Prediction of Mycobacterium tuberculosis cell wall permeability using machine learning methods

IF 3.9 2区 化学 Q2 CHEMISTRY, APPLIED
Aritra Banerjee, Anju Sharma, Pradnya Kamble, Prabha Garg
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Abstract

Tuberculosis (TB) caused by the bacteria Mycobacterium tuberculosis (M. tb), continues to pose a significant worldwide health threat. The advent of drug-resistant strains of the disease highlights the critical need for novel treatments. The unique cell wall of M. tb provides an extra layer of protection for the bacteria and hence only compounds that can penetrate this barrier can reach their targets within the bacterial cell wall. The creation of a reliable machine learning (ML) model to predict the mycobacterial cell wall permeability of small molecules is presented in this work and four ML algorithms, including Random Forest, Support Vector Machines (SVM), k-nearest Neighbour (k-NN) and Logistic Regression were trained on a dataset of 5368 compounds. RDKit and Mordred toolkits were used to calculate features. To determine the most effective model, various performance metrics were used such as accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve. The best-performing model was further refined with hyperparameter tuning and tenfold cross-validation. The SVM model with filtering outperformed the other machine learning models and demonstrated 80.26% and 81.13% accuracy on the test and validation datasets, respectively. The study also provided insights into the molecular descriptors that play the most important role in predicting the ability of a molecule to pass the M. tb cell wall, which could guide future compound design. The model is available at https://github.com/PGlab-NIPER/MTB_Permeability.

Abstract Image

利用机器学习方法预测结核分枝杆菌细胞壁的渗透性。
由结核分枝杆菌(M. tb)引起的结核病(TB)继续对全世界的健康构成重大威胁。耐药菌株的出现凸显了对新型疗法的迫切需要。结核杆菌独特的细胞壁为细菌提供了一层额外的保护,因此只有能够穿透这层屏障的化合物才能到达细菌细胞壁内的目标。本文介绍了如何创建一个可靠的机器学习(ML)模型来预测小分子的分枝杆菌细胞壁渗透性,并在一个包含 5368 种化合物的数据集上训练了四种 ML 算法,包括随机森林、支持向量机(SVM)、k-最近邻(k-NN)和逻辑回归。RDKit 和 Mordred 工具包用于计算特征。为了确定最有效的模型,使用了各种性能指标,如准确度、精确度、召回率、F1 分数和接收者工作特征曲线下面积。通过超参数调整和十倍交叉验证,进一步完善了表现最佳的模型。带有过滤功能的 SVM 模型在测试数据集和验证数据集上的表现优于其他机器学习模型,准确率分别为 80.26% 和 81.13%。该研究还深入揭示了在预测分子通过结核杆菌细胞壁的能力方面发挥最重要作用的分子描述符,这可以为未来的化合物设计提供指导。该模型可在 https://github.com/PGlab-NIPER/MTB_Permeability 上查阅。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Molecular Diversity
Molecular Diversity 化学-化学综合
CiteScore
7.30
自引率
7.90%
发文量
219
审稿时长
2.7 months
期刊介绍: Molecular Diversity is a new publication forum for the rapid publication of refereed papers dedicated to describing the development, application and theory of molecular diversity and combinatorial chemistry in basic and applied research and drug discovery. The journal publishes both short and full papers, perspectives, news and reviews dealing with all aspects of the generation of molecular diversity, application of diversity for screening against alternative targets of all types (biological, biophysical, technological), analysis of results obtained and their application in various scientific disciplines/approaches including: combinatorial chemistry and parallel synthesis; small molecule libraries; microwave synthesis; flow synthesis; fluorous synthesis; diversity oriented synthesis (DOS); nanoreactors; click chemistry; multiplex technologies; fragment- and ligand-based design; structure/function/SAR; computational chemistry and molecular design; chemoinformatics; screening techniques and screening interfaces; analytical and purification methods; robotics, automation and miniaturization; targeted libraries; display libraries; peptides and peptoids; proteins; oligonucleotides; carbohydrates; natural diversity; new methods of library formulation and deconvolution; directed evolution, origin of life and recombination; search techniques, landscapes, random chemistry and more;
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