物理化学学报Issue(1)：171-182,12.DOI:10.3866/PKU.WHXB201311041
基于分子描述符和机器学习方法预测和虚拟筛选MMP-13对MMP-1的选择性抑制剂
Predicting and Virtually Screening the Selective Inhibitors of MMP-13 over MMP-1 by Molecular Descriptors and Machine Learning Methods
摘要
Abstract
Matrix metal oproteinase-13 (MMP-13) is an interesting target for the prevention and therapy of osteoarthritis (OA). Interruption of MMP-13 activity with an inhibitor has the potential to affect OA. However, a broad-spectrum inhibitor, which restrains the other members of the MMP family, especial y MMP-1, can cause musculoskeletal syndrome. So, the design and discovery of potential and highly selective inhibitors for MMP-13 over MMP-1 are necessary and of great significance for the development of novel therapeutic agents against OA. Two machine-learning (ML) methods, support vector machine and random forest (RF), were explored in this work to develop classification models for predicting selective inhibitors of MMP-13 over MMP-1 from diverse compounds. These ML models achieved promising prediction accuracies. Among the two ML models, RF gave the better performance, i.e., 97.58% for MMP-13 selective inhibitors and 100%for non-inhibitors. We also used different feature selection methods to extract the molecular features most relevant to selective inhibition of MMP-13 over MMP-1 from the two models. In addition, the better-performing RF model was used to perform virtual screening of MMP-13 selective inhibitors against the“fragment-like”subset of the ZINC database to enrich the potential active agents, thereby obtaining a series of the most potent candidates. Our study suggests that ML methods, particularly RF, are potentially useful for facilitating the discovery of MMP-13 inhibitors and for identifying the molecular descriptors associated with MMP-13 selective inhibitors.关键词
基质金属蛋白酶-13/选择性抑制剂/机器学习方法/支持向量机/随机森林/虚拟筛选Key words
Matrix metal oproteinase-13/Selective inhibitor/Machine learning method/Support vector machine/Random forest/Virtual screening分类
化学化工引用本文复制引用
李秉轲,丛湧,田之悦,薛英..基于分子描述符和机器学习方法预测和虚拟筛选MMP-13对MMP-1的选择性抑制剂[J].物理化学学报,2014,(1):171-182,12.基金项目
The project was supported by the National Natural Science Foundation of China (21173151).@@@@国家自然科学基金(21173151)资助项目 （21173151）
