1. Proximity Graph Networks: Predicting Ligand Affinity with Message Passing Neural Networks.
- Author
-
Gale-Day, Zachary, Shub, Laura, Chuang, Kangway, and Keiser, Michael
- Subjects
Ligands ,Neural Networks ,Computer ,Proteins ,Molecular Docking Simulation ,Protein Binding - Abstract
Message passing neural networks (MPNNs) on molecular graphs generate continuous and differentiable encodings of small molecules with state-of-the-art performance on protein-ligand complex scoring tasks. Here, we describe the proximity graph network (PGN) package, an open-source toolkit that constructs ligand-receptor graphs based on atom proximity and allows users to rapidly apply and evaluate MPNN architectures for a broad range of tasks. We demonstrate the utility of PGN by introducing benchmarks for affinity and docking score prediction tasks. Graph networks generalize better than fingerprint-based models and perform strongly for the docking score prediction task. Overall, MPNNs with proximity graph data structures augment the prediction of ligand-receptor complex properties when ligand-receptor data are available.
- Published
- 2024