a two-step target binding and selectivity support vector machines approach for virtual screening of dopamine receptor subtype-selective ligands一个两步的目标绑定和选择性支持向量机方法subtype-selective多巴胺受体配体的虚拟筛选.pdfVIP

A Two-Step Target Binding and Selectivity Support Vector Machines Approach for Virtual Screening of Dopamine Receptor Subtype-Selective Ligands 1,2 2,3 2,3 1 1,2 1 Jingxian Zhang , Bucong Han , Xiaona Wei , Chunyan Tan , Yuzong Chen *, Yuyang Jiang * 1The Key Laboratory of Chemical Biology, Guangdong Province, Graduate School at Shenzhen, Tsinghua University, Shenzhen, People’s Republic of China, 2 Bioinformatics and Drug Design Group, Department of Pharmacy, Centre for Computational Science and Engineering, National University of Singapore, Singapore, Singapore, 3 Computation and Systems Biology, Singapore-MIT Alliance, National University of Singapore, Singapore, Singapore Abstract Target selective drugs, such as dopamine receptor (DR) subtype selective ligands, are developed for enhanced therapeutics and reduced side effects. In silico methods have been explored for searching DR selective ligands, but encountered difficulties associated with high subtype similarity and ligand structural diversity. Machine learning methods have shown promising potential in searching target selective compounds. Their target selective capability can be further enhanced. In this work, we introduced a new two-step support vector machines target-binding and selectivity screening method for searching DR subtype-selective ligands, which was tested together with three previously-used machine learning methods for searching D1, D2, D3 and D4 selective ligands. It correctly identified 50.6%–88.0% of the 21–408 subtype selective and 71.7%–81.0% of the 39–147 multi-subtype ligands. Its subtype selective ligand identification rates are significantly better than, and its