Protein-ligand binding affinity is a key pharmacodynamic endpoint in drug discovery. Sole reliance on experimental design, make, and test cycles is costly and time consuming, providing an opportunity for computational methods to assist. Herein, we present results comparing random forest and feed-forward neural network proteochemometric models for their ability to predict pIC50 measurements for held out generic Bemis-Murcko scaffolds. In addition, we assess the ability of conformal prediction to provide calibrated prediction intervals in both a retrospective and semi-prospective test using the recently released Grand Challenge 4 data set as an external test set. In total, random forest and deep neural network proteochemometric models show quality retrospective performance but suffer in the semi-prospective setting. However, the conformal predictor prediction intervals prove to be well-calibrated both retrospectively and semi-prospectively showing that they can be used to guide hit discovery and lead optimization campaigns.

蛋白质-配体结合亲和力是药物发现中的关键药效​​学终点。仅仅依赖实验设计、制造和测试周期既昂贵又耗时，这为计算方法提供了协助的机会。在此，我们提出了比较随机森林和前馈神经网络蛋白质化学计量模型的结果，以了解它们预测通用 Bemis-Murcko 支架的 pIC50 测量值的能力。此外，我们使用最近发布的 Grand Challenge 4 数据集作为外部测试集，评估了保形预测在回顾性和半前瞻性测试中提供校准预测区间的能力。总的来说，随机森林和深度神经网络蛋白质化学计量模型显示出高质量的回顾性性能，但在半前瞻性环境中受到影响。然而，事实证明，保形预测器的预测区间在回顾性和半前瞻性方面都得到了很好的校准，表明它们可以用来指导命中发现和先导优化活动。