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

• Published in: Drug discovery today Vol. 29; no. 7; p. 104046
• Main Authors: López-López, Edgar, Medina-Franco, José L.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.07.2024
• Subjects: Animals; bioinformatics; chemoinformatics; Computational Biology - methods; Computer Simulation; computer-aided drug design; Drug Design; Drug Repositioning - methods; Humans; multiobjective; multitarget; Polypharmacology; Structure-Activity Relationship; chemoinformatics; multiobjective; computer-aided drug design; multitarget; bioinformatics
• ISSN: 1359-6446; 1878-5832; 1878-5832
• DOI: 10.1016/j.drudis.2024.104046

•Polypharmacological approaches provide a holistic overview of complex systems.•Structure–multiple activity relationships (SMARTs) are valuable in drug discovery.•Machine and deep learning benefit from SMARTs.•Multitarget algorithms allow the identification of side and off-target drug effects.•Polypharmacological approaches will guide the automation of drug design. 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.