Emerging structure-based computational methods to screen the exploding accessible chemical space.

Structure-based virtual screening can be a valuable approach to computationally select hit candidates based on their predicted interaction with a protein of interest. The recent explosion in the size of chemical libraries increases the chances of hitting high-quality compounds during virtual screening exercises but also poses new challenges as the number of chemically accessible molecules grows faster than the computing power necessary to screen them. We review here two novel approaches rapidly gaining in popularity to address this problem: machine learning-accelerated and synthon-based library screening. We summarize the results from seminal proof-of-concept studies, highlight the latest developments, and discuss limitations and future directions.

Novel Histone Deacetylase Inhibitors and HIV-1 Latency-Reversing Agents Identified by Large-Scale Virtual Screening.

Current antiretroviral therapies used for HIV management do not target latent viral reservoirs in humans. The experimental "shock-and-kill" therapeutic approach involves use of latency-reversal agents (LRAs) that reactivate HIV expression in reservoir-containing cells, followed by infected cell elimination through viral or host immune cytopathic effects. Several LRAs that function as histone deacetylase (HDAC) inhibitors are reported to reverse HIV latency in cells and in clinical trials; however, none to date have consistently reduced viral reservoirs in humans, prompting a need to identify new LRAs. Toward this goal, we describe here a virtual screening (VS) approach which uses 14 reported HDAC inhibitors to probe PubChem and identifies 60 LRA candidates. We then show that four screening "hits" including (S)-N-Hydroxy-4-(3-methyl-2-phenylbutanamido)benzamide (compound 15), N-(4-Aminophenyl)heptanamide (16), N-[4-(Heptanoylamino)phenyl]heptanamide (17), and 4-(1,3-Dioxo-1H-benzo[de]isoquinolin-2(3H)-yl)-N-(2-hydroxyethyl)butanamide (18) inhibit HDAC activity and/or reverse HIV latency in vitro. This study demonstrates and supports that VS-based approaches can readily identify novel HDAC inhibitors and LRAs, which in turn may help toward inhibitor design and chemical optimization efforts for improved HIV shock-and-kill-based efforts.

Pharmacophore modeling and in silico toxicity assessment of potential anticancer agents from African medicinal plants.

Molecular modeling has been employed in the search for lead compounds of chemotherapy to fight cancer. In this study, pharmacophore models have been generated and validated for use in virtual screening protocols for eight known anticancer drug targets, including tyrosine kinase, protein kinase B ß, cyclin-dependent kinase, protein farnesyltransferase, human protein kinase, glycogen synthase kinase, and indoleamine 2,3-dioxygenase 1. Pharmacophore models were validated through receiver operating characteristic and Güner-Henry scoring methods, indicating that several of the models generated could be useful for the identification of potential anticancer agents from natural product databases. The validated pharmacophore models were used as three-dimensional search queries for virtual screening of the newly developed AfroCancer database (~400 compounds from African medicinal plants), along with the Naturally Occurring Plant-based Anticancer Compound-Activity-Target dataset (comprising ~1,500 published naturally occurring plant-based compounds from around the world). Additionally, an in silico assessment of toxicity of the two datasets was carried out by the use of 88 toxicity end points predicted by the Lhasa's expert knowledge-based system (Derek), showing that only an insignificant proportion of the promising anticancer agents would be likely showing high toxicity profiles. A diversity study of the two datasets, carried out using the analysis of principal components from the most important physicochemical properties often used to access drug-likeness of compound datasets, showed that the two datasets do not occupy the same chemical space.

Molecular modeling of potential anticancer agents from African medicinal plants.

Naturally occurring anticancer compounds represent about half of the chemotherapeutic drugs which have been put in the market against cancer until date. Computer-based or in silico virtual screening methods are often used in lead/hit discovery protocols. In this study, the "drug-likeness" of ~400 compounds from African medicinal plants that have shown in vitro and/or in vivo anticancer, cytotoxic, and antiproliferative activities has been explored. To verify potential binding to anticancer drug targets, the interactions between the compounds and 14 selected targets have been analyzed by in silico modeling. Docking and binding affinity calculations were carried out, in comparison with known anticancer agents comprising ~1,500 published naturally occurring plant-based compounds from around the world. The results reveal that African medicinal plants could represent a good starting point for the discovery of anticancer drugs. The small data set generated (named AfroCancer) has been made available for research groups working on virtual screening.