Y-Mol: A Multiscale Biomedical Knowledge-Guided Large Language Model for Drug Development

• URL: http://arxiv.org/abs/2410.11550v1
• Date: Tue, 15 Oct 2024 12:39:20 GMT
• Title: Y-Mol: A Multiscale Biomedical Knowledge-Guided Large Language Model for Drug Development
• Authors: Tengfei Ma, Xuan Lin, Tianle Li, Chaoyi Li, Long Chen, Peng Zhou, Xibao Cai, Xinyu Yang, Daojian Zeng, Dongsheng Cao, Xiangxiang Zeng,
• Abstract summary: Y-Mol is a knowledge-guided LLM designed to accomplish tasks across lead compound discovery, pre-clinic, and clinic prediction. It learns from a corpus of publications, knowledge graphs, and expert-designed synthetic data. Y-Mol significantly outperforms general-purpose LLMs in discovering lead compounds, predicting molecular properties, and identifying drug interaction events.
• Score: 24.5979645373074
• License:
• Abstract: Large Language Models (LLMs) have recently demonstrated remarkable performance in general tasks across various fields. However, their effectiveness within specific domains such as drug development remains challenges. To solve these challenges, we introduce \textbf{Y-Mol}, forming a well-established LLM paradigm for the flow of drug development. Y-Mol is a multiscale biomedical knowledge-guided LLM designed to accomplish tasks across lead compound discovery, pre-clinic, and clinic prediction. By integrating millions of multiscale biomedical knowledge and using LLaMA2 as the base LLM, Y-Mol augments the reasoning capability in the biomedical domain by learning from a corpus of publications, knowledge graphs, and expert-designed synthetic data. The capability is further enriched with three types of drug-oriented instructions: description-based prompts from processed publications, semantic-based prompts for extracting associations from knowledge graphs, and template-based prompts for understanding expert knowledge from biomedical tools. Besides, Y-Mol offers a set of LLM paradigms that can autonomously execute the downstream tasks across the entire process of drug development, including virtual screening, drug design, pharmacological properties prediction, and drug-related interaction prediction. Our extensive evaluations of various biomedical sources demonstrate that Y-Mol significantly outperforms general-purpose LLMs in discovering lead compounds, predicting molecular properties, and identifying drug interaction events.

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