ArticlePloS one2024
Improving structure-based protein-ligand affinity prediction by graph representation learning and ensemble learning.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Robustness of Protein-Ligand Binding Affinity Prediction Models to Docked and Predicted Structures.Journal of chemical information and modeling · 2026Article
- Critical Artifacts Improve Reproducibility of Protein-Ligand Binding Affinity Prediction Models on CASF-2016.Journal of chemical information and modeling · 2026Article
- Structural Knowledge Is What Matters in Protein-Ligand Binding Affinity Prediction.Molecules (Basel, Switzerland) · 2026Article
- GNNSeq: A Sequence-Based Graph Neural Network for Predicting Protein-Ligand Binding Affinity.Pharmaceuticals (Basel, Switzerland) · 2025Article
- Edge-enhanced interaction graph network for protein-ligand binding affinity prediction.PloS one · 2025Article
- Predicting Inhibition of CDK2 with SAnDReS: The Application of Machine Learning to Navigate the Scoring Function Space.Current medicinal chemistry · 2025Review
- Incorporating Water Molecules into Highly Accurate Binding Affinity Prediction for Proteins and Ligands.International journal of molecular sciences · 2024Article
- Advances in Protein-Ligand Binding Affinity Prediction via Deep Learning: A Comprehensive Study of Datasets, Data Preprocessing Techniques, and Model Architectures.Current drug targets · 2024Review
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Authors and funding
1 author.
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Abstract
Predicting protein-ligand binding affinity presents a viable solution for accelerating the discovery of new lead compounds. The recent widespread application of machine learning approaches, especially graph neural networks, has brought new advancements in this field. However, some existing structure-based methods treat protein macromolecules and ligand small molecules in the same way and ignore the data heterogeneity, potentially leading to incomplete exploration of the biochemical information of ligands. In this work, we propose LGN, a graph neural network-based fusion model with extra ligand feature extraction to effectively capture local features and global features within the protein-ligand complex, and make use of interaction fingerprints. By combining the ligand-based features and interaction fingerprints, LGN achieves Pearson correlation coefficients of up to 0.842 on the PDBbind 2016 core set, compared to 0.807 when using the features of complex graphs alone. Finally, we verify the rationalization and generalization of our model through comprehensive experiments. We also compare our model with state-of-the-art baseline methods, which validates the superiority of our model. To reduce the impact of data similarity, we increase the robustness of the model by incorporating ensemble learning.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.