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A Study of Applications of Machine Learning Based Classification Methods for Virtual Screening of Lead Molecules

机译：基于机器学习的分类方法在铅分子虚拟筛选中的应用研究

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摘要

The ligand-based virtual screening of combinatorial libraries employs a number of statistical modeling and machine learning methods. A comprehensive analysis of the application of these methods for the diversity oriented virtual screening of biological targets/drug classes is presented here. A number of classification models have been built using three types of inputs namely structure based descriptors, molecular fingerprints and therapeutic category for performing virtual screening. The activity and affinity descriptors of a set of inhibitors of four target classes DHFR, COX, LOX and NMDA have been utilized to train a total of six classifiers viz. Artificial Neural Network (ANN), k nearest neighbor (k-NN), Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree - (DT) and Random Forest - (RF). Among these classifiers, the ANN was found as the best classifier with an AUC of 0.9 irrespective of the target. New molecular fingerprints based on pharmacophore, toxicophore and chemophore (PTC), were used to build the ANN models for each dataset. A good accuracy of 87.27% was obtained using 296 chemophoric binary fingerprints for the COX-LOX inhibitors compared to pharmacophoric (67.82 %) and toxicophoric (70.64 %). The methodology was validated on the classical Ames mutagenecity dataset of 4337 molecules. To evaluate it further, selectivity and promiscuity of molecules from five drug classes viz. anti-anginal, anti-convulsant, anti-depressant, anti-arrhythmic and anti-diabetic were studied. The TPC fingerprints computed for each category were able to capture the drug-class specific features using the k-NN classifier. These models can be useful for selecting optimal molecules for drug design.
机译：组合库基于配体的虚拟筛选采用了许多统计模型和机器学习方法。本文介绍了这些方法在生物目标/药物类别的面向多样性的虚拟筛选中的应用的综合分析。使用三种类型的输入，即基于结构的描述符，分子指纹和用于进行虚拟筛选的治疗类别，已经建立了许多分类模型。一组四个目标类别DHFR，COX，LOX和NMDA抑制剂的活性和亲和力描述子已被用来训练总共六个分类器。人工神经网络（ANN），k最近邻（k-NN），支持向量机（SVM），朴素贝叶斯（NB），决策树-（DT）和随机森林-（RF）。在这些分类器中，与目标无关，ANN被认为是AUC为0.9的最佳分类器。基于药效团，毒理基团和化学基团（PTC）的新分子指纹被用于建立每个数据集的ANN模型。与药效团（67.82％）和毒理团（70.64％）相比，使用296种化学发光的COX-LOX抑制剂二元指纹图谱可达到87.27％的良好准确性。该方法论已在4337个分子的经典Ames诱变数据集上得到验证。为了进一步评估，来自五个药物类别的分子的选择性和混杂性。研究了抗心绞痛，抗惊厥药，抗抑郁药，抗心律失常药和抗糖尿病药。为每个类别计算的TPC指纹能够使用k-NN分类器捕获药物类别的特定特征。这些模型可用于选择用于药物设计的最佳分子。

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