Three-Dimensional Convolutional Neural Networks and a Cross-Docked Data Set for Structure-Based Drug Design

TLDR

Predicting protein‑ligand binding affinity remains a key challenge in drug discovery, and while machine learning has advanced the field, existing evaluation practices are overly optimistic and no standard large‑scale dataset exists for fair model comparison. This study introduces CrossDocked2020, a 22.5‑million‑pose dataset of ligands docked into similar PDB binding pockets, and evaluates grid‑based convolutional neural networks on it. The authors assess how training/test partitioning, inclusion of lower‑quality data, and training with docked poses affect model performance, demonstrating that these factors influence affinity predictions and pose sensitivity. An ensemble of five densely connected CNNs achieves a root‑mean‑square error of 1.42 and a high Pearson correlation, outperforming previous approaches.

Abstract

One of the main challenges in drug discovery is predicting protein-ligand binding affinity. Recently, machine learning approaches have made substantial progress on this task. However, current methods of model evaluation are overly optimistic in measuring generalization to new targets, and there does not exist a standard data set of sufficient size to compare performance between models. We present a new data set for structure-based machine learning, the CrossDocked2020 set, with 22.5 million poses of ligands docked into multiple similar binding pockets across the Protein Data Bank, and perform a comprehensive evaluation of grid-based convolutional neural network (CNN) models on this data set. We also demonstrate how the partitioning of the training data and test data can impact the results of models trained with the PDBbind data set, how performance improves by adding more lower-quality training data, and how training with docked poses imparts pose sensitivity to the predicted affinity of a complex. Our best performing model, an ensemble of five densely connected CNNs, achieves a root mean squared error of 1.42 and Pearson