Drug Design Data Resource (D3R) Grand Challenge 4 (GC4) offered a unique opportunity for designing and testing novel methodology for accurate docking and affinity prediction of ligands in an open and blinded manner. We participated in the beta-secretase 1 (BACE) Subchallenge which is comprised of cross-docking and redocking of 20 macrocyclic ligands to BACE and predicting binding affinity for 154 macrocyclic ligands. For this challenge, we developed machine learning models trained specifically on BACE. We developed a deep neural network (DNN) model that used a combination of both structure and ligand-based features that outperformed simpler machine learning models. According to the results released by D3R, we achieved a Spearman's rank correlation coefficient of 0.43(7) for predicting the affinity of 154 ligands. We describe the formulation of our machine learning strategy in detail. We compared the performance of DNN with linear regression, random forest, and support vector machines using ligand-based, structure-based, and combining both ligand and structure-based features. We compared different structures for our DNN and found that performance was highly dependent on fine optimization of the L2 regularization hyperparameter, alpha. We also developed a novel metric of ligand three-dimensional similarity inspired by crystallographic difference density maps to match ligands without crystal structures to similar ligands with known crystal structures. This report demonstrates that detailed parameterization, careful data training and implementation, and extensive feature analysis are necessary to obtain strong performance with more complex machine learning methods. Post hoc analysis shows that scoring functions based only on ligand features are competitive with those also using structural features. Our DNN approach tied for fifth in predicting BACE-ligand binding affinities.

中文翻译：

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D3R Grand Challenge 4中BACE抑制剂的深度神经网络亲和模型

药物设计数据资源（D3R）挑战4（GC4）为设计和测试新颖的方法提供了独特的机会，从而可以以开放和盲目的方式对配体进行精确对接和亲和力预测。我们参加了β-分泌酶1（BACE）挑战赛，该挑战赛包括20个大环配体与BACE的对接和重新对接以及预测对154个大环配体的结合亲和力。为了应对这一挑战，我们开发了经过BACE专门培训的机器学习模型。我们开发了一种深度神经网络（DNN）模型，该模型结合了结构和基于配体的功能，其性能优于简单的机器学习模型。根据D3R发布的结果，我们获得的Spearman秩相关系数为0.43（7），可预测154个配体的亲和力。我们详细描述了我们的机器学习策略的制定。我们将DNN的性能与线性回归，随机森林和支持向量机（使用基于配体，基于结构以及结合配体和基于结构的特征）进行了比较。我们比较了DNN的不同结构，发现性能高度依赖于L2正则化超参数alpha的精细优化。我们还开发了一种新的度量配体三维相似性的方法，该度量方法受到晶体学差异密度图的启发，以将没有晶体结构的配体与具有已知晶体结构的相似配体进行匹配。该报告表明，详细的参数设置，认真的数据培训和实施，为了使用更复杂的机器学习方法获得强大的性能，必须进行大量的特征分析。事后分析表明，仅基于配体特征的评分功能与那些也使用结构特征的评分功能竞争。我们的DNN方法在预测BACE-配体结合亲和力方面并列第五。