De Novo Drug Design of Targeted Chemical Libraries Based on Artificial Intelligence and Pair-Based Multiobjective Optimization.
J Chem Inf Model
; 60(10): 4582-4593, 2020 10 26.
Article
in English
| MEDLINE | ID: covidwho-889112
ABSTRACT
Artificial intelligence and multiobjective optimization represent promising solutions to bridge chemical and biological landscapes by addressing the automated de novo design of compounds as a result of a humanlike creative process. In the present study, we conceived a novel pair-based multiobjective approach implemented in an adapted SMILES generative algorithm based on recurrent neural networks for the automated de novo design of new molecules whose overall features are optimized by finding the best trade-offs among relevant physicochemical properties (MW, logP, HBA, HBD) and additional similarity-based constraints biasing specific biological targets. In this respect, we carried out the de novo design of chemical libraries targeting neuraminidase, acetylcholinesterase, and the main protease of severe acute respiratory syndrome coronavirus 2. Several quality metrics were employed to assess drug-likeness, chemical feasibility, diversity content, and validity. Molecular docking was finally carried out to better evaluate the scoring and posing of the de novo generated molecules with respect to X-ray cognate ligands of the corresponding molecular counterparts. Our results indicate that artificial intelligence and multiobjective optimization allow us to capture the latent links joining chemical and biological aspects, thus providing easy-to-use options for customizable design strategies, which are especially effective for both lead generation and lead optimization. The algorithm is freely downloadable at https//github.com/alberdom88/moo-denovo and all of the data are available as Supporting Information.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Drug Design
/
Neural Networks, Computer
/
Enzyme Inhibitors
Type of study:
Experimental Studies
/
Prognostic study
Limits:
Humans
Language:
English
Journal:
J Chem Inf Model
Journal subject:
Medical Informatics
/
Chemistry
Year:
2020
Document Type:
Article
Affiliation country:
Acs.jcim.0c00517
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