集体几何深度学习水溶性
Mohammad M Ghahremanpour1, Anastasia Saar1, Julian Tirado-Rives1
1Department of Chemistry, Yale University New Haven, Connecticut 06520-8107, United States.
Journal of chemical information and modeling
|November 22, 2023
概括
几何深度学习模型SolNet-GCN和SolNet-GAT准确地预测了分子水溶性. 这些图形神经网络显著优于现有方法,有助于药物候选药物的药理动力学改进.
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
- 分子属性预测的预测
背景情况:
- 水溶性对于药物的药理动力学至关重要.
- 预测溶解度有助于开发有效的候选药物.
- 大数据和深度学习为分子性质预测提供了新的途径.
研究的目的:
- 开发和评估用于预测水溶性的几何深度学习模型.
- 为了比较基于光谱和空间卷积的图形神经网络的性能.
- 评估模型在药物发现工作流程中的实用性.
主要方法:
- 构建了两个图形神经网络集:SolNet-GCN (光谱卷积) 和SolNet-GAT (空间卷积).
- 模型进行了预训练,并在207个分子的验证集上与现有的神经网络进行了基准测试.
- 使用根平均平方误差 (RMSE) 和皮尔森相关系数 (r2) 评估性能.
主要成果:
- 索尔网-GCN和索尔网-GAT显著超过了现有的神经网络.
- 在训练和验证集上,SolNet-GCN实现了0.53和0.72的RMSE和0.95和0.75的r2分别在训练和验证集上达到最佳性能.
- 模型排名与基于量子力学的热力学循环方法有很好的相关性.
结论:
- 几何深度学习模型,特别是SolNet-GCN,在预测水溶性方面表现出高准确性.
- 这些模型可以有效地帮助药物候选药物的药理学优化.
- 结合与结合和平面性相关的原子属性,可以进一步提高可溶性预测的准确性.
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