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.

Automated summary

A novel approach combining neural networks and linear regression algorithms was used to design new molecules with desired properties. Molecular descriptors based on Continual Molecular Interior analysis (CoMIn) and Cinderella's Shoe (CiS) were used to build models for cytotoxicity and anti-HIV activity. A genetic algorithm coupled with the Des-Pot Grid algorithm was used to generate new molecules from a predefined pool and predict their bioactivity and cytotoxicity. 16 hit molecules with high anti-HIV activity and low cytotoxicity were proposed, and the anti-SARS-CoV-2 activity of the hits was predicted.