Map PAINS substructures according to PAINS class and find the highest PAINS violation

Many scientists are unaware that PAINS substructures are classified by hit frequency; this has led to a tendency to irresponsibly use PAINS filters during compound triage by filtering out potential leads based on the presence of a PAINS substructure instead of the presence of a type of PAINS substructure. Baell suggested in his review, Seven Year Itch: Pan-Assay Interference Compounds (PAINS) in 2017 - Utility and Limitations, that scientists should focus on class A, maybe class B, and not class C. Because the original paper only reported hit frequency based on a singular assay technology, compounds should not be necessarily excluded for containing a class A substructure. This protocol splits the different classes of PAINS into distinct substructure mapping subprotocols to produce a more informed PAINS report. The SMARTS patterns can be obtained here. This protocol is being made available in conjunction with an upcoming publication.许多科研人员并不了解，泛assay干扰化合物（Pan-Assay Interference Compounds，PAINS）子结构是按照命中频率进行分类的；这一认知缺失导致在化合物初筛过程中出现了滥用PAINS过滤器的现象：研究者往往仅根据化合物含有PAINS子结构就将其筛除，而非依据其含有的PAINS子结构类别进行甄别。Baell在其2017年的综述《七年之痒：泛assay干扰化合物（PAINS）的效用与局限》（Seven Year Itch: Pan-Assay Interference Compounds (PAINS) in 2017 - Utility and Limitations）中提出，科研人员应重点关注A类（乃至B类）PAINS子结构，而非C类。由于原始文献仅基于单一检测技术报道了命中频率数据，因此不应仅因化合物含有A类PAINS子结构就直接将其排除。 本协议将不同类别的PAINS子结构拆解为独立的子结构映射子流程，以生成信息更为全面的PAINS分析报告。 可在此处获取SMARTS模式。 本协议将与即将发表的学术论文同步发布。