Citation: LI Bing-Ke, CONG Yong, TIAN Zhi-Yue, XUE Ying. Predicting and Virtually Screening the Selective Inhibitors of MMP-13 over MMP-1 by Molecular Descriptors and Machine Learning Methods[J]. Acta Physico-Chimica Sinica, ;2014, 30(1): 171-182. doi: 10.3866/PKU.WHXB201311041 shu

Predicting and Virtually Screening the Selective Inhibitors of MMP-13 over MMP-1 by Molecular Descriptors and Machine Learning Methods

LI Bing-Ke ¹ ,
CONG Yong ¹ ,
TIAN Zhi-Yue ¹ ,
XUE Ying ^1,2,

1.
1 College of Chemistry, Key Laboratory of Green Chemistry and Technology, Ministry of Education, Sichuan University, Chengdu 610064, P. R. China;
2 State Key Laboratory of Biotherapy, Sichuan University, Chengdu 610041, P. R. China

Received Date: 5 July 2013
Available Online: 4 November 2013
Fund Project: 国家自然科学基金(21173151)资助项目 (21173151)

Matrix metalloproteinase-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, especially 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 betterperforming 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.
- Matrix metalloproteinase-13,
- Selective inhibitor,
- Machine learning method,
- Support vector machine,
- Random forest,
- Virtual screening
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沈阳化工大学材料科学与工程学院沈阳 110142