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MolPROP:使用多模式语言和图形融合进行分子性质预测

Zachary A Rollins¹, Alan C Cheng², Essam Metwally³

¹Modeling and Informatics, Merck & Co., Inc., South San Francisco, CA, USA. zachary.rollins@merck.com.

Journal of cheminformatics

|May 22, 2024

概括

这项研究介绍了MolPROP,一种新的语言和图形模型的多式融合,用于预测小分子特性. MolPROP与关键回归任务的现有方法相匹配或超越,例如水化自由能量和可溶性.

科学领域:

计算化学和化学信息学
深度学习和人工智能
药物的发现和开发.

背景情况:

预训练有素的深度学习模型通过对下游任务进行微调,在各种领域表现出色.
多模式数据融合旨在通过整合不同的数据表示来提高性能.
分子性质预测对于药物发现和材料科学至关重要.

研究的目的:

介绍和评估MolPROP,一种用于分子性质预测的新型多式联络融合模型.
将MolPROP与各种分子数据集上的最新架构进行基准测试.
调查不同预训练策略对多式联性能的影响.

主要方法:

预训练语言模型 (ChemBERTa-2) 与图形神经网络的融合.
在七个支架分割的MoleculeNet数据集中对MolPROP套件进行基准测试.
掩盖语言模型 (MLM) 和多任务回归 (MTR) 预训练任务的比较.

主要成果:

MolPROP与现代架构相匹配或优于现代架构的回归任务,例如无水化无能量,溶解性,脂性和毒性.
多模式融合主要有利于回归任务.
当与图形神经网络合并时,ChemBERTa-2的MLM预训优于MTR.
尽管有回归改进,但MolPROP在某些分类任务中表现不佳.

结论:

语言和图形表示的新型多式融合增强了小分子属性预测,特别是在回归任务中.
选择预训练策略 (MLM与MTR) 影响多式联络融合的有效性.
在多式联络分子性质预测中的分类任务中,进一步探索融合策略是有必要的.

关键词:

深度学习 (Deep Learning) 是一种深度学习.图表图表图表图表语言语言语言语言分子特性分子特性.多式联络是多式联络.

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