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FDS Special Seminar
Nature Language Model: Deciphering the Language of Nature for Scientific Discovery
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Speaker: Peiran Jin Senior Research Engineer Microsoft Research Wednesday, March 19, 2025 1:00PM - 2:00PM Location: Bass Center for Molecular and Structural Biology, Room 405, 266 Whitney Avenue, New Haven, CT 06511 and via Webcast: https://yale.zoom.us/j/3082460000 |
Nature Language Model: Deciphering the Language of Nature for Scientific Discovery
[https://arxiv.org/abs/2502.07527]
Abstract: Foundation models have revolutionized natural language processing and artificial intelligence, significantly enhancing how machines comprehend and generate human languages. Inspired by the success of these foundation models, researchers have developed foundation models for individual scientific domains, including small molecules, materials, proteins, DNA, RNA and even cells. However, these models are typically trained in isolation, lacking the ability to integrate across different scientific domains. Recognizing that entities within these domains can all be represented as sequences, which together form the “language of nature”, we introduce Nature Language Model (NatureLM), a sequence-based science foundation model designed for scientific discovery. Pre-trained with data from multiple scientific domains, NatureLM offers a unified, versatile model that enables various applications including: (i) generating and optimizing small molecules, proteins, RNA, and materials using text instructions; (ii) cross-domain generation/design, such as protein-to-molecule and protein-to-RNA generation; and (iii) top performance across different domains, matching or surpassing state-of-the-art specialist models. NatureLM offers a promising generalist approach for various scientific tasks, including drug discovery (hit generation/optimization, ADMET optimization, synthesis), novel material design, and the development of therapeutic proteins or nucleotides. We have developed NatureLM models in different sizes (1 billion, 8 billion, and 46.7 billion parameters) and observed a clear improvement in performance as the model size increases.
Speaker bio: Peiran Jin is a Senior Research Engineer at Microsoft Research AI for Science team, where he focuses on LLM, Diffusion model, protein sequence and structure modeling. His work explores the intersection of deep learning and molecular modeling to protein structure prediction and related applications. Before joining Microsoft, Peiran was a Senior Research Engineer at Seagate R&D team, where he contributed to the development of heat-assisted magnetic recording technology. He holds a PhD in Physics from Georgetown University and a Bachelor’s degree from the University of Science and Technology of China.
Hosted by Mark Gerstein and the Gerstein Lab.
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