The design of compounds with desirable properties - The anti-HIV case study.
J Comput Chem
; 44(10): 1016-1030, 2023 04 15.
Article
in English
| MEDLINE | ID: covidwho-2274450
ABSTRACT
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.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Artificial Intelligence
/
COVID-19
Type of study:
Case report
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
J Comput Chem
Journal subject:
Chemistry
Year:
2023
Document Type:
Article
Affiliation country:
Jcc.27061
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