摩尔预言:通过多模式框架将药物化学家的知识与分子预训练模型联系起来
Jianping Zhao1, Qiong Zhou1, Tian Wang2
1College of Computer Science and Technology, Changchun University of Science and Technology, Changchun, Jilin 130012, China.
Journal of advanced research
|November 7, 2025
概括
一个新的框架MolProphecy将模拟化学家的专业知识与分子数据相结合,以提高药物发现预测. 这种方法提高了准确性和可解释性,在多个基准上表现优于现有模型.
科学领域:
- 计算化学是一种计算化学.
- 人工智能在药物发现中的作用
- 机器学习用于分子性质预测和预测.
背景情况:
- 专家领域的知识对于药物发现中的有效分子设计至关重要.
- 当前的分子预训练模型往往忽视了专家的见解,导致了低于最佳的结果.
- 将默认知识与结构数据相结合,可以增强预测模型.
研究的目的:
- 为了引入MolProphecy,一个代理的人在循环 (代理HITL) 多模式框架.
- 将化学家的领域知识与结构分子信息相结合.
- 提高药物发现中的预测准确性和可解释性.
主要方法:
- 模拟化学家推理使用ChatGPT生成专家见解.
- 用大型语言模型 (LLM) 编码洞察力.
- 通过交叉注意力将LLM编码的知识与基于图形的分子特征融合在一起.
主要成果:
- 在9个MoleculeNet基准指标中,MolProphecy的表现始终优于基线模型.
- 在FreeSolv上实现了9.1%的RMSE减少,并在BACE,SIDER和ClinTox上改善了AUROC.
- 在独立的可溶性数据集上展示了强大的概括性.
结论:
- 通过结合模拟专业知识和结构数据,MolProphecy为分子性质预测提供了一个可概括的框架.
- 该框架允许无整合真正的化学家知识,而不需要再培训.
- 建立了协作和可解释的药物发现的途径.
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