Dataset Information

Proximity Graph Networks: Predicting Ligand Affinity with Message Passing Neural Networks.

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.

SUBMITTER: Gale-Day ZJ

PROVIDER: S-EPMC11267574 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature

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Publications

Proximity Graph Networks: Predicting Ligand Affinity with Message Passing Neural Networks.

Gale-Day Zachary J ZJ Shub Laura L Chuang Kangway V KV Keiser Michael J MJ

Journal of chemical information and modeling 20240702 14

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 benchma ...[more]

PMID: 38953560

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Dataset Information

Proximity Graph Networks: Predicting Ligand Affinity with Message Passing Neural Networks.

Publications

Proximity Graph Networks: Predicting Ligand Affinity with Message Passing Neural Networks.

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