北京雁栖湖应用数学研究院 北京雁栖湖应用数学研究院

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关于我们
院长致辞
理事会
协作机构
参观来访
人员
管理层
科研人员
博士后
来访学者
行政团队
学术支持
学术研究
研究团队
公开课
讨论班
期刊
招生招聘
教研人员
博士后
学生
会议
学术会议
工作坊
论坛
学院生活
住宿
交通
配套设施
周边旅游
意见建议
新闻
新闻动态
通知公告
资料下载
清华大学 "求真书院"
清华大学丘成桐数学科学中心
清华三亚国际数学论坛
上海数学与交叉学科研究院
河套数学与交叉学科研究院
BIMSA > BIMSA 计算数学讨论班 BIMSA 计算数学讨论班 Pushing the Boundaries of Chemical Synthesis with AI
Pushing the Boundaries of Chemical Synthesis with AI
组织者
Theory and Computation of PDE Team
演讲者
Guoqing Liu
时间
2026年11月05日 15:00 至 16:00
地点
Online
线上
Zoom 518 868 7656 (BIMSA)
摘要
Making a new molecule starts with a plan: working backwards from the target to simple, available building blocks. This task, known as retrosynthesis, is essential to drug discovery, and it is a hard problem for AI. The space of possible answers is enormous, and the reactions that matter most are often the rarest in the data. This talk presents RetroChimera, recently published in Nature. Instead of building one ever-larger model, we combine two models that think differently: one edits the molecule step by step, while the other writes the answer from scratch. Each excels on different kinds of reactions, and a learned ensemble lets them cover each other's blind spots. RetroChimera handles both common and rare reactions, halves the error rate of strong baselines and transfers to real data from two pharmaceutical companies. In blinded tests, expert chemists preferred its predictions over reactions published in the literature. A predicted reaction, however, is not yet an experiment. I will close with QFANG, a reasoning language model that turns a reaction into step-by-step laboratory instructions, moving AI from planning chemical synthesis towards carrying it out.
演讲者介绍
Guoqing Liu is a Senior Researcher at Microsoft Research AI for Science in Cambridge, UK. His research interests include reinforcement learning, large language models, and AI for Science, with a focus on post-training language models for scientific reasoning and on AI systems that move chemical synthesis from route planning towards laboratory execution. He has published extensively as a first or corresponding author in leading scientific journals and machine learning conferences, including Nature, Nature Machine Intelligence, ICML, NeurIPS, and ICLR, with over 2,000 citations on Google Scholar. Previously, he was a researcher at Microsoft Research Asia, focusing on reinforcement learning. He received his PhD from the University of Science and Technology of China through a joint program with Microsoft Research Asia.
北京雁栖湖应用数学研究院
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