报告题目：Co-regularized variational autoencoders for Drug-target binding affinity prediction
报 告 人：李丽敏
主 持 人：郭菲
报告简介：Drug-target binding affinity has been a key step in drug discovery. In this talk, I will present our co-regularized variational autoencoders (Co-VAE) for predicting drug-target binding affinity based on drug structures and target sequences. The Co-VAE model consists of two VAEs for generating drug SMILES strings and target sequences, respectively, and a co-regularization part for generating the binding affinities. We theoretically prove that the Co-VAE model is to maximize the lower bound of the joint likelihood of drug, protein and their affinity. The Co-VAE could predict drug-target affinity and generate new drugs which share similar targets with the input drugs. The experimental results on two datasets show that the Co-VAE could predict drug-target affinity better than existing affinity prediction methods such as DeepDTA and DeepAffinity, and could generate more new valid drugs than existing methods such as GAN and VAE.
讲者简介：李丽敏，西安交通大学数学与统计学院教授，博士生导师。本科和硕士毕业于浙江大学，博士毕业于香港大学。主要研究机器学习和统计方法以及它们在生物信息中应用，包括药物靶蛋白识别，癌症亚型识别，跨物种预测等。近年来在相关领域发表多篇论文，包括TPAMI, Briefings in Bioinformatics, SIAM Journal on Scientific Computing, TCBB等。主持国家自然科学基金面上项目和优秀青年科学基金。