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DeepGRID:使用GRID描述符进行BBB预测的深度学习.

Loriano Storchi1, Gabriele Cruciani2, Simon Cross3

  • 1Dipartimento di Farmacia, Università G. D'Annunzio, Via dei Vestini 31, 66100 Chieti, Italy.

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使用新型GRID衍生AI (GrAId) 描述符和卷积神经网络 (CNNs) 的深度学习模型准确地预测血脑屏障的透. 这种DeepGRID方法为药物设计提供了一个旋转,形状和对齐独立的方法.

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科学领域:

  • 计算化学是一种计算化学.
  • 化学信息学 化学信息学
  • 机器学习 机器学习

背景情况:

  • 血脑屏障 (BBB) 透对于中枢神经系统 (CNS) 药物开发至关重要.
  • 像VolSurf这样的传统方法使用GRID分子相互作用场 (MIF) 与部分最小平方 (PLS) 来建模BBB透.
  • 开发准确的BBB透预测模型仍然是一个挑战.

研究的目的:

  • 介绍DeepGRID,一种新的深度学习方法,将GRID衍生AI (GrAId) 描述符与卷积神经网络 (CNN) 结合起来.
  • 开发旋转,形状和对齐独立的模型来预测血脑屏障的透.
  • 将DeepGRID模型的性能与VolSurf与PLS和随机森林 (RF) 等传统方法进行比较.

主要方法:

  • 开发GRID衍生AI (GrAId) 描述符,这是GRID MIFs的修改.
  • 将GRAID描述符与CNN集成,以创建DeepGRID模型.
  • 应用DeepGRID用于与BBB透相关的回归和分类任务.
  • 与使用手工制作的VolSurf描述符和PLS/RF算法的模型进行比较.

主要成果:

  • DeepGRID和RF回归模型在预测BBB透方面表现出卓越的性能,化合物的百分比最高,在2倍的几何平均折叠误差内.
  • 对于较小数据集的分类任务,所有模型都表现良好,ROC AUC值约为0.9.
  • 在较大的数据集上,DeepGRID分类器获得了最高的AUC (0.87),超过了RF (0.84) 和原始VolSurf模型 (0.83).

结论:

  • DeepGRID 代表了使用深度学习对 BBB 透模式的重大进步.
  • 由于DeepGRID的旋转性,形状性和对齐性独立,使其在药物发现中的实用性得到提高.
  • 与现有的方法相比,DeepGRID为预测BBB透提供了一个强大而准确的替代方案.