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.%基质金属蛋白酶-13(MMP-13)为预防和治疗骨关节炎(OA)提供了充满希望的靶标.通过抑制剂来阻断MMP-13的活性将会对治疗OA疾病产生潜在的作用.然而,宽谱抑制剂同样抑制MMP家族的其它成员,特别是MMP-1,这将会导致肌与骨的综合症.因此,设计和发现潜在的MMP-13相对于MMP-1的高效选择性抑制剂,在对治疗OA新型药物的研发中具有相当重要的现实意义.本研究通过两种机器学习方法(ML)：支持向量机(SVM)和随机森林(RF)来建立分类模型,用于预测不同结构的MMP-13对MMP-1的选择性抑制剂.所建这些模型的预测效果都已经达到了令人满意的精度.在这两种ML模型中, RF对于MMP-13选择性抑制剂和非抑制剂的精度分别达到97.58%和100%.同时,与MMP-13对MMP-1的选择性抑制最相关的分子描述符也基于不同的特征选择方法被两种模型挑选出来.最后,用预测效果最好的RF模型虚拟筛选了ZINC数据库的“fragment-like”子集,从而得到了一系列潜在的候选药物.研究表明,机器学习方法,特别是RF方法,对于发现潜在的MMP-13选择性抑制剂十分有效.同时还得到了一些与MMP-13的选择性抑制相关的分子描述符.
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