北京大学定量生物学中心

学术报告 

题    目:  Prediction of drug efficacy from transcriptional profiles with deep learning

报告人:  谢正伟

北京大学国际癌症研究院副研究员

时    间: 10月11日（周一）13:00-14:00

地    点: 吕志和楼三层大厅

主持人: 林一瀚研究员/韩敬东教授

摘 要:

Drug discovery focused on target proteins has been a successful strategy, but many diseases and biological processes lack obvious targets to enable such approaches. Here, to overcome this challenge, we describe a deep learning–based efficacy prediction system (DLEPS) that identifies drug candidates using a change in the gene expression profile in the diseased state as input. DLEPS was trained using chemically induced changes in transcriptional profiles from the L1000 project. We found that the changes in transcriptional profiles for previously unexamined molecules were predicted with a Pearson correlation coefficient of 0.74. We examined three disorders and experimentally tested the top drug candidates in mouse disease models. Validation showed that perillen, chikusetsusaponin IV and trametinib confer disease-relevant impacts against obesity, hyperuricemia and nonalcoholic steatohepatitis, respectively. DLEPS can generate insights into pathogenic mechanisms, and we demonstrate that the MEK–ERK signaling pathway is a target for developing agents against nonalcoholic steatohepatitis. Our findings suggest that DLEPS is an effective tool for drug repurposing and discovery.

报告人简介：

谢正伟，博士毕业于北京大学定量生物中心，师从欧阳颀和李浩教授；现任北京大学国际癌症研究院副研究员。主要应用交叉学科手段，包括人工智能和计算生物学方法，进行衰老相关研究和代谢疾病的创新药物研发。正在进行的研究方向包括衰老、癌症、肥胖、非酒精性肝炎、肌腱干细胞、高尿酸等疾病的药物研发；使用合成染色体研究的衰老的分子机制；基于人工智能的新型算法平台等。