DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico; Department of Chemistry and Graduate Program in Pharmacology, Center for Research and Advanced Studies of the National Polytechnic Institute, Mexico City 07000, Mexico.
Department of Nanosafety, International Iberian Nanotechnology Laboratory, Braga 4715-330, Portugal.
Drug Discov Today. 2022 Aug;27(8):2353-2362. doi: 10.1016/j.drudis.2022.05.005. Epub 2022 May 11.
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.
与药物化学核心的构效关系(SARs)类似,研究结构失活关系(SIRs)对于理解和预测生物活性至关重要。当前的计算方法应该以相同的置信度预测或区分“活性”和“失活”,因为这两个概念是互补的。然而,缺乏失活数据,特别是在公共领域,限制了预测模型的发展及其广泛应用。在这篇综述中,我们鼓励科学界在考虑标记为“活性”和“失活”的化合物的情况下,披露和分析高置信度的活性数据。