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.

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.

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 β, 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.

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.