Yes SIR! On the structure–inactivity relationships in drug discovery

•Inactivity data is helpful.•Structure-Inactivity Relationships (SIRs) are valuable in drug discovery.•Machine and deep learning benefit from SIRs.•The inactivity data gap in the literature limits the use of in silico approaches.•Authors tend to publish novel datasets over exhaustive data sets. In a...

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Bibliographic Details
Published in:Drug discovery today 2022-08, Vol.27 (8), p.2353-2362
Main Authors: López-López, Edgar, Fernández-de Gortari, Eli, Medina-Franco, José L.
Format: Article
Language:English
Subjects:
Chemoinformatics
Data mining
Negative data
Open science
QSPR
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Summary:•Inactivity data is helpful.•Structure-Inactivity Relationships (SIRs) are valuable in drug discovery.•Machine and deep learning benefit from SIRs.•The inactivity data gap in the literature limits the use of in silico approaches.•Authors tend to publish novel datasets over exhaustive data sets. In analogy with structure–activity relationships (SARs), which are at the core of medicinal chemistry, studying structure–inactivity relationships (SIRs) is essential to understanding and predicting biological activity. Current computational methods should predict or distinguish ‘activity’ and ‘inactivity’ with the same confidence because both concepts are complementary. However, the lack of inactivity data, in particular in the public domain, limits the development of predictive models and its broad application. In this review, we encourage the scientific community to disclose and analyze high-confidence activity data considering both the labeled ‘active’ and ‘inactive’ compounds.
ISSN:1359-6446
1878-5832
DOI:10.1016/j.drudis.2022.05.005