Accurately predicting protein–ligand binding affinities is an important problem in computational chemistry since it can substantially accelerate drug discovery for virtual screening and lead optimization. We propose here a fast machine-learning approach for predicting binding affinities using state-of-the-art 3D-convolutional neural networks and compare this approach to other machine-learning and scoring methods using several diverse data sets. The results for the standard PDBbind (v.2016) core test-set are state-of-the-art with a Pearson’s correlation coefficient of 0.82 and a RMSE of 1.27 in pK units between experimental and predicted affinity, but accuracy is still very sensitive to the specific protein used. K_DEEP is made available via PlayMolecule.org for users to test easily their own protein–ligand complexes, with each prediction taking a fraction of a second. We believe that the speed, performance, and ease of use of K_DEEP makes it already an attractive scoring function for modern computational chemistry pipelines.

中文翻译：

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K _DEEP：通过3D卷积神经网络预测蛋白质-配体的绝对结合亲和力

准确预测蛋白质与配体的结合亲和力是计算化学中的一个重要问题，因为它可以大大加快虚拟筛选和前导优化的药物发现。我们在这里提出一种使用最新的3D卷积神经网络预测绑定亲和力的快速机器学习方法，并将这种方法与使用几种不同数据集的其他机器学习和评分方法进行比较。标准PDBbind（v.2016）核心测试集的结果是最先进的，在实验亲和力与预测亲和力之间的p K单位中，皮尔逊相关系数为0.82，RMSE为1.27 ，但准确性仍然很高对使用的特定蛋白质敏感。ķ _DEEP可以通过PlayMolecule.org进行使用，以使用户可以轻松测试自己的蛋白质-配体复合物，每次预测只需花费一秒钟的时间。我们相信，K _DEEP的速度，性能和易用性使其已成为现代计算化学流水线的诱人得分函数。