计算机辅助药物设计
实验室主要研究课题：
- Application of machine learning methods for prediction the bioactivities of inhibitors of anti-cancer targets (Aurora-A (or B) kinase, Epidermal growth factor receptor (EGFR)) and virtual screening for finding new inhibitors. 抗癌药物设计靶点 (极光激酶A和B、表皮生长因子受体EGFR) 的抑制剂生物活性的计算预测及抑制剂的虚拟筛选研究；
- 抗丙型肝炎病毒药物设计靶点 (HCV 聚合酶、HCV 蛋白酶) 的抑制剂生物活性的计算预测研究； Application of machine learning methods for building SAR/QSAR models for prediction the bioactivities of inhibitors of anti-HCV targets (HCV polymerase, HCV protease).
- Application of machine learning methods for building SAR/QSAR models for prediction the bioactivities of inhibitors of anti-HIV targets (HIV-1 protease; HIV-1 integrase: including strand transfer (ST), 3’ processing and LEDGF/p75 related binding sites).抗艾滋病药物靶点 (HIV-1整合酶ST作用位点、3’p作用位点、LEDGF/p75作用位点、HIV-1蛋白酶) 的抑制剂生物活性的计算预测研究；
- 抗炎药物设计靶点 (环氧合酶COX-1、COX-2、前列腺素E 2合酶（mPGES）、5-脂氧合酶 (5-LOX)、非胰腺分泌型磷脂酶A2 (GIIA sPLA2 ) ) 的抑制剂生物活性的计算预测研究； Application of machine learning methods for building SAR/QSAR models for prediction the bioactivities of inhibitors of anti-inflammation targets (cyclooxygenases: including COX-1 and COX-2; microsomal Prostaglandin E2 Synthase (mPGES); 5-lipoxygenase (5-LOX); GIIA secreted phospholipase A2 (GIIA sPLA2)).
