The design of compounds with desirable properties - The anti-HIV case study.

Efficacy and safety are among the most desirable characteristics of an ideal drug. The tremendous increase in computing power and the entry of artificial intelligence into the field of computational drug design are accelerating the process of identifying, developing, and optimizing potential drugs. Here, we present novel approach to design new molecules with desired properties. We combined various neural networks and linear regression algorithms to build models for cytotoxicity and anti-HIV activity based on Continual Molecular Interior analysis (CoMIn) and Cinderella's Shoe (CiS) derived molecular descriptors. After validating the reliability of the models, a genetic algorithm was coupled with the Des-Pot Grid algorithm to generate new molecules from a predefined pool of molecular fragments and predict their bioactivity and cytotoxicity. This combination led to the proposal of 16 hit molecules with high anti-HIV activity and low cytotoxicity. The anti-SARS-CoV-2 activity of the hits was predicted.

Can natural products stop the SARS-CoV-2 virus? A docking and molecular dynamics study of a natural product database.

Background: The SARS-CoV-2 3CLpro is one of the primary targets for designing new and repurposing known drugs. Methodology: A virtual screening of molecules from the Natural Product Atlas was performed, followed by molecular dynamics simulations of the most potent inhibitor bound to two conformations of the protease and into two binding sites. Conclusion: Eight molecules with appropriate ADMET properties are suggested as potential inhibitors. The greatest benefit of this study is the demonstration that these ligands can bind in the catalytic site but also to the groove between domains II and III, where they interact with a series of residues which have an important role in the dimerization and the maturation process of the enzyme.