binding data from the BindingDB database and docked all the molecules onto a single (of a few available) 3D structure (2cn0 from the pdb databank).

The figure represents graphically the results of the research. The calculated and the measured activities are well correlated. Strong binders are indeed identified as strong binders (left bottom part of the graph). The accuracy of the predictions is quite good (see our discussion on the quality of the biological data here and here).

The results of the calculations can be conveniently summarized in terms of confidentiality matrix. Normally a first screen of novel compounds is performed at a certain concentration to distinguish between the active and non-active compounds. Let's take a standard, 1muM (~-35kJ/M) activity, as a separation cut-off. Then the confidence matrix has the following elements:

Experimentally active, Predicted active: 29 molecules
Experimentally n-active, Predicted active: 15 molecules (false positives)
Experimentally active, Predicted n-active: 8 molecules (false negatives)
Experimentally n-active, Predicted n-active: 156 molecules

Protein Flexibility and False Positives detection.

2008-05-26T07:58:00.000-07:00

Standard hit identification procedure with QUANTUM software implies screening of a large compound library against a given protein target. An example of such procedure for a small set of compounds with known activities is discussed in another blog entry.

Let us show first that a calculation with flexible protein gives a reasonable prediction of the binding free energy. To do that we selected a set of ~200 protein - ligand complexes from the BindingDB database. The protein-ligand pairs were selected mainly so that the complex is small and therefore the whole calculation is fast. The results are represented on the Figure. The horizontal and the vertical axis represent the calculated and the experimental value of the binding free energy calculated from the complexed positions of the ligand within the protein.

The correlation is clearly there and in a few days I will show that the calculated values demonstrate not only the accuracy, but also a good selectivity.

The other Figure represents the correlation between the results of rigid receptor fast docking procedure (horizontal axis) and the fully flexible binding free energies (vertical axis). Although the rigid protein force field has a decent correlation, it fails to recognize electrostatic clashes and thus leads to a fairly large amount of false positives among the predicted ligands. Only about 10% of all the ligands, all originally predicted in the muM range survives as binders. The trend is also clear: all the binding energy values increase (fully flexible force field gives less binders than the rigid calculation would suggest).

Quantum Pharmaceuticals and Tibotec Pharmaceuticals enter antiviral drug discovery collabortaion.

2008-05-24T02:00:00.000-07:00

Quantum Pharmaceuticals announces a drug discovery collaboration with Tibotec Pharmaceuticals (subsidiary of Johnson & Johnson).

Under the terms of the agreement Quantum Pharmaceuticals will provide Tibotec Pharmaceuticals with the family of anti-viral drug hits. The drug hits were discovered by Quantum Pharmaceuticals using its proprietary discovery technology. Tibotec Pharmaceuticals is to further evaluate and develop transferred molecules. Financial terms of the collaboration were not disclosed.

About Tibotec Pharmaceuticals BVBA
Tibotec BVBA is a global pharmaceutical and research development company. The Company's main research and development facilities are in Mechelen, Belgium with offices in Yardley, Pa. and Cork, Ireland. Tibotec is dedicated to the discovery and development of innovative HIV/AIDS and hepatitis C drugs, and anti-infectives for diseases of high unmet medical need.

About Quantum Pharmaceuticals
Quantum Pharmaceuticals is a drug discovery company based in Moscow, Russia specializing in small molecule screening and design through the use of its proprietary technology platform.

Computer aided drug design video from Quantum Pharma

2008-05-21T04:37:00.000-07:00

Molecular modelling software of Quantum Pharmaceuticals is used to dock small molecule to active site of target protein. The molecular docking on flexible protein is explored. The Quantum docking software is available for free use at LeadFinding.com, the online hit-to-lead optimization service to filter and profile chemical compounds in chemical database of ChemDiv - organic chemistry supplier.

Model of Intestinal Passive Absorption

2008-04-09T23:10:00.000-07:00

Drug penetration from intestinum into blood can be divided into two processes: the drug diffusion to apical membrane of enterocytes and the drug diffusion through the membrane. Let C₀, C₁ are drug concentrations in the intestinal lumen and in close to intestinal wall, correspondingly;

h_d and h_m – are thickness of the diffusion layer adjacent to the intestinal wall and the enterocyte’s membrane;

D_w and D_L are diffusion coefficients of the drug in the intestinal lumen (can be approximately described by the diffusion coefficient in water), and in the drug membrane. D – distribution coefficient of the drug.

The drug diffusion to apical membrane of enterocytes and through it is described by Fick’s law:

dJ₁/dt = -D_w gradC = -D_w (C₁-C₀)/h_d

dJ₂/dt = -D_L gradC = -D_L C₁D/h_m.

(here we supposed the the blood flow is high that the drug concentration in the blood is zero)

In the steady-state
dJ₁/dt = dJ₂/dt = dJ/dt,
and

C₁ = (D_w/h_D)/ (D_LD/h_m+ D_w/h_d) * C₀

Therefore apparent permeability of the drug is:

P_app = [dJ/dt] / ΔC = [dJ/dt] / C₀ = (D_w/h_d)/(1 + D_wh_m/(h_dD_LD)) (*)

The Figure represents experimental LogP_app plotted against LogD for drugs that are reported not to be subjected to active transport, active efflux, and paracellular diffusion (blue points); and model predictions (solid line). RMSD = 0.34 log units.

Quantum Pharmaceuticals and Otechestvennye Lekarstva OJSC entered drug discovery collaboration.

2008-03-25T04:45:00.000-07:00

(Moscow, 25 March 2008) Quantum Pharmaceuticals entered a collaboration with Otechestvennye Lekarstva OJSC. Under the terms of collaborative agreement Quantum Pharmaceuticals will apply its proprietary technology to explore the mechanism of action and new therapeutic uses of several compounds of Otechestvennye Lekarstva OJSC. Quantum Pharmaceutical will utilize its proprietary protein panel representative for human proteome. The financial terms of the agreement were not disclosed.

About Quantum Pharmaceuticals

Quantum Pharmaceuticals is a drug discovery company based in Moscow, Russia specializing in small molecule screening and design through the use of its proprietary technology platform.

About Otechestvennye Lekarstva OJSC
Otechestvennye Lekarstva OJSC is one of the largest Russian pharmaceutical companies with recent turnover of US$150 million. The company has 5,000 employees and markets over 200 products in Russia and in 20 other countries. Otechestvennye Lekarstva OJSC was selected as the best pharmaceutical company in Russia in 2005 and 2006 by the Russian government.

LD50 vs. MRDD: what's death for a mice is good enough for a man

2008-01-24T12:00:00.000-08:00

Prediction of toxic properties of small drug like molecules is a big challenge both from theoretical and practical points of view. Quantitatively people use different measures of toxicity such as Maximum Recommended Daily Dose (MRDD) or Lethal Dose (LD50).

Accurate prediction of such endpoints is only possible if both quantities are "physical" characteristics of a compound, rather than signatures of ever changing views of regulating agencies.

The plot on the left represents the "correlation" between experimental values of MRDD (according to FDA) and LD50 (rat) taken from different sources. As you can see, both quantities have a reasonable degree of correlation for low or intermediate toxicity levels. As soon as toxic compounds are considered, the correlation is lost and apparently no good prediction starting from physical properties of a molecule can be done.

For a moderately toxic molecule we can derive an approximate relation:
-LogMRDD = -LogLD50+2.
In "a human language": the lethal and the maximum recommended dose are roughly two orders of magnitude different; a concentration killing a mice is in fact the maximum recommended for a human being.

q-hERG: QUANTUM's innovative approach to hERG binding calculations is finally released

2008-01-18T01:39:00.000-08:00

QUANTUM hERG (q-hERG) screening assays is a unique and innovative computational approach, which allows you to predict from a molecule structures of compounds their inhibition constants (IC50) for hERG channels.

q-hEARG features:

Output is pIC50 values (-logIC50) for the molecules. The accuracy of prediction is 1.1 pIC50 units;
No training sets or QSAR methods applied;
hERG inhibition prediction is made by docking of compound on Quantum Pharmaceuticals’ Proprietary Flexible 3D structure of hERG;
Docking is based on quantum and molecular physics (see Quantum Science Core for an overview);
Average correlation has RMSD=1.18 pIC50 unit, and correlation coefficient = 0.82;
Easy to use user interface, no special hardware requirements, both Linux/Windows supported;
You can also request services based on QUANTUM hERG Screening Assays.

q-hERG is an independent software module, sharing the user interface and basic usage concepts with our q-ADME: ADME/PK properties prediction software, q-Mol: physico-chemical properties calculator, and q-Tox: toxicological profiling software. More information, including q-hERG product booklet can be obtained from the Quantum Pharmaceuticals products site.

Obtaining Q-Albumin software:

Please review your licensing options, add Q-Albumin: QUANTUM Albumin Binding Prediction Software to your shopping card and checkout to get the download links.

And continue to CHECK OUT

HERG binding prediction quality: q-HERG model vs. experiments

2008-01-11T01:59:00.000-08:00

Quantum Pharmaceuticals has recently completed development of its in-house HERG-protein binding model. Since there is no 3D structure of HERG-protein available, the calculations envolved a number of fits and model assumptions.

To see whether our data is notoverfitted, we compared the errors inour calculations with experimentaluncertanty of binding affinities for the same set of molecules. The graph on the left shows two sets of points: q-HERG model vs. experiment (red squares) and pIC50 values for the same molecules taken from different sources (green triangles, see our How good are biological experiments? HERG binding data analysis post for more details).

The two distributions are roughly of the same width, which, in a way, provides a sanity check for our HERG model.

Drug likeness: what do bioavailability and toxicity properties tell us about druglikeness?

2008-01-09T02:37:00.000-08:00

Druglikeness is a qualitative concept used in drug design for an estimate on how "druglike" a prospective compound is. Usually it is estimated from the molecular structure, often even before the substance is synthesized and tested.

A good drug should show good availability, low toxicity and high potency. The quantitative measures of such properties are bioavailability (BA, measured in %), Maximum Recomended Daily Dose (MRDD, mmol/L) and IC50 against a drug's target.

The product of toxicity and availability, MRDD*BA, gives an upper bound on target IC50 and hence is an indication of a drug quality. The Figure above represents the distribution of such product for slightly over 100 drugs. As it can be seen from the Graph, most of drug compounds have the product small, roughly below 2*10^-5mol/L. Hence, small value of MRDD*BA product may be regarded as an indication of druglikeness.

In fact the situation gets even more interesting if the same druglikeness parameter is plotted in log-scale (see the Figure on the right). Since MRDD*BA limits drug's IC50 against its target, we can deduce that most drugs are centered around pIC50 = 5 (which means that the target pIC50 should exceed 5).

LEADFINDING.COM, ONLINE HIT-TO-LEAD OPTIMIZATION SERVICE

2007-12-20T00:51:00.000-08:00

San Diego, California – December 17th, 2007 – ChemDiv Inc (San Diego) and Quantum Pharmaceuticals (Moscow) announce the launch of LEADFINDING.COM, their powerful new internet-based hit-to-lead optimization service and online chemistry store.
LEADFINDING.COM brings together, the industry leading computational chemistry software from Quantum Pharmaceuticals and ChemDiv’s world’s largest and most diverse small molecule collection for drug discovery. Fully automated web-based interface predicts binding affinities on the fly, facilitating basic hit-to-lead optimization tasks for a wide audience of researchers.
LEADFINDING.COM will be of special interest to academic and biotech researchers who have identified a small molecule hit and wish to deploy LEADFINDING.COM’s expertise in selecting candidate molecules for hit follow-up. We offer an online hit-to-lead service helping identify novel chemical lead series from ChemDiv world’s largest Discovery Collection of small molecules. LEADFINDING.COM provides unique online computational tools including hit analog searches, physiochemical properties filters, and predictions of biological activity (IC50). Availability of all selected molecules can be confirmed for online purchase and immediate delivery.
About LEADFINDING:
LEADFINDING is a joint project of ChemDiv, Inc. (ChemDiv) and Quantum Pharmaceuticals (Quantum). The project is resulting from Quantum’s effort in bringing the power of their computational models to the world life sciences community and ChemDiv’s conceptual approach to modern day discovery process. ChemDiv has been constantly contributing to the evolution of drug discovery roadmap, most recently by introducing the Chemistry Anywhere™ concept. Chemistry Anywhere™ is ChemDiv’s partnering program which by accessing a variety of online research tools shortens the time from design to wet lab results.
About ChemDiv:
ChemDiv ( www.chemdiv.com ) is a global chemistry-driven contract research organization. ChemDiv is focused on identifying and delivering pre-clinical opportunities and services to life science partners for the treatment of life-threatening diseases. Over 17 years ChemDiv provides Discovery outSourceTM solutions including medicinal and synthetic chemistry, pre-clinical development, screening libraries and global logistics. ChemDiv international research team encompasses 550 chemists and biologists in San Diego and Moscow based R&D centers.
About Quantum Pharmaceuticals:
Quantum (www.q-pharm.com) develops and commercializes industry leading computational drug discovery technologies based on applying quantum, molecular and statistical physics in molecular modeling. Our solutions help pharmaceutical companies and research facilities around the world successfully accelerate the identification and optimization of new compounds that have the potential to become drug products.

How good are biological data - II: Trombine, GSK, GPCR

2007-12-18T00:18:00.000-08:00

Many bindign affinity prediction methods, such as scores and QSAR models, rely on availability of accurate information on binding constants. The figure on the left is a result of our sdf-file parser applied to trombine (blue) and GSK (yellow) binding data from BindingDB database. The parser is written with python and uses pybel to extract unique molecules from a given multimolecular sdf.
The parser not only finds identical (in Tanimoto-similarity sense) compounds, but also prints the binding constants from the sdf records. The graph shows the correlation of the reported inverse log(binding constants) for the same molecules from different entries (sources).
The result is in fact fairly impressive (the blue points): the discrepancies-"errors" are quite large and are especially profound for good (or better say very good) binders.
The yellow points represent the result of the same script over GSK-kinase activity data. Although the total number of molecules in BindDB is much larger, almost all of them are unique. The difference between different sources is not as much as for trombine.
The Figure on the right is the visualized script output for GPCR(5-HT2B) from PDSP Ki database. The situation is roughly the same: the accuracy of a typical biological experiment reported in a literature amounts roughly to a single unit of pKd.

This and previously reported correlation for HERG ion channel should serve as an example when the results of binding affinity calculations are compared to experimental data.

EMD Serono, Inc licensed Quantum Pharmaceuticals’ drug discovery technology.

2007-12-10T03:55:00.000-08:00

Moscow, 10 December, 2007

EMD Serono, Inc entered license agreement with Quantum Pharmaceuticals to get an access to Quantum Pharmaceuticals’ small molecule hit identification computational platform and apply it in in-house research.

The Quantum Pharmaceuticals’ industry leading computational drug design technologies is based on applying quantum, molecular and statistical physics in molecular modeling and was successfully applied in different drug discovery projects. The initial term of the agreement is one year. The financial terms of the agreement were not disclosed.

Quantum Pharmaceuticals is a drug discovery company based in Moscow, Russia specializing in small molecule screening and design through the use of its proprietary technology platform.

EMD Serono, Inc. and Merck Serono S.A. are affiliates of Merck KGaA, Darmstadt, Germany, with over 16,000 employees worldwide and a strong presence on all continents.

How good are biological experiments? HERG binding data analysis

2007-12-07T03:04:00.000-08:00

A correlation between predicted and expermentally measured values of biological activity is a natural measure of a model quality. For instance, QUANTUM docking software calculates binding free energies, which are directly comparable with experimental values of -p(binding constant, Kd). Root mean squared error between the measured and the calculated quantities is the quantitative measure of the software performance.
Whatever the correlation is presented to prove the validity of a model, another important issue is the quality of the experimental data itself. The reported values for binding constants (or activities) often vary because of different measurement strategies, experimental errors or interpretation uncertanties. To visualize the situation we investigated a few datasets for HERG binding taken from QSAR World website.
The downloaded files were saved in source folder and processed with the following simple python script (thanks to openbabel):

files = os.listdir('source/')
molecules = []
for file in files:
molfile = readfile("sdf",'source/'+file)
for mol in molfile:
molfp = mol.calcfp()
present = 0
for savedmol in molecules:
savedmolfp = savedmol.calcfp()
if (molfp | savedmolfp == 1):
present = 1
print mol.data, savedmol.data
if (not present):
molecules.append(mol)

The results where analyzed in a spreadsheet program and represented on the graph above. A lot of molecules occur multiple times in the datasets. While in many of the cases the activities coinside up to 0.01 (which most probably indicates citing from a single source), the remaining values thouch correlated with each other, differ by roughly a single pKd unit.

Quantum Science Overview, v 0.1 has been released

2007-09-18T13:07:00.000-07:00

Quantum Pharmaceuticals is pleased to release Quantum Science Overview. The document conveys an overview Quantum's drug discovery solutions and scientific results. The content of the report are summarized below:
I. A Novel Integrated Approach in Drug Discovery 1
II. QUANTUM free energy vs. statistical scoring functions
III. Inside Quantum Drug Discovery Studio: Molecular Modeling Concepts and Tools
IV. QDDS and Molecular Interactions in Aqueous Environments (Solvation Model).
V. Collective (non-additive) contributions to a protein-ligand complex binding free energy.
VI. Discovery of new classes of HIV integrase inhibitors.
VII. Biological Spectra Analysis: Linking Biological Activity Profiles to Molecular Toxicity.

Appendices
A. Structure of a Basic PBPK Model applied in q-ADME
B. Binding affinity calculation of known drugs tohuman serum albumin
C. Rediscovery of Blockbuster drugs with QUANTUM

QUANTUM and albumin binding calculations: the role of protein flexibility

2007-09-03T01:52:00.000-07:00

Drug distribution within the body is determined mainly by free (unbound) concentration of drug in circulating plasma. The unbound fraction, in turn, depends on drug absorption by plasma proteins. Human Serum Albumin (HSA) is the most abundant blood plasma protein and is produced in the liver.

Binding of a compound to HSA results in an increased solubility in plasma, decreased toxicity, and /or protection against oxydation of the bound ligand. Binding can also have a significant impact on the pharmacokinetics of drugs, e.g. prolonging in vivo half life of the therapeutic agent. However too strong binding prevents drug release in tissues. That is why HSA binding information is one of the key characteristics of a compound determining its ADME properties. Successful in silico calculation of HSA binding could provide a structural basis of drug derivatives with altered HSA-binding properties.

HSA has at least two main drug binding sites characterized as Sudlow site I and Sudlow site II, which bind a number of drugs selectively. Site I, also known as the warfarin binding site, is formed by a pocket in subdomain IIA of HSA. Site II is located in subdomain IIIA and is known as the benzodiazepine binding site. Ibuprofen and diazepam are selectively bind to site II. Multiple active sites make HSA a complicated target for structure-based modeling.

The ligands with known HSA binding affinities were taken from and prepared with a set of built in QUANTUM molecular preparation and processing tools. Acenocoumarol, Acetylsalicilic_acid, Azapropazone, Benzylpenicillin, Benzylthiouracil, Bilirubin, Canrenoate, Carbamazepine, Carbenicillin, Chlorpropamide, Diphenylhydantoin, Furosemide, Indomethacin, Methyl_p-hydroxybenzoate, N-acetyl-L-tryptophan, Oxyphenbutazone, Phenobarbital, phenyl_salicylate, Phenylbutazone, Piretanide, Propyl_p-hydroxybenzoate, Quercetin, Salicylate, Sodium_benzoate, Spironolactone, Sulfadimethoxine, Sulfamethizole, Sulfathiazole, sulfisoxazole, Tenoxicam, Tolbutamide, Warfarin, Carprofen, Chlofibrate, Iopanoate, L-tryptophan were docked on to both of the sites. Three different structures – 2BXH, 2BXF and 2BXG were taken for docking. 2BXH was used for docking to the first binding site, 2BXF and 2BXG have different structures of the binding site II and we decided to use both structures for docking. Docking grid 20x20x20A were centered around a central ligand atom in appropriate binding site.

The results of the docking runs are summarized on the Figure. Both binding sites show considerable flexibility, molecular dynamics and the binding free energy calculations with flexible protein (large red points) lead to remarkable improvement of the predicted values of HSA binding affinities with respect to experiment (smaller blue points represent the results of docking on a rigid protein model). Since QUANTUM model does not have training parameters the presented correlation proves QUANTUM abilities to predict HSA binding constants of druglike compounds to both of the active sites.

Quantum Pharmaceuticals and MOLECMO Nanobiotechnologies announce collaboration.

2007-08-13T01:05:00.000-07:00

Moscow, Russia (August, 13 2007) -Quantum Pharmaceuticals announced today a collaboration with MOLECMO Nanobiotechnologies, a drug discovery company.
The collaboration is aimed at application of Quantum Pharmaceuticals' industry leading technology to anti-viral drug discovery project led by MOLECMO. The financial terms of the agreement were not disclosed.
About Quantum Pharmaceuticals.
Quantum Pharmaceuticals is a drug discovery company based in Moscow, Russia specializing in small molecule screening and design through the use of its proprietary technology platform.
About MOLECMO.
MOLECMO is an antiviral drug discovery company based in Cambridge, Massachusetts in the heart of the biotechnology circle, proximal to world class research institutes like Harvard and MIT, famed Boston Hospitals and leading Pharmaceutical firms.

QUANTUM free energy vs. statistical scoring functions

2007-05-15T05:48:00.000-07:00

QUANTUM employs quantum mechanics, thermodynamics, and an advanced continuous water model for solvation effects to calculate ligands binding affinities. This approach differs dramatically from scoring functions that are commonly used for binding affinity predictions. By including the entropy and aqueous electrostatics contributions in to the calculations directly, QUANTUM algorithms produce much more accurate and robust values of binding free energies.
Interaction of a ligand with a protein is characterized by the value of binding free energy. The free energy (F) is the thermodynamic quantity, that is directly related to experimentally measurable value of inhibition constant (IC50) and depends on electrostatic, quantum, aqueous solvation forces, as well as on statistical properties of interacting molecules. There are two major contributing quantities leading to non-additivity in F: 1) the electrostatic and solvation energy, and 2) the entropy.
Most of popular scores employ a reasonable approximation for short-range quantum interactions, but do not perform a detailed calculation of aqueous electrostatics and entropy. Both the solvation energy and the entropy are difficult to compute: so instead of exact computations, scoring function use an approximation. In this approximation, the contributions of non-additve properties are estimated as fractions of easier-to-calculate pairwise interactions: electrostatics and van der Waals forces.
Such approximation works to a some extent because vacuum electrostatics is nearly canceled by solvation energies; at the same time the enthalpy of binding is approximately compensated by entropy. Therefore, the calculations of solvation energies and entropy seemingly can be avoided by combining molecular mechanics, van der Waals and electrostatic forces linearly with usually small numerical coefficients (of the order of 10%).
However, a potential energy surface given by such linear combinations of unrelated quantities with statistics-based coefficients is not necessarily related to the true interaction profile. That is why such a simple score fails frequently to reproduce unique binding modes and hence gives docking false negatives. In the same time, this approximation tends to overestimate the affinity of weak binders producing docking false positives. Thus, in spite of reasonable accuracy of such predictions, the selectivity of scoring function is low. This means that frequently scoring functions will not allow to identify really strong binder among the pool of similar weak binders. Moreover, affinities of weak binders may be overestimated.
QUANTUM software does not rely on approximate cancellations of important physical quantities. Instead, we employ our continuous water model to compute both the vacuum and aqueous electrostatic energies, use quantum mechanics to calculate the short range forces and thermodynamic sampling to obtain the value of the free energy (entropy). As the result we can not only observe the necessary subtractions of individual energy components, but also perform molecular modeling in a more realistic and physically justified potentials. Fig. 1 shows the results of a docking run on a single rigid protein structure for 220 different ligands. R.m.s. error in free energy is 2kJ/mol, the correlation coefficient is 0.7.
The selectivity improvement in our approach is illustrated by the following model calculation. First, we derived a simple linear model directly from our QUANTUM vacuum force field. The short range interactions where variationally adjusted (to allow for empirical hydrogen bonds). The van der Waals “scaling factors” and “protein dielectric constant” were found by correlating the suggested score with experimental binding affinities for a number of known complexes. Both the linear score and complete QUANTUM force field were tested using 300 protein-ligand pairs and showed comparable accuracy.
Fig. 2 shows docking funnels (the energy difference between the conformers plotted as a function of r.m.s. from the known crystallographic position of a ligand). Both full QUANTUM free energies (red lines) and scoring function (blue lines) were used to calculate, in the case of QUANTUM, or estimate, in the case of linear score, the free energies for numerous binding modes (conformers). The conformers where generated by the QUANTUM 3.3 docking program. The resulting energies were averaged over the conformers with similar r.m.s. values and plotted on the same graph.
The model calculation shows that the QUANTUM force field has a much steeper docking funnel, i.e. is more sensitive to misplacements of a ligand, than a scoring function. Therefore QUANTUM complete force field can be used to distinguish similar binding modes and hence obtain much more accurate docking positions. As a result, QUANTUM shows dramatically lower ratio of false positives and false negatives as compared with a scoring function based method.
In fact, non-additive interactions (especially solvation effects) play a key role in molecular recognition of small molecules by proteins. QUANTUM software is practically the only accurate and highly sensitive method available to a broad audience of researchers, which is capable to realistically model the intermolecular interactions.

Docking selectivity: Additive vs. non-additive force field

2007-04-19T07:03:00.000-07:00

The free binding energy (F) of a small molecule and a protein is a non-additive complex function of individual interatomic interactions. There are two major contributing quantities leading to non-additivity in F: the electrostatic energy, and the entropy.

A common approach to molecular docking is to develop a simple (generally additive) model of intermolecular interactions and train it to reproduce experimental values of binding energy for a range of known inhibitors. The obvious advantage is the calculations speed.

Any more sophisticated approach takes requires more computational resources and may hardly lead to an immense improvement in calculations accuracy. The question thus is: do non-additive force fields have any advantages in docking situtations?

To investigate the issue the following experiment was performed:

A simple force Molecular Mechanics (MM) force field, containing a reasonable approximation for (distant-dependent media polarization) electrostatics, van-der-waals and hydrogen bonding, was developed.
Same van-der-waals and hydrogen bonds were paired with vacuum electrostatics and a (non-additive) water model to simulate solvation effects.

Both models were transformed into simple linear regressions (to avoid sophisticated thermodynamic integration) and trained to reproduce the same amount of experimental values.

Although the two models show roughly the same level of accuracy in predicting the binding energies, the selectivity is drustically different. The figure above shows the results of the energy calculations for a set of few hundreds decoys. Both graphs represent the relative binding energy (counted from the minimum position) vs. the r.m.s. deviation of the calculated ligand positions from the known native position. The lines are the energy offset values averaged over the conformers (decoys) with similar r.m.s. positions.

The conclusion?
The solvation energy is the major source of non-additivity in ligand binding. Though being one of the most complicated quantities to be acounted properly in a docking run, the solvation energy is one of the major mechanisms of molecular recognition

What makes a correct binding energy calculation?

2007-01-19T01:27:00.000-08:00

A correct calculation of a ligand binding affinity requires proper account of a few large contributions at the same time. The final value of the binding free energy is the sum of multiple sign-alternating entries: electrostatic (ES), solvation (ESolv), exchange-van der Waals (vdW) and entropic (TdS) term.

Common wisdom claims that ES and ESolv nearly compensate each other (up to a few h-bond energies), the remaining (normally large) vdW contribution is nearly canceled by TdS term. In fact, there is another important anti-correlation: strongly burried ligands with large vdW contributions are normally both entropycally and electrostatically constrained: The deeper a ligand gets into a protein (low dielectric constant medium) from water (high dielectric costant medium), the more the dessolvation energy (ES+ESolv) is, the larger the entropy losses normally are.

To clarify this issue, a simple calculation with 32 pdb structures with knows binding affinities was performed (1AU0, 1AU3, 1AU4, 1BR5, 1BR6, 1C83, 1C84, 1C85, 1C86, 1C87, 1EFY, 1EJN, 1JIJ, 1JIK, 1MS6, 1N3W, 1NAX, 1NLI, 1PWY, 1Q6K, 1RRI, 1RRW, 1RRY, 1RS2, 1TOW, 1TSM, 1URG, 1V2M, 2CBR, 2CBS, 3CBS, 4ERK). The binding affinities were taken from the PDBBind database. For every structure both the complex, the ligand and the protein was initially optimized in QUANTUM force field, then a thermodynamic integration was used to establish the free binding energies. The correlation between the calculated and measured (reported in the literature) values is shown on the left Figure 1. The free binding energies are reported in kJ/mol.

First we check the sanity of our solvation energy calculations. The following graph represents the anti-correlation between the electrostatic energy ES and the solvation contribution ESolv (both polar and non-polar, no additional dielectric constant for the protein was used since the protein is fully flexible). The two values nearly cancel each other, as it should be the case.
The only physically meaningful combination is the dessolvation punishment ESolv+ES, which is plotted against the vdW contribution (see Figure 3).

Remarkably, the dessolvation energy anti-correlates with the vdW term, which is a good measure of the contact surface. In fact, many scoring functions are pair sums of relatively short range potentials and hence correlate well with the contact surface. Non-additive desolvation energy does not correlate with the contact terms and (together with entropy losses) provides a limitation on binding energy for large and deeply burried ligands. TdS depends on the details of interactions in a smooth logarithmic way, which means that the dessolvation punishment is a leading effect.

The whole Molecular Mechanics (MM) Hamiltonian was used to generate MM trajectories for thermodynamic integration for each of the complexes. The results of the calculations are represented on the Figure 1. Root mean squared error of the calculations 6.1 kJ/mol. The dessolvation energy is a big contribution and removes (or better to say improves over) the correlation between the binding free energy and contact surface.
Conclusion 1: Binding free energy of a ligand is not a contact surface (or anything close to that approximable with a sum of pairwise short-range potential)
Conclusion 2: Desolvation energy is a major limiting factor restricting the binding energy of large ligands.

Molecular dynamics of HIV Integrase with an inhibitor

2007-01-09T01:15:00.000-08:00

Below we provide an example of Molecular Dynamics of HIV-integrase monomer with 1- (5- CHLOROINDOL-3- YL)-3 -HYDROXY-3 -(2H- TETRAZOL- 5- YL)-PROPENONE (pdb code 1qs4).

The protein structure is taken from the original 1qs4 pdb data and combined with the missing loop data from 2itg structure. The complete calculation yuilds -27kJ/mol binding energy, close to the experimentally observed value.

The inhibitor molecule is shown in red licorice. The Mg++ ion is shown as a megenta sphere.

Protein flexibilty and ligand interaction complementarity example

2006-12-10T10:19:00.000-08:00

Human neutrophil elastase complexed with an inhibitor (gw475151) studied with QUANTUM normal modes analysis tools. The video shows the inhibitor (red licorice) remains in a strongly interacting position regardless of the protein most probable motion:

Most of available docking software performs free energy scoring for different ligands positions on the same rigid protein structure. The validity of such procedure can not be easily asserted. Not only different PDB Data Bank structures of the same protein are different, NMR studies show often impressive protein flexibility and thus uncertainty in the protein atoms positions. The necessity to compensate for the lack of the structure information makes scoring functions developers utilize smooth energy scores corresponding to some kind of coarse grain approximation for the protein-ligand interactions. Such an averaging leads to inability to recover fine details of interactions and hence to lack of selectivity and false postives, i.e. hits with binding constants actually lower than predicted.

The movie above helps explain this situation. While a scoring function could suggest an accurate value for the particular ligand docked in the experimentally observed position, the scores for "hits" overlapping with the protein motion could be the same good or better, but false, since the protein position extracted from the PDB data represents only a single member of the statistical ensemble. Mathematically speaking the ligand gw475151 does not only have a good interaction energy (enthalpy), but also remains complementary to the protein pocket in spite of sufficiently large protein displacements (has a low entropy loss associated with the protein degrees of freedom).

Materials and methods:The initial information about the protein-ligand structure is taken from 1h1b PDB entry. The ligand was taken out and protein was let to relax in QUANTUM continuum water (see cond-mat/0601129). After a sufficiently long molecular dynamics simulation the protein motion has been analyzed and the lowest normal mode (highest amplitude protein motion) was separated. The ligand is then placed back at a fixed position to highlight dynamic overlap between the ligand steric interactions and the protein motion.

HIV-1 integrase drug discovery collaboration

2006-10-28T06:13:00.000-07:00

Moscow, September, 28 2006

Quantum Pharmaceuticals and A.N. Belozersky Institute of Physico-Chemical Biology entered drug discovery collaboration aimed to discovery of novel small molecule inhibitors of HIV-1 integrase. HIV-1 integrase is one of the most promising targets in HIV drug discovery. Under the terms of the agreement Quantum Pharmaceuticals will apply its indystry leading drug discovery technology platform to identify novel classess of HIV-1 integrase inhibitors. The Institute is to evaluate the discovered compounds and contribute into its further research and development.

About Quantum Pharmaceuticals
Quantum Pharmaceuticals is a drug discovery company based in Moscow, Russia specializing in small molecule screening and design through the use of its proprietary technology platform.

Quantum Pharmaceuticals and Institute of Pulmonology entered COPD drug discovery collaboration.

2006-06-18T06:03:00.000-07:00

Moscow, June, 18 2006 Quantum Pharmaceuticals and Federal State Institution "Scientific Research Institute of Pulmonology of Roszdrav" entered COPD collaboration.

Quantum Pharmaceuticals and Federal State Institution "Scientific Research Institute of Pulmonology of Roszdrav» announced drug discovery collaboration. The collaboration is aimed on the application of Quantum Pharmaceuticals’ proprietary drug discovery technology to identify novel small molecules inhibitors of Human Neutrophil Elastase.

Under the terms of agreement Institute will be responsible for biological evaluation and further development of discovered inhibitors.

About Quantum Pharmaceuticals.

Quantum Pharmaceuticals is a drug discovery company based in Moscow, Russia specializing in small molecule screening and design through the use of its proprietary technology platform.