基于碎片的图形是药物设计中更好的基于图形的分子表示吗? 一个基于图形的模型的比较研究
Baiyu Chen1, Ziqi Pan2, Minjie Mou2
1Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 202103, China.
Computers in biology and medicine
|January 3, 2024
概括
基于碎片的图形神经网络 (GNN) 增强了AI药物设计模型,特别是在有限的数据下. 虽然它们是有效的,但它们的表现可能不会超过经过广泛培训的传统方法.
科学领域:
- 人工智能的人工智能
- 计算化学的计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 图形神经网络 (GNN) 越来越多地用于人工智能驱动的药物设计.
- 将碎片化概念集成到GNN中旨在改进分子生成模型.
- 像对称性破坏和循环表示等挑战质疑基于片段的方法的优越性.
研究的目的:
- 严格评估和比较八种深度学习模型的预测性能.
- 将传统的GNN与创新的基于片段的GNN方法进行对比.
- 在12个不同的基准数据集中评估模型有效性.
主要方法:
- 开发并测试了八种深度学习模型,包括GCN,AttentiveFP,D-MPNN和基于片段的技术.
- 评估了12个基准数据集的模型,涵盖了各种分子性质.
- 比较性能指标以确定不同图表表示的有效性.
主要成果:
- 基于碎片的方法,特别是PharmHGT,显著提高了模型性能和可解释性.
- 这些改进在低数据制度中最显著.
- 基于碎片的分子图表表示并不总是优于使用大型数据集的传统方法.
结论:
- 碎片集成在GNN中显示出对人工智能增强药物设计的重大前景.
- PharmHGT表现出卓越的性能和可解释性,特别是在有限的数据下.
- 基于碎片和传统方法之间的选择取决于数据的可用性和培训规模.
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