摩尔卡普:分子化学反应活性预训练和提示微调增强分子表示学习
Yu Wang1, Jingjie Zhang1, Junru Jin1
1School of Software, Shandong University, Jinan, 250101, China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China.
Computers in biology and medicine
|November 13, 2023
概括
分子表示学习 (MRL) 得到了MolCAP的增强,这是一种结合化学反应性的新方法. 这种方法通过揭示更深层次的分子关系和在各种生物医学任务上超越现有模型来改善药物发现.
科学领域:
- 计算化学的计算化学
- 药物发现 药物发现 药物发现
- 机器学习 机器学习
背景情况:
- 分子表示学习 (MRL) 对于药物发现至关重要.
- 当前的深度学习方法往往忽略了化学反应性,这是分子性质的关键因素.
- 现有的预训练框架主要侧重于分子内表征.
研究的目的:
- 介绍MolCAP,一个新的图形预训练变压器模型.
- 为了利用化学反应性 (IMR) 的知识来提高MRL.
- 改进分子性质预测和药物发现.
主要方法:
- 开发了基于变压器的图形预训练模型MolCAP.
- 内置的内在分子反应性 (IMR) 知识.
- 使用提示微调来进行模型调整.
主要成果:
- 在13个不同的分子数据集中,MolCAP显著优于传统的MRL方法.
- 即使是基本的图形神经网络在被MolCAP提示时也获得了卓越的性能.
- 在MRL中证明了化学反应性信息的有效性.
结论:
- 化学反应性信息为推进药物发现的MRL提供了一个有希望的途径.
- MolCAP提供了更深入的化学洞察力,增强了药物发现管道.
- 分子模板可以帮助识别和减轻数据集偏差.
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