Toward structure-multiple activity relationships (SMARts) using computational approaches: A polypharmacological perspective.

Autor: López-López E; Department of Chemistry and Graduate Program in Pharmacology, Center for Research and Advanced Studies of the National Polytechnic Institute, Section 14-740, Mexico City 07000, Mexico; DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico. Electronic address: elopez.lopez@cinvestav.mx., Medina-Franco JL; DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico. Electronic address: medinajl@unam.mx.
Jazyk: angličtina
Zdroj: Drug discovery today [Drug Discov Today] 2024 Jul; Vol. 29 (7), pp. 104046. Date of Electronic Publication: 2024 May 27.
DOI: 10.1016/j.drudis.2024.104046
Abstrakt: In the current era of biological big data, which are rapidly populating the biological chemical space, in silico polypharmacology drug design approaches help to decode structure-multiple activity relationships (SMARts). Current computational methods can predict or categorize multiple properties simultaneously, which aids the generation, identification, curation, prioritization, optimization, and repurposing of molecules. Computational methods have generated opportunities and challenges in medicinal chemistry, pharmacology, food chemistry, toxicology, bioinformatics, and chemoinformatics. It is anticipated that computer-guided SMARts could contribute to the full automatization of drug design and drug repurposing campaigns, facilitating the prediction of new biological targets, side and off-target effects, and drug-drug interactions.
(Copyright © 2024 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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