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PubMed2002

Development of a virtual screening method for identification of "frequent hitters" in compound libraries.

A computer-based method was developed for rapid and automatic identification of potential "frequent hitters". These compounds show up as hits in many different biological assays covering a wide range of targets. A scoring scheme was elaborated from substructure analysis, multivariate linear and nonlinear statistical methods applied to several sets of one and two-dimensional molecular descriptors. The final model is based on a three-layered neural network, yielding a predictive Matthews correlation coefficient of 0.81. This system was able to correctly classify 90% of the test set molecules in a 10-times cross-validation study. The method was applied to database filtering, yielding between 8% (compilation of trade drugs) and 35% (Available Chemicals Directory) potential frequent hitters. This filter will be a valuable tool for the prioritization of compounds from large databases, for compound purchase and biological testing, and for building new virtual libraries.

Databases, Factual↗
PubMed2004

Recovering the true targets of specific ligands by virtual screening of the protein data bank.

The Protein Data Bank (PDB) has been processed to extract a screening protein library (sc-PDB) of 2148 entries. A knowledge-based detection algorithm has been applied to 18,000 PDB files to find regular expressions corresponding to either protein, ions, co-factors, solvent, or ligand atoms. The sc-PDB database comprises high-resolution X-ray structures of proteins for which (i) a well-defined active site exists, (ii) the bound-ligand is a small molecular weight molecule. The database has been screened by an inverse docking tool derived from the GOLD program to recover the known target of four unrelated ligands. Both the database and the inverse screening procedures are accurate enough to rank the true target of the four investigated ligands among the top 1% scorers, with 70-100 fold enrichment with respect to random screening. Applying the proposed screening procedure to a small-sized generic ligand was much less accurate suggesting that inverse screening shall be reserved to rather selective compounds.

Algorithms↗
PubMed2005

Virtual screening to enrich a compound collection with CDK2 inhibitors using docking, scoring, and composite scoring models.

Docking programs can generate subsets of a compound collection with an increased percentage of actives against a target (enrichment) by predicting their binding mode (pose) and affinity (score), and retrieving those with the highest scores. Using the QXP and GOLD programs, we compared the ability of six single scoring functions (PLP, Ligscore, Ludi, Jain, ChemScore, PMF) and four composite scoring models (Mean Rank: MR, Rank-by-Vote: Vt, Bayesian Statistics: BS and PLS Discriminant Analysis: DA) to separate compounds that are active against CDK2 from inactives. We determined the enrichment for the entire set of actives (IC50 < 10 microM) and for three activity subsets. In all cases, the enrichment for each subset was lower than for the entire set of actives. QXP outperformed GOLD at pose prediction, but yielded only moderately better enrichments. Five to six scoring functions yielded good enrichments with GOLD poses, while typically only two worked well with QXP poses. For each program, two scoring functions generally performed better than the others (Ligscore2 and Ludi for GOLD; QXP and Jain for QXP). Composite scoring functions yielded better results than single scoring functions. The consensus approaches MR and Vt worked best when separating micromolar inhibitors from inactives. The statistical approaches BS and DA, which require training data, performed best when distinguishing between low and high nanomolar inhibitors. The key observation that all hit rate profiles for all four activity intervals for all scoring schemes for both programs are significantly better than random, is evidence that docking can be successfully applied to enrich compound collections.

Adenosine Triphosphate↗
PubMed2006

Virtual screening for finding natural inhibitor against cathepsin-L for SARS therapy.

Recently Simmons et al. reported a new mechanism for SARS virus entry into target cells, where MDL28170 was identified as an efficient inhibitor of CTSL-meditated substrate cleavage with IC(50) of 2.5 nmol/l. Based on the molecule fingerprint searching method, 11 natural molecules were found in the Traditional Chinese Medicines Database (TCMD). Molecular simulation indicates that the MOL376 (a compound derived from a Chinese medicine herb with the therapeutic efficacy on the human body such as relieving cough, removing the phlegm, and relieving asthma) has not only the highest binding energy with the receptor but also the good match in geometric conformation. It was observed through docking studies that the van der Waals interactions made substantial contributions to the affinity, and that the receptor active pocket was too large for MDL21870 but more suitable for MOL736. Accordingly, MOL736 might possibly become a promising lead compound for CTSL inhibition for SARS therapy.

Algorithms↗
PubMed2002

Markovian chemicals "in silico" design (MARCH-INSIDE), a promising approach for computer aided molecular design II: experimental and theoretical assessment of a novel method for virtual screening of fasciolicides.

A novel method for in silico selection of fluckicidal drugs is introduced. Two QSARs that permit us to discriminate between fasciolicide and non-fasciolicide drugs (the first) and to outline some conclusions about the possible mechanism of action of a chemical (the second) are performed. The first model correctly classified 93.85% of compounds in the training series and 89.5% of the compounds in the predicting one. This model correctly classified 87.7, 93.8, 92.2 and 93.9% of compounds in leave- n-out cross validation procedures when n takes values from 2 to until 6. The model seems to be stable in around 92% of good classification in leave- n-out cross validation analysis when n>6. The second model correctly classified 70% of non-fasciolicide compounds, 85.71% of beta-tubulin inhibitors and 100% of proton ionophores in the training set. This model recognizes as proton ionophores 100% of any nitrosalicylanilides in the predicting series. Both models have a low p-level <0.05. Finally, the experimental assay of six organic chemicals by an in vivo test permit us to carry out an assessment of the model with a fairly good 100% agreement between experiment and theoretical prediction.

Animals↗
PubMed2005

Enhancing the accuracy of virtual screening: molecular dynamics with quantum-refined force fields.

A methodology aimed at improving the accuracy of current docking-scoring procedures is proposed, and validated through detailed tests of its performance in predicting the activity of HIV-1 protease inhibitors. This methodology is based on molecular dynamics simulations using a force field whose effective charges are refined by means of a novel procedure that relies on quantum-mechanical calculations and preserves the internal consistency of the parameterization scheme.

Drug Design↗
PubMed2005

Surrogate docking: structure-based virtual screening at high throughput speed.

Structure-based screening using fully flexible docking is still too slow for large molecular libraries. High quality docking of a million molecule library can take days even on a cluster with hundreds of CPUs. This performance issue prohibits the use of fully flexible docking in the design of large combinatorial libraries. We have developed a fast structure-based screening method, which utilizes docking of a limited number of compounds to build a 2D QSAR model used to rapidly score the rest of the database. We compare here a model based on radial basis functions and a Bayesian categorization model. The number of compounds that need to be actually docked depends on the number of docking hits found. In our case studies reasonable quality models are built after docking of the number of molecules containing approximately 50 docking hits. The rest of the library is screened by the QSAR model. Optionally a fraction of the QSAR-prioritized library can be docked in order to find the true docking hits. The quality of the model only depends on the training set size - not on the size of the library to be screened. Therefore, for larger libraries the method yields higher gain in speed no change in performance. Prioritizing a large library with these models provides a significant enrichment with docking hits: it attains the values of approximately 13 and approximately 35 at the beginning of the score-sorted libraries in our two case studies: screening of the NCI collection and a combinatorial libraries on CDK2 kinase structure. With such enrichments, only a fraction of the database must actually be docked to find many of the true hits. The throughput of the method allows its use in screening of large compound collections and in the design of large combinatorial libraries. The strategy proposed has an important effect on efficiency but does not affect retrieval of actives, the latter being determined by the quality of the docking method itself.

Bayes Theorem↗
PubMed2006

Investigation of substituent effect of 1-(3,3-diphenylpropyl)-piperidinyl phenylacetamides on CCR5 binding affinity using QSAR and virtual screening techniques.

A linear quantitative-structure activity relationship model is developed in this work using Multiple Linear Regression Analysis as applied to a series of 51 1-(3,3-diphenylpropyl)-piperidinyl phenylacetamides derivatives with CCR5 binding affinity. For the selection of the best variables the Elimination Selection-Stepwise Regression Method (ES-SWR) is utilized. The predictive ability of the model is evaluated against a set of 13 compounds. Based on the produced QSAR model and an analysis on the domain of its applicability, the effects of various structural modifications on biological activity are investigated. The study leads to a number of guanidine derivatives with significantly improved predicted activities.

Algorithms↗
PubMed2006

Identification of some novel AHAS inhibitors via molecular docking and virtual screening approach.

Acetohydroxyacid synthase (AHAS; EC 2.2.1.6) catalyzes the first common step in branched-chain amino acid biosynthesis. This enzyme is an important target for the design of environmental-benign herbicides. Based on the crystal structure of AHAS/sulfonylurea complex, we have carried out computational screening of the ACD-3D database in order to look for novel non-sulfonylurea inhibitors of AHAS for the first time. Three novel compounds were found to inhibit plant AHAS in vitro among 14 procured compounds. One compound showed promising activity in vivo for rape root growth inhibition bioassay. This research provided useful clues for further design and discovery of AHAS inhibitors.

Acetolactate Synthase↗
PubMed2003

Identification of novel inhibitors of BCR-ABL tyrosine kinase via virtual screening.

Inhibition of BCR-ABL tyrosine kinase activity has shown to be essential for the treatment of chronic myelogenous leukemia (CML). However, drug resistance has quickly arisen in recent clinical trials for STI571 (Gleevec), which is the first approved drug of CML by inhibiting ABL tyrosine kinase. It is desirable to develop new types of ABL tyrosine kinase inhibitors that may overcome this drug resistance problem. Here we present the discovery of novel inhibitors targeted at the catalytic domain of ABL tyrosine kinase by using three-dimensional database searching techniques. From a database containing 200,000 commercially available compounds, the top 1000 compounds with the best DOCK energy score were selected and subjected to structural diversity and drug likeness analysis, 15 compounds were submitted for biological assay. Eight out of the 15 showed inhibitory activity against K562 cells with IC(50) value ranging from 10 to 200 microM. Two promising compounds showed inhibition in further ABL tyrosine phosphorylation assay. It is anticipated that those two compounds can serve as lead compounds for further drug design and optimization.

Binding Sites↗
PubMed2004

Getting it right: modeling of pH, solvent and "nearly" everything else in virtual screening of biological targets.

"Getting it right" refers to the careful modeling of all elements in the living system, i.e. biological macromolecules, ligands and water molecules. In addition, careful attention should be paid to the protonation state of ionizable functional groups on the ligands and residues at the active site. Computational technology based on the empirical HINT program is described to: (1) calculate free energy scores for ligand binding; (2) include the implicit and explicit effects of water in and around the ligand binding site; and (3) incorporate the effects of global and local pH in molecular models. This last point argues for the simultaneous consideration of a number of molecular models, each with different protonation profiles. Data from recent studies of protein-ligand systems (trypsin, thrombin, neuraminidase, HIV-1 protease and others) are used to illustrate the concepts in the paper. Also discussed are experimental factors related to accurate free energy predictions with this and other computational technologies.

Benzylamines↗
PubMed2006

Pharmacophore identification and virtual screening for methionyl-tRNA synthetase inhibitors.

Aminoacyl-tRNA synthetases (aaRSs) are essential enzymes involved in protein biosynthesis in all living organisms and are an unexploited antibacterial targets, as many strains of bacteria have become resistant to all established classes of antibiotics. Therefore, the main aim of this study is to discover new lead molecules which would be useful as anti-bacterial compounds. Pharmacophore models were developed by using CATALYST HypoGen with a training set of 29 diverse methionyl-tRNA synthetase (MetRS) inhibitors. The best quantitative pharmacophore hypothesis (Hypo1) obtained a correlation coefficient of 0.975, root mean square deviation (RMSD) of 0.55 and cost difference (null cost-total cost) of 70.32. This Hypo1 was validated by two methods, first by using 104 test set molecules which resulted a correlation of 0.926 between HypoGen estimated activities versus experimental activities and secondly by Cat-Scramble validation method. This validated pharmacophore model was further used for screening databases for discovery of new MetRS inhibitors. The new lead compounds were further analyzed for drug-like properties. Homology modeled structure of Staphylococcus aureus MetRS was built and molecular docking studies were performed with many inhibitors using the newly built protein structure. Finally, it was found that the new leads exhibited good estimated inhibitory activity, calculated binding properties similar to experimentally proven compounds and also favorable drug-like properties.

Amino Acid Sequence↗
PubMed2006

Development and virtual screening of target libraries.

The concomitant development of in silico screening technologies and of three-dimensional information on therapeutically relevant macromolecular targets makes it possible to navigate in the structural proteome and to identify targets fulfilling user-defined queries. This review illustrates some in-house recent advances in the development of target libraries and how they can be browsed to unravel chemogenomic information.

Animals↗
PubMed2004

Protein crystallization: virtual screening and optimization.

Advances in genomics have yielded entire genetic sequences for a variety of prokaryotic and eukaryotic organisms. This accumulating information has escalated the demands for three-dimensional protein structure determinations. As a result, high-throughput structural genomics has become a major international research focus. This effort has already led to several significant improvements in X-ray crystallographic and nuclear magnetic resonance methodologies. Crystallography is currently the major contributor to three-dimensional protein structure information. However, the production of soluble, purified protein and diffraction-quality crystals are clearly the major roadblocks preventing the realization of high-throughput structure determination. This paper discusses a novel approach that may improve the efficiency and success rate for protein crystallization. An automated nanodispensing system is used to rapidly prepare crystallization conditions using minimal sample. Proteins are subjected to an incomplete factorial screen (balanced parameter screen), thereby efficiently searching the entire "crystallization space" for suitable conditions. The screen conditions and scored experimental results are subsequently analyzed using a neural network algorithm to predict new conditions likely to yield improved crystals. Results based on a small number of proteins suggest that the combination of a balanced incomplete factorial screen and neural network analysis may provide an efficient method for producing diffraction-quality protein crystals.

Combinatorial Chemistry Techniques↗
PubMed2000

Virtual screening of intestinal drug permeability.

Lead compounds generated in high throughput drug discovery programmes often have unfavorable biopharmaceutical properties, resulting in a low success rate of such drug candidates in clinical development. Drug companies and researchers would thus like to have methods of predicting biopharmaceutical properties accurately. The intestinal permeability to a lead compound is one such property which is particularly important. Therefore, access to methods to accurately predict biopharmaceutical properties, such as the intestinal permeability of a large series of compounds, is of particular importance. This review deals with new theoretical methods used to predict intestinal drug permeability. There are several possible transport routes across the intestine, but theoretical methods generally deal with only one of them, the passive transcellular route. Therefore, this review will also discuss the relative importance of passive and active drug transport and efflux routes using recent data generated in cell cultures, animal models and human subjects.

Animals↗