Improving generalisability of 3D binding affinity models in low data regimes

TL;DR

This paper investigates the generalisability of 3D binding affinity models in low data settings, proposing new dataset splits and training strategies to improve model performance, especially for GNN architectures.

Contribution

Introduces a novel dataset split minimizing similarity leakage and three pre-training techniques to enhance GNN performance in low data regimes.

Findings

3D global models outperform local models in low data regimes

Supervised pre-training with quantum data improves GNN accuracy

Explicit hydrogen modeling benefits GNN performance

Abstract

Predicting protein-ligand binding affinity is an essential part of computer-aided drug design. However, generalisable and performant global binding affinity models remain elusive, particularly in low data regimes. Despite the evolution of model architectures, current benchmarks are not well-suited to probe the generalisability of 3D binding affinity models. Furthermore, 3D global architectures such as GNNs have not lived up to performance expectations. To investigate these issues, we introduce a novel split of the PDBBind dataset, minimizing similarity leakage between train and test sets and allowing for a fair and direct comparison between various model architectures. On this low similarity split, we demonstrate that, in general, 3D global models are superior to protein-specific local models in low data regimes. We also demonstrate that the performance of GNNs benefits from three novel contributions: supervised pre-training via quantum mechanical data, unsupervised pre-training via small molecule diffusion, and explicitly modeling hydrogen atoms in the input graph. We believe that this work introduces promising new approaches to unlock the potential of GNN architectures for binding affinity modelling.