星球:一个多目标图形神经网络模型,用于蛋白质 - 配体结合的亲和力预测
Xiangying Zhang1, Haotian Gao1, Haojie Wang1
1Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, People's Republic of China.
Journal of chemical information and modeling
|June 15, 2023
概括
我们开发了PLANET,这是一个图形神经网络,用于预测蛋白质-连接体结合亲和力. 这种高效的模型在虚拟查和药物设计任务中表现出强的表现.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 在生物信息学中的机器学习.
背景情况:
- 预测蛋白质 - 配体结合亲和力对于药物设计至关重要.
- 现有的深度学习模型通常需要3D复杂的结构,并且仅专注于亲和力预测.
研究的目的:
- 开发一个高效的图形神经网络模型,PLANET,用于预测蛋白质-连接体结合亲和力.
- 通过结合多目标学习和多样化的培训数据来改进现有方法.
主要方法:
- 开发了PLANET,一个图形神经网络模型.
- 输入包括3D绑定口袋图和2D连接体结构.
- 使用多目标方法进行训练,具有绑定亲和力,联系地图和距离矩阵任务.
- 集成的PDBbind数据和非绑定诱用于培训.
主要成果:
- 在CASF-2016基准中,PLANET的得分能力与顶级深度学习模型相美.
- 与其他模型相比,在DUD-E的虚拟选中表现出卓越的性能.
- 显示了与LIT-PCBA上的Glide相似的准确性,计算时间大大缩短.
结论:
- PLANET提供了准确性和效率的平衡,用于绑定亲和力预测.
- 该模型显示了作为药物发现中大规模虚拟查的有价值工具的潜力.
- 它的多目标培训和输入灵活性有助于其强大的表现.
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