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- W4280496215 endingPage "2362" @default.
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- W4280496215 abstract "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." @default.
- W4280496215 created "2022-05-22" @default.
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- W4280496215 date "2022-08-01" @default.
- W4280496215 modified "2023-10-03" @default.
- W4280496215 title "Yes SIR! On the structure–inactivity relationships in drug discovery" @default.
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- W4280496215 doi "https://doi.org/10.1016/j.drudis.2022.05.005" @default.
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