A Web-based, graphical user interface has been developed to conduct rapid searches by numerous criteria in the more than 250,000 structures of the Open NCI Database. It is based on the chemistry information toolkit CACTVS. Nearly all structures and anticancer and anti-HIV screening data provided by NCI's Developmental Therapeutics Program have been included. This data set has been augmented by a large amount of additional, mostly computed, data, such as calculated log P values, predicted biological activities, systematically determined names, and others. Complex boolean searches are possible. Flexible substructure searches have been implemented. The user can conduct 3D pharmacophore queries in up to 25 conformations precalculated for each compound. Numerous output formats as well as 2D and 3D visualization options are provided. It is possible to export search results in various forms and with choices for data contents in the exported files, for structure sets ranging in size from a single compound to the entire database. Only a Web browser is needed to use this service, with a few plug-ins being useful but optional.

Schistosomiasis is a prevalent and chronic helmintic disease in tropical regions. Treatment and control relies on chemotherapy with just one drug, praziquantel and this reliance is of concern should clinically relevant drug resistance emerge and spread. Therefore, to identify potential target proteins for new avenues of drug discovery we have taken a comparative chemogenomics approach utilizing the putative proteome of Schistosoma mansoni compared to the proteomes of two model organisms, the nematode, Caenorhabditis elegans and the fruitfly, Drosophila melanogaster. Using the genome comparison software Genlight, two separate in silico workflows were implemented to derive a set of parasite proteins for which gene disruption of the orthologs in both the model organisms yielded deleterious phenotypes (e.g., lethal, impairment of motility), i.e., are essential genes/proteins. Of the 67 and 68 sequences generated for each workflow, 63 were identical in both sets, leading to a final set of 72 parasite proteins. All but one of these were expressed in the relevant developmental stages of the parasite infecting humans. Subsequent in depth manual curation of the combined workflow output revealed 57 candidate proteins. Scrutiny of these for ‘druggable’ protein homologs in the literature identified 35 S. mansoni sequences, 18 of which were homologous to proteins with 3D structures including co-crystallized ligands that will allow further structure-based drug design studies. The comparative chemogenomics strategy presented generates a tractable set of S. mansoni proteins for experimental validation as drug targets against this insidious human pathogen.

In pharmaceutical industry, lead discovery strategies and screening collections have been predominantly tailored to discover compounds that modulate target proteins through noncovalent interactions. Conversely, covalent linkage formation is an important mechanism for a quantity of successful drugs in the market, which are discovered in most cases by hindsight instead of systematical design. In this article, the implementation of a docking-based virtual screening workflow for the retrieval of covalent binders is presented considering human cathepsin K as a test case. By use of the docking conditions that led to the best enrichment of known actives, 44 candidate compounds with unknown activity on cathepsin K were finally selected for experimental evaluation. The most potent inhibitor, 4-(N-phenylanilino)-6-pyrrolidin-1-yl-1,3,5-triazine-2-carbonitrile (CP243522), showed a K(i) of 21 nM and was confirmed to have a covalent reversible mechanism of inhibition. The presented approach will have great potential in cases where covalent inhibition is the desired drug discovery strategy.

In the field of in silico screening, many applications do not automatically consider possible tautomeric states of molecules. However, the detection of new compound candidates might rely on correct structural description, which is important for the perfect fit toward the biologically relevant interactions. In this paper, we present a new exhaustive tautomer enumeration approach implemented by means of the CACTVS software package. The approach contains a set of 21 predefined SMIRKS-based transforms and a powerful transformation engine that is capable of generating most tautomers described comprehensively in the literature or found in databases in the field of medicinal chemistry. User-defined tautomer rules applied to specific structural databases or scientific issues can be implemented easily and used instead of the predefined rules. In addition, we describe the impact of tautomer-enriched databases on pharmacophore screening approaches for human matrix metalloproteinase 8 as an example of a protein-based pharmacophore screening scenario and for human cyclin-dependent kinases as an example of a ligand-based pharmacophore screening approach. In both test cases, as a preprocessing step, we have used our new tautomer enumerator tool for the tautomer enrichment of the screening data sets and have used it as a postprocessing step to remove tautomeric duplicates from the results. We could demonstrate that the tautomer-enriched screening data sets show significant advantages compared to their non-enhanced counterparts. The discrimination between hits and nonhits was significantly better in the case of tautomer-enriched databases. Moreover, it has been proved that tautomer-enhanced databases will lead to a higher number of potential hits.

With the goal to identify novel trypanothione reductase (TR) inhibitors, we performed a combination of in vitro and in silico screening approaches. Starting from a highly diverse compound set of 2,816 compounds, 21 novel TR inhibiting compounds could be identified in the initial in vitro screening campaign against T. cruzi TR. All 21 in vitro hits were used in a subsequent similarity search-based in silico screening on a database containing 200,000 physically available compounds. The similarity search resulted in a data set containing 1,204 potential TR inhibitors, which was subjected to a second in vitro screening campaign leading to 61 additional active compounds. This corresponds to an approximately 10-fold enrichment compared to the initial pure in vitro screening. In total, 82 novel TR inhibitors with activities down to the nM range could be identified proving the validity of our combined in vitro/in silico approach. Moreover, the four most active compounds, showing IC50 values of <1 μM, were selected for determining the inhibitor constant. In first on parasites assays, three compounds inhibited the proliferation of bloodstream T. brucei cell line 449 with EC50 values down to 2 μM.

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The efflux transporter P-glycoprotein (P-gp) is responsible for the extrusion of a wide variety of molecules, including drug molecules, from the cell. Therefore, P-gp-mediated efflux transport limits the bioavailability of drugs. To identify potential P-gp substrates early in the drug discovery process, in silico models have been developed based on structural and physicochemical descriptors. In this study, we investigate the use of molecular dynamics fingerprints (MDFPs) as an orthogonal descriptor for the training of machine learning (ML) models to classify small molecules into substrates and nonsubstrates of P-gp. MDFPs encode the information from short MD simulations of the molecules in different environments (water, membrane, or protein pocket). The performance of the MDFPs, evaluated on both an in-house dataset (3930 compounds) and a public dataset from ChEMBL (1114 compounds), is compared to that of commonly used 2D molecular descriptors, including structure-based and property-based descriptors. We find that all tested classifiers interpolate well, achieving high accuracy on chemically diverse subsets. However, by challenging the models with external validation and prospective analysis, we show that only tree-based ML models trained on MDFPs or property-based descriptors generalize well to regions of the chemical space not covered by the training set.

Late‐stage functionalization of lead compounds is of high interest in drug discovery since it offers an easy access to metabolites and derivatives of a lead compound without the need to redesign an often long multistep synthesis. Owing to their high degree of chemoselectivity, biocatalytic transformations, enzymatic oxidations in particular, are potentially very powerful because they could allow the synthesis of less lipophilic derivatives of a lead compound. In the majority of cases, enzymatic oxidations have been used in an empirical way as their regioselectivity is difficult to predict. In this publication, the concept of using docking/protecting groups in a biomimetic fashion was investigated, which could help steer the regioselectivity of a P450BM3‐mediated oxidation. A novel set of docking/protecting groups was designed that can be cleaved under very mild conditions and address the often problematic aqueous solubility of the substrates. Vabicaserin was used as tool compound containing typical groups such as basic, aliphatic, and aromatic moieties. The results were rationalized with the help of in silico docking and molecular dynamic studies.