ArticleJournal of chemical information and modeling2024
Proximity Graph Networks: Predicting Ligand Affinity with Message Passing Neural Networks.
Article in Journal of chemical information and modeling, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Structure-guided compound prioritization strategy for virtual screening identifies putative binders for the nuclear receptor LRH-1.bioRxiv : the preprint server for biology · 2026Article
- MIC: A deep learning tool for assigning ions and waters in cryo-EM and crystal structures.Nature communications · 2025Article
- A new strategy for Cas protein recognition based on graph neural networks and SMILES encoding.Scientific reports · 2025Article
- Retrieval Augmented Docking Using Hierarchical Navigable Small Worlds.Journal of chemical information and modeling · 2024Article
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Authors and funding
4 authors.
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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.
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