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

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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).

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

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

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.

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

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.