DecoyDB: A Dataset for Graph Contrastive Learning in Protein-Ligand Binding Affinity Prediction

Yupu Zhang, Zelin Xu, Tingsong Xiao, Gustavo Seabra, Yanjun Li, Chenglong Li, Zhe Jiang

Keywords: Dataset and benchmarking, graph contrastive learning, drug discovery, binding affinity prediction, DecoyDB

Abstract: Predicting the binding affinity of protein-ligand complexes plays a vital role in drug discovery. Unfortunately, progress has been hindered by the lack of large-scale and high-quality binding affinity labels. The widely used PDBbind dataset has fewer than 20K labeled complexes. Self-supervised learning, especially graph contrastive learning (GCL), provides a unique opportunity to break the barrier by pretraining graph neural network models based on vast unlabeled complexes and fine-tuning the models on much fewer labeled complexes. However, the problem faces unique challenges, including a lack of a comprehensive unlabeled dataset with well-defined positive/negative complex pairs and the need to design GCL algorithms that incorporate the unique characteristics of such data. To fill the gap, we propose DecoyDB, a large-scale, structure-aware dataset specifically designed for self-supervised GCL on protein–ligand complexes. DecoyDB consists of high-resolution ground truth complexes and diverse decoy structures with computationally generated binding poses that range from realistic to suboptimal. Each decoy is annotated with a Root Mean Square Deviation (RMSD) from the native pose. We further design a customized GCL framework to pretrain graph neural networks based on DecoyDB and fine-tune the models with labels from PDBbind. Extensive experiments confirm that models pretrained with DecoyDB achieve superior accuracy, sample efficiency, and generalizability.

Dataset URL: https://huggingface.co/datasets/jiangteam/DecoyDB

Code URL: https://github.com/spatialdatasciencegroup/DecoyDB

Primary Area: AL/ML Datasets & Benchmarks for life sciences (e.g. climate, health, life sciences, physics, social sciences)

Submission Number: 2222