MolTech Ltd

Moscow, Russia

MolTech Ltd

Moscow, Russia
SEARCH FILTERS
Time filter
Source Type

Novikov F.N.,MolTech Ltd | Stroylov V.S.,MolTech Ltd | Zeifman A.A.,RAS N. D. Zelinsky Institute of Organic Chemistry | Stroganov O.V.,RAS N. D. Zelinsky Institute of Organic Chemistry | And 2 more authors.
Journal of Computer-Aided Molecular Design | Year: 2012

Lead Finder is a molecular docking software. Sampling uses an original implementation of the genetic algorithm that involves a number of additional optimization procedures. Lead Finder's scoring functions employ a set of semi-empiric molecular mechanics functionals that have been parameterized independently for docking, binding energy predictions and rank-ordering for virtual screening. Sampling and scoring both utilize a staged approach, moving from fast but less accurate algorithm versions to computationally more intensive but more accurate versions. Lead Finder includes tools for the preparation of full atom protein and ligand models. In this exercise, Lead Finder achieved 72.9% docking success rate on the Astex test set when the original author-prepared full atom models were used, and 74.1% success rate when the structures were prepared by Lead Finder. The major cause of docking failures were scoring errors resulting from the use of imperfect solvation models. In many cases, docking errors could be corrected by the proper protonation and the use of correct cyclic conformations of ligands. In virtual screening experiments on the DUD test set the early enrichment factor of several tens was achieved on average. However, the area under the ROC curve ("AUC ROC") ranged from 0.70 to 0.74 depending on the screening protocol used, and the separation from the null model was not perfect-0.12-0.15 units of AUC ROC. We assume that effective virtual screening in the whole range of enrichment curve and not just at the early enrichment stages requires more accurate solvation modeling and accounting for the protein backbone flexibility. © Springer Science+Business Media B.V. 2012.


Novikov F.N.,MolTech Ltd. | Zeifman A.A.,RAS N. D. Zelinsky Institute of Organic Chemistry | Stroganov O.V.,RAS N. D. Zelinsky Institute of Organic Chemistry | Stroylov V.S.,MolTech Ltd. | And 2 more authors.
Journal of Chemical Information and Modeling | Year: 2011

The dG prediction accuracy by the Lead Finder docking software on the CSAR test set was characterized by R 2=0.62 and rmsd=1.93 kcal/mol, and the method of preparation of the full-atom structures of the test set did not significantly affect the resulting accuracy of predictions. The primary factors determining the correlation between the predicted and experimental values were the van der Waals interactions and solvation effects. Those two factors alone accounted for R 2=0.50. The other factors that affected the accuracy of predictions, listed in the order of decreasing importance, were the change of ligands internal energy upon binding with protein, the electrostatic interactions, and the hydrogen bonds. It appears that those latter factors contributed to the independence of the prediction results from the method of full-atom structure preparation. Then, we turned our attention to the other factors that could potentially improve the scoring function in order to raise the accuracy of the dG prediction. It turned out that the ligand-centric factors, including Mw, cLogP, PSA, etc. or protein-centric factors, such as the functional class of protein, did not improve the prediction accuracy. Following that, we explored if the weak molecular interactions such as X-H⋯Ar, X-H⋯Hal, CO⋯Hal, C-H⋯X, stacking and φ-cationic interactions (where X is N or O), that are generally of interest to the medicinal chemists despite their lack of proper molecular mechanical parametrization, could improve dG prediction. Our analysis revealed that out of these new interactions only CO⋯Hal is statistically significant for dG predictions using Lead FInder scoring function. Accounting for the CO⋯Hal interaction resulted in the reduction of the rmsd from 2.19 to 0.69 kcal/mol for the corresponding structures. The other weak interaction factors were not statistically significant and therefore irrelevant to the accuracy of dG prediction. On the basis of our findings from our participation in the CSAR scoring challenge we conclude that a significant increase of accuracy predictions necessitates breakthrough scoring approaches. We anticipate that the explicit accounting for water molecules, protein flexibility, and a more thermodynamically accurate method of dG calculation rather than single point energy calculation may lead to such breakthroughs. © 2011 American Chemical Society.


Zeifman A.A.,MolTech Ltd. | Titov I.Y.,RAS N. D. Zelinsky Institute of Organic Chemistry | Svitanko I.V.,RAS N. D. Zelinsky Institute of Organic Chemistry | Rakitina T.V.,RAS Research Center Kurchatov Institute | And 5 more authors.
Mendeleev Communications | Year: 2012

Molecular modeling and subsequent synthesis of novel Syk-kinase inhibitors, 7H-pyrrolo[2,3-d]pyrimidine and 1,3,5-triazine derivatives, have been carried out. The best of the obtained compounds demonstrated to inhibit Syk-kinase activity at IC 50 = 230±10 nM. © 2012 Mendeleev Communications. All rights reserved.


Stroylov V.S.,MolTech Ltd. | Rakitina T.V.,RAS Research Center Kurchatov Institute | Novikov F.N.,MolTech Ltd. | Stroganov O.V.,RAS N. D. Zelinsky Institute of Organic Chemistry | And 2 more authors.
Mendeleev Communications | Year: 2010

A set of novel fragment-like catechol derivatives were identified as EphA2 inhibitors and were further profiled against a panel of 19 tyrosine kinases. In addition to EphA2, the recovered hits were active against EGFR, FGFR1, FGFR2, Abl and PDGFR-a, and according to molecular modelling studies catechol moiety was capable of forming two or more correlated hydrogen bonds with the kinase hinge region, suggesting prospects of its further optimization as an EphA2 inhibitor. © 2010 Mendeleev Communications. All rights reserved.


Stroganov O.V.,RAS N. D. Zelinsky Institute of Organic Chemistry | Novikov F.N.,MolTech Ltd. | Zeifman A.A.,RAS N. D. Zelinsky Institute of Organic Chemistry | Stroylov V.S.,MolTech Ltd. | Chilov G.G.,RAS N. D. Zelinsky Institute of Organic Chemistry
Proteins: Structure, Function and Bioinformatics | Year: 2011

A new graph-theoretical approach called thermodynamic sampling of amino acid residues (TSAR) has been elaborated to explicitly account for the protein side chain flexibility in modeling conformation-dependent protein properties. In TSAR, a protein is viewed as a graph whose nodes correspond to structurally independent groups and whose edges connect the interacting groups. Each node has its set of states describing conformation and ionization of the group, and each edge is assigned an array of pairwise interaction potentials between the adjacent groups. By treating the obtained graph as a belief-network-a well-established mathematical abstraction-the partition function of each node is found. In the current work we used TSAR to calculate partition functions of the ionized forms of protein residues. A simplified version of a semi-empirical molecular mechanical scoring function, borrowed from our Lead Finder docking software, was used for energy calculations. The accuracy of the resulting model was validated on a set of 486 experimentally determined pK a values of protein residues. The average correlation coefficient (R) between calculated and experimental pK a values was 0.80, ranging from 0.95 (for Tyr) to 0.61 (for Lys). It appeared that the hydrogen bond interactions and the exhaustiveness of side chain sampling made the most significant contribution to the accuracy of pK a calculations. Proteins 2011; © 2011 Wiley-Liss, Inc.


A new graph-theoretical approach called thermodynamic sampling of amino acid residues (TSAR) has been elaborated to explicitly account for the protein side chain flexibility in modeling conformation-dependent protein properties. In TSAR, a protein is viewed as a graph whose nodes correspond to structurally independent groups and whose edges connect the interacting groups. Each node has its set of states describing conformation and ionization of the group, and each edge is assigned an array of pairwise interaction potentials between the adjacent groups. By treating the obtained graph as a belief-network-a well-established mathematical abstraction-the partition function of each node is found. In the current work we used TSAR to calculate partition functions of the ionized forms of protein residues. A simplified version of a semi-empirical molecular mechanical scoring function, borrowed from our Lead Finder docking software, was used for energy calculations. The accuracy of the resulting model was validated on a set of 486 experimentally determined pK(a) values of protein residues. The average correlation coefficient (R) between calculated and experimental pK(a) values was 0.80, ranging from 0.95 (for Tyr) to 0.61 (for Lys). It appeared that the hydrogen bond interactions and the exhaustiveness of side chain sampling made the most significant contribution to the accuracy of pK(a) calculations.


PubMed | MolTech Ltd
Type: Journal Article | Journal: Journal of molecular modeling | Year: 2010

In the current study an innovative method of structural filtration of docked ligand poses is introduced and applied to improve the virtual screening results. The structural filter is defined by a protein-specific set of interactions that are a) structurally conserved in available structures of a particular protein with its bound ligands, and b) that can be viewed as playing the crucial role in protein-ligand binding. The concept was evaluated on a set of 10 diverse proteins, for which the corresponding structural filters were developed and applied to the results of virtual screening obtained with the Lead Finder software. The application of structural filtration resulted in a considerable improvement of the enrichment factor ranging from several folds to hundreds folds depending on the protein target. It appeared that the structural filtration had effectively repaired the deficiencies of the scoring functions that used to overestimate decoy binding, resulting into a considerably lower false positive rate. In addition, the structural filters were also effective in dealing with some deficiencies of the protein structure models that would lead to false negative predictions otherwise. The ability of structural filtration to recover relatively small but specifically bound molecules creates promises for the application of this technology in the fragment-based drug discovery.


PubMed | MolTech Ltd
Type: Journal Article | Journal: Journal of computer-aided molecular design | Year: 2012

Lead Finder is a molecular docking software. Sampling uses an original implementation of the genetic algorithm that involves a number of additional optimization procedures. Lead Finders scoring functions employ a set of semi-empiric molecular mechanics functionals that have been parameterized independently for docking, binding energy predictions and rank-ordering for virtual screening. Sampling and scoring both utilize a staged approach, moving from fast but less accurate algorithm versions to computationally more intensive but more accurate versions. Lead Finder includes tools for the preparation of full atom protein and ligand models. In this exercise, Lead Finder achieved 72.9% docking success rate on the Astex test set when the original author-prepared full atom models were used, and 74.1% success rate when the structures were prepared by Lead Finder. The major cause of docking failures were scoring errors resulting from the use of imperfect solvation models. In many cases, docking errors could be corrected by the proper protonation and the use of correct cyclic conformations of ligands. In virtual screening experiments on the DUD test set the early enrichment factor of several tens was achieved on average. However, the area under the ROC curve (AUC ROC) ranged from 0.70 to 0.74 depending on the screening protocol used, and the separation from the null model was not perfect-0.12-0.15 units of AUC ROC. We assume that effective virtual screening in the whole range of enrichment curve and not just at the early enrichment stages requires more accurate solvation modeling and accounting for the protein backbone flexibility.

Loading MolTech Ltd collaborators
Loading MolTech Ltd collaborators