使用MoE增强的定向消息传递神经网络进行温度依赖的小分子可溶性预测
Lixiang Guo1, Yujing Zhao1,2, Qilei Liu1,3
1State Key Laboratory of Fine Chemicals, Frontiers Science Center for Smart Materials Oriented Chemical Engineering, Department of Pharmaceutical Sciences, Institute of Chemical Process Systems Engineering, School of Chemical Engineering, Dalian University of Technology, Dalian 116024, China.
Journal of chemical information and modeling
|July 10, 2025
概括
一种新的可溶性预测模型,DMPNN-MoE,通过整合定向消息传递神经网络 (DMPNN) 和专家混合 (MoE) 来提高药物开发的准确性. 这种方法提高了跨不同溶剂和温度的通用性.
科学领域:
- 计算化学计算化学
- 机器学习在材料科学中的应用
- 药物发现信息学 药物发现信息学
背景情况:
- 准确的可溶性预测对于制药开发和材料科学至关重要.
- 现有的模型往往缺乏跨各种溶剂和温度的通用性.
- 挑战包括捕捉复杂的分子相互作用和结构细微差别.
研究的目的:
- 开发一种新的,可通用的溶解性预测模型,DMPNN-MoE.
- 为了提高对各种化学系统的温度依赖溶解度的预测.
- 为理解溶解性的关键分子驱动因素提供一个可解释的模型.
主要方法:
- 集成一个定向消息传递神经网络 (DMPNN) 用于特征提取.
- 利用专家混合 (MoE) 算法进行自适应式学习.
- 在56,945个实验溶解度值的大数据集上进行培训和验证.
主要成果:
- 通过十倍交叉验证,DMPNN-MoE通过十倍交叉验证实现了卓越的性能 (MAE = 0.256,R2 = 0.863).
- 超越现有的基于图形和基于描述器的模型高达35%.
- 证明了对未见的稀有溶剂和溶解物具有高精度的强大泛化.
结论:
- DMPNN-MoE模型在可溶性预测准确性和通用性方面取得了重大进展.
- 该模型提供了对影响溶解度的关键分子特征的见解,例如环结构.
- 这种可解释的模型在制药发现和化学工程中具有广泛的应用.
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