The D3R Grand Challenge 4 provided a brilliant opportunity to test macrocyclic docking protocols on a diverse high-quality experimental data. We participated in both pose and affinity prediction exercises. Overall, we aimed to use an automated structure-based docking pipeline built around a set of tools developed in our team. This exercise again demonstrated a crucial importance of the correct local ligand geometry for the overall success of docking. Starting from the second part of the pose prediction stage, we developed a stable pipeline for sampling macrocycle conformers. This resulted in the subangstrom average precision of our pose predictions. In the affinity prediction exercise we obtained average results. However, we could improve these when using docking poses submitted by the best predictors. Our docking tools including the Convex-PL scoring function are available at https://team.inria.fr/nano-d/software/.

中文翻译：

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在D3R Grand Challenge 4中使用Convex-PL，AutoDock Vina和RDKit对接刚性宏周期。

D3R大挑战4提供了绝妙的机会，可以在各种高质量的实验数据上测试大环对接方案。我们参加了姿势和亲和力预测练习。总体而言，我们的目标是使用围绕我们团队开发的一组工具构建的基于结构的自动化对接管道。这项练习再次证明了正确的局部配体几何形状对对接的总体成功至关重要。从姿势预测阶段的第二部分开始，我们开发了一个稳定的管道来采样大环顺应剂。这导致了我们的姿势预测的亚埃级平均精度。在亲和力预测练习中，我们获得了平均结果。但是，当使用最佳预测变量提交的对接姿势时，我们可以改善这些问题。