近距离图形网络:预测与传递信息的神经网络的连接物亲和力
Zachary J Gale-Day1,2, Laura Shub1,3,2,4, Kangway V Chuang1,3,2,4
1Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, California 94158, United States.
Journal of chemical information and modeling
|July 2, 2024
概括
使用近距离图形网络 (PGN) 包的传递信息的神经网络 (MPNNs) 提高了蛋白质-连接体相互作用的预测. 这个工具包增强了分子图分析,用于药物发现和计算化学任务.
科学领域:
- 计算化学是一种计算化学.
- 机器学习在药物发现中的作用
- 结构生物信息学 结构生物信息学
背景情况:
- 传递信息的神经网络 (MPNN) 擅长编码分子图表,用于诸如蛋白质-连接体复合体评分等任务.
- 现有的方法可能无法充分利用相互作用分子之间的结构信息.
研究的目的:
- 介绍近距离图形网络 (PGN) 包,这是一个开源工具包,用于将MPNN应用于联体受体相互作用.
- 使用PGN建立亲和力和对接分数预测的基准.
- 用近距离图来评估MPNNs的性能.
主要方法:
- 建立基于原子近距离的连接体-受体图.
- 将MPNN架构应用于这些图表.
- 开发用于亲和度和对接分数预测任务的基准.
主要成果:
- 使用近距离图数据结构的MPNNs表现出强的性能,特别是在对接分数预测方面.
- 与传统的基于指纹的模型相比,图形网络显示出更好的概括能力.
- 该PGN包促进了MPNNs的快速应用和评估.
结论:
- 与近距离图表表示相结合的MPNNs显著提高了对联体受体复合体属性的预测.
- PGN工具包为计算化学和药物发现研究人员提供了宝贵的资源.
- 接近图数据结构是有效的增强预测模型,当交互数据可用时.
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