yl9193永利官网系列讲座名家讲坛第25期——mCLM: A Function-Infused and Synthesis-Friendly Modular Chemical Language Model (or: mCLM: 一个融合功能且易于合成的模块化化学语言模型)

报告题目(Title)：mCLM: A Function-Infused and Synthesis-Friendly Modular Chemical Language Model (or: mCLM: 一个融合功能且易于合成的模块化化学语言模型)

时间(Date & Time)：2025.7.7 10:00-11:30

地点(Location)：理科一号楼1453（燕园校区） Room 1453, Science Building #1 (Yanyuan)

主讲人(Speaker)：Heng Ji

邀请人(Host)：Sujian Li

报告摘要(Abstract)：

Everything in our wonderful world is composed of molecules. Recent advances in block  chemistry involve the manual design of drugs and materials by decomposing molecules into graph substructures—i.e., functional modules—and reassembling them into new molecules with desired functions. However, the process of discovering and manufacturing functional molecules has remained highly artisanal, slow, and expensive. Most importantly, there are many instances of known commercial drugs or materials that have well-documented functional limitations that have remained unaddressed. In this talk I will present our recent efforts at teaching computers to speak two complementary languages: one that represents molecular subgraph structures indicative of specific functions, and another that describes these functions in natural language. Unlike existing approaches that add such knowledge as a post hoc step, we developed a function- and synthesis-aware modular chemical language model (mCLM). Inspired by bilingual speakers who frequently “code-switch”, we propose a novel neural encoder that integrates molecular structure and natural language. mCLM incorporates both function- and synthesis-related knowledge into the small molecule tokenization process a priori. In experiments on 430 FDA-approved drugs, we find mCLM capable of significantly improving 5 out of 6 chemical functions critical to determining drug potentials. More importantly, mCLM can reason on multiple functions and improve the FDA-rejected drugs (“fallen angels”) over multiple iterations to greatly improve their shortcomings.

主讲人简介(Bio)：

Heng Ji is a Professor of Computer Science at Siebel School of Computing and Data Science, and a faculty member affiliated with Electrical and Computer Engineering Department, Coordinated Science Laboratory, and Carl R. Woese Institute for Genomic Biology of University of Illinois Urbana-Champaign. She is an Amazon Scholar. She is the Founding Director of Amazon-Illinois Center on AI for Interactive Conversational Experiences (AICE), and the Founding Director of CapitalOne-Illinois Center on AI Safety and Knowledge Systems (ASKS). She received Ph.D. in Computer Science from New York University. Her research interests focus on Natural Language Processing, especially on Multimedia Multilingual Information Extraction, Knowledge-enhanced Large Language Models and Vision-Language Models, and AI for Science. The awards she received include Outstanding Paper Award at ACL2024, two Outstanding Paper Awards at NAACL2024, "Young Scientist" by the World Laureates Association in 2023 and 2024, "Young Scientist" and a member of the Global Future Council on the Future of Computing by the World Economic Forum in 2016 and 2017, "Women Leaders of Conversational AI" (Class of 2023) by Project Voice, "AI's 10 to Watch" Award by IEEE Intelligent Systems in 2013, NSF CAREER award in 2009, PACLIC2012 Best paper runner-up, "Best of ICDM2013" paper award, "Best of SDM2013" paper award, ACL2018 Best Demo paper nomination, ACL2020 Best Demo Paper Award, NAACL2021 Best Demo Paper Award, Google Research Award in 2009 and 2014, IBM Watson Faculty Award in 2012 and 2014 and Bosch Research Award in 2014-2018. She served as the associate editor for IEEE/ACM Transaction on Audio, Speech, and Language Processing, and the Program Committee Co-Chair of many conferences including NAACL-HLT2018 and AACL-IJCNLP2022. She was elected as the North American Chapter of the Association for Computational Linguistics (NAACL) secretary 2020-2023.

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