ArticleJournal of chemical information and modeling2024
From Proteins to Ligands: Decoding Deep Learning Methods for Binding Affinity Prediction.
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 13 papers.
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Who cites it
13 citing papers in PubMed, 21 citations in OpenAlex.
- Evaluation of deep learning architectures for predicting ligand interactions with neurologically relevant GPCRs.Journal of cheminformatics · 2026Article
- Molecular embedding-based algorithm selection in protein-ligand docking.Journal of cheminformatics · 2026Article
- Multi-View Biomedical Foundation Models for Molecule-Target and Property Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Leak Proof PDBBind: A Reorganized Data Set of Protein-Ligand Complexes for More Generalizable Binding Affinity Prediction.The journal of physical chemistry. B · 2026Article
- Deep learning model for activity cliffs prediction: a comprehensive approach to protein kinase inhibitors.Journal of computer-aided molecular design · 2025Article
- Assessing Computational Strategies for the Evaluation of Antibody Binding Affinities.Journal of chemical theory and computation · 2025Article
- All That Glitters Is Not Gold: Importance of Rigorous Evaluation of Proteochemometric Models.Journal of chemical information and modeling · 2025Article
- PLAIG: Protein-Ligand Binding Affinity Prediction Using a Novel Interaction-Based Graph Neural Network Framework.ACS bio & med chem Au · 2025Article
- GNNSeq: A Sequence-Based Graph Neural Network for Predicting Protein-Ligand Binding Affinity.Pharmaceuticals (Basel, Switzerland) · 2025Article
- Incorporating Water Molecules into Highly Accurate Binding Affinity Prediction for Proteins and Ligands.International journal of molecular sciences · 2024Article
- Lessons learned during the journey of data: from experiment to model for predicting kinase affinity, selectivity, polypharmacology, and resistance.bioRxiv : the preprint server for biology · 2024Article
- Do Molecular Fingerprints Identify Diverse Active Drugs in Large-Scale Virtual Screening? (No).Pharmaceuticals (Basel, Switzerland) · 2024Article
- Benchmarking Active Learning Protocols for Ligand-Binding Affinity Prediction.Journal of chemical information and modeling · 2024Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate in silico prediction of protein-ligand binding affinity is important in the early stages of drug discovery. Deep learning-based methods exist but have yet to overtake more conventional methods such as giga-docking largely due to their lack of generalizability. To improve generalizability, we need to understand what these models learn from input protein and ligand data. We systematically investigated a sequence-based deep learning framework to assess the impact of protein and ligand encodings on predicting binding affinities for commonly used kinase data sets. The role of proteins is studied using convolutional neural network-based encodings obtained from sequences and graph neural network-based encodings enriched with structural information from contact maps. Ligand-based encodings are generated from graph-neural networks. We test different ligand perturbations by randomizing node and edge properties. For proteins, we make use of 3 different protein contact generation methods (AlphaFold2, Pconsc4, and ESM-1b) and compare these with a random control. Our investigation shows that protein encodings do not substantially impact the binding predictions, with no statistically significant difference in binding affinity for KIBA in the investigated metrics (concordance index, Pearson's R Spearman's Rank, and RMSE). Significant differences are seen for ligand encodings with random ligands and random ligand node properties, suggesting a much bigger reliance on ligand data for the learning tasks. Using different ways to combine protein and ligand encodings did not show a significant change in performance.
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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.