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X-WR-CALDESC:Events for Biomedical Mathematics Group
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DTSTART:20240101T000000
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DTSTART;TZID=Asia/Seoul:20250620T110000
DTEND;TZID=Asia/Seoul:20250620T123000
DTSTAMP:20260423T081158
CREATED:20250426T143500Z
LAST-MODIFIED:20250617T001232Z
UID:11064-1750417200-1750422600@www.ibs.re.kr
SUMMARY:Large language models for scientific discovery in molecular property prediction - Aqsa Awan
DESCRIPTION:In this talk\, we discuss the paper “Large language models for scientific discovery in molecular property prediction” by Yizhen Zheng et.al.\, nature machine intelligence\, 2025. \nAbstract \nLarge language models (LLMs) are a form of artificial intelligence system encapsulating vast knowledge in the form of natural language. These systems are adept at numerous complex tasks including creative writing\, storytelling\, translation\, question-answering\, summarization and computer code generation. Although LLMs have seen initial applications in natural sciences\, their potential for driving scientific discovery remains largely unexplored. In this work\, we introduce LLM4SD\, a framework designed to harness LLMs for driving scientific discovery in molecular property prediction by synthesizing knowledge from literature and inferring knowledge from scientific data. LLMs synthesize knowledge by extracting established information from scientific literature\, such as molecular weight being key to predicting solubility. For inference\, LLMs identify patterns in molecular data\, particularly in Simplified Molecular Input Line Entry System-encoded structures\, such as halogen-containing molecules being more likely to cross the blood–brain barrier. This information is presented as interpretable knowledge\, enabling the transformation of molecules into feature vectors. By using these features with interpretable models such as random forest\, LLM4SD can outperform the current state of the art across a range of benchmark tasks for predicting molecular properties. We foresee it providing interpretable and potentially new insights\, aiding scientific discovery in molecular property prediction.
URL:https://www.ibs.re.kr/bimag/event/large-language-models-for-scientific-discovery-in-molecular-property-prediction-aqsa-awan/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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