How much water is in a Generalized Born protein?

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

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

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....

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

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.

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

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

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!

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

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.

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

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?

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

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

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

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)

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

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.

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.

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

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

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.

(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

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

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.

Licensing Options: One Month License $50,00 One Year License $100,00

And continue to CHECK OUT

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

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?

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).