Khan Afreen A, Poojary Sannidhi S, Bhave Ketki K, Nandan Santosh R, Iyer Krishna R, Coutinho Evans C
Department of Pharmaceutical Chemistry, Vasvik Research Centre, Bombay College of Pharmacy, Kalina, Santacruz (E), Mumbai 400 098, India.
Ambernath Organics Pvt. Ltd., 222, The Summit Business Bay, Andheri (E), Mumbai 400 093, India.
ACS Omega. 2022 May 19;7(21):18094-18102. doi: 10.1021/acsomega.2c01613. eCollection 2022 May 31.
It has always been a challenge to develop interventional therapies for . Over the years, several attempts at developing such therapies have hit a dead-end owing to rapid mutation rates of the tubercular bacilli and their ability to lay dormant for years. Recently, cytochrome complex (QcrB) has shown some promise as a novel target against the tubercular bacilli, with Q203 being the first molecule acting on this target. In this paper, we report the deployment of several ML-based approaches to design molecules against QcrB. Machine learning (ML) models were developed based on a data set of 350 molecules using three different sets of molecular features, i.e., MACCS keys, ECFP6 fingerprints, and Mordred descriptors. Each feature set was trained on eight ML classifier algorithms and optimized to classify molecules accurately. The support vector machine-based classifier using the ECFP6 feature set was found to be the best classifier in this study. Further, screening of the known imidazopyridine amide inhibitors demonstrated that the model correctly classified the most potent molecules as actives, hence validating the model for future applications.
开发针对……的介入疗法一直是一项挑战。多年来，由于结核杆菌的快速突变率及其多年潜伏的能力，开发此类疗法的几次尝试都陷入了僵局。最近，细胞色素复合物（QcrB）作为一种针对结核杆菌的新型靶点显示出了一些前景，Q203是作用于该靶点的首个分子。在本文中，我们报告了几种基于机器学习的方法来设计针对QcrB的分子。基于350个分子的数据集，使用三组不同的分子特征，即MACCS键、ECFP6指纹和Mordred描述符，开发了机器学习（ML）模型。每个特征集都在八种ML分类器算法上进行训练，并进行优化以准确分类分子。在本研究中，发现使用ECFP6特征集的基于支持向量机的分类器是最佳分类器。此外，对已知的咪唑并吡啶酰胺抑制剂的筛选表明，该模型正确地将最有效的分子分类为活性分子，从而验证了该模型在未来应用中的有效性。
