Addressing the Metabolic Stability of Antituberculars through Machine Learning.,ACS Medicinal Chemistry Letters

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Addressing the Metabolic Stability of Antituberculars through Machine Learning.
ACS Medicinal Chemistry Letters ( IF 3.5 ) Pub Date : 2017-09-14 , DOI: 10.1021/acsmedchemlett.7b00299
Thomas P Stratton ₁ , Alexander L Perryman ₁ , Catherine Vilchèze ₂ , Riccardo Russo ₃ , Shao-Gang Li ₁ , Jimmy S Patel ₁ , Eric Singleton ₃ , Sean Ekins _{4,

5} , Nancy Connell ₃ , William R Jacobs ₂ , Joel S Freundlich _{1,

3}

Affiliation

We present the first prospective application of our mouse liver microsomal (MLM) stability Bayesian model. CD117, an antitubercular thienopyrimidine tool compound that suffers from metabolic instability (MLM t1/2 < 1 min), was utilized to assess the predictive power of our new MLM stability model. The S-substituent was removed, a set of commercial reagents was utilized to construct a virtual library of 411 analogues, and our MLM stability model was applied to prioritize 13 analogues for synthesis and biological profiling. In MLM stability assays, all 13 analogues had superior metabolic stability to the parent compound, and six new analogues had acceptable MLM t1/2 values greater than or equal to 60 min. It is noteworthy that whole-cell efficacy and lack of relative mammalian cell cytotoxicity could not be predicted simultaneously. These results support the utility of our new MLM stability model in chemical tool and drug discovery optimization efforts.

中文翻译：

通过机器学习解决抗结核药物的代谢稳定性。

我们介绍了我们的小鼠肝微粒体（MLM）稳定性贝叶斯模型的第一个前瞻性应用。CD117是一种患有代谢不稳定（MLM t1 / 2 <1分钟）的抗结核性噻吩并嘧啶工具化合物，用于评估我们新的MLM稳定性模型的预测能力。除去S取代基，利用一套商业试剂构建411个类似物的虚拟文库，并使用我们的MLM稳定性模型对13个类似物进行优先排序以进行合成和生物学分析。在MLM稳定性测定中，所有13个类似物的代谢稳定性均优于母体化合物，并且六个新类似物的可接受的MLM t1 / 2值均大于或等于60分钟。值得注意的是，不能同时预测全细胞效力和相对哺乳动物细胞的细胞毒性的缺乏。

更新日期：2017-09-22

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