通过思维链进行有效和可解释的分子性质预测,使大语言模型和多模式分子信息融合成为可能
Chang Jin1, Siyuan Guo1, Shuigeng Zhou2
1Department of Computer Science and Technology, Tongji University, Shanghai 200092, China.
Journal of chemical information and modeling
|May 20, 2025
概括
本研究介绍了LLM-MPP,这是一种使用大语言模型 (LLM) 进行准确和可解释的药物发现中的分子性质预测的新型多式联络方法. 它有效地整合了各种分子数据,优于现有方法.
科学领域:
- 计算化学计算化学
- 药物发现信息学 药物发现信息学
- 化学中的人工智能.
背景情况:
- 分子性质预测 (MPP) 对于药物设计至关重要,它可以从多式联络数据 (SMILES,图表) 中获益.
- 现有的方法通常使用有限的模式和简单的融合技术,无法利用补充信息.
- 目前的模型缺乏可解释性,这是药物发现任务的关键要求.
研究的目的:
- 开发一种有效且可解释的大型语言模型 (LLM) 驱动的多式联络方法,用于药物分子性质预测.
- 解决多式联运数据集成和可解释性的现有方法的局限性.
- 提高分子性质预测的准确性和透明度.
主要方法:
- 在LLM-MPP中,我们利用1D SMILES,2D分子图形和文本描述来实现多模式学习.
- 纳入思维链 (CoT) 以提高可解释性和跨模式对齐.
- 采用交叉注意力和对比学习来实现有效的多式联接表示融合.
主要成果:
- 在9个基准数据集中的5个中实现了对分子性质预测的最先进的性能.
- 在1个数据集中排名第二,超过了22个现有的基线方法.
- 废弃性研究证实了拟议的创新模块的有效性.
结论:
- 法学士-MPP在多式联络分子性质预测方面取得了重大进展.
- 该方法提供了可解释的结果,对于药物设计和发现至关重要.
- 证明了LLM和多式融合在复杂化学任务中的力量.
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