ArticleIEEE/ACM transactions on computational biology and bioinformatics
Deep Learning in Drug Design: Protein-Ligand Binding Affinity Prediction.
Article in IEEE/ACM transactions on computational biology and bioinformatics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Revolutionizing drug discovery from natural products: The roles of artificial intelligence and multi-omics in accelerating innovation.Acta pharmaceutica Sinica. B · 2026Article
- DualPG-DTA: A Large Language Model-Powered Graph Neural Network Framework for Enhanced Drug-Target Affinity Prediction and Discovery of Novel CDK9 Inhibitors Exhibiting In Vivo Anti-Leukemia Activity.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A virtual screening and molecular dynamics approach in search of novel antibiotic chemotypes.PloS one · 2026Article
- Calculating Enzyme Inhibition with Random Forests.Methods in molecular biology (Clifton, N.J.) · 2026Article
- AgentMol: Multi-Model AI System for Automatic Drug-Target Identification and Molecule Development.Methods and protocols · 2025Article
- A generative framework for enhancing drug target interaction prediction in drug discovery.Scientific reports · 2025Article
- Drug Resistance Predictions Based on a Directed Flag Transformer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Scoring protein-ligand binding structures through learning atomic graphs with inter-molecular adjacency.PLoS computational biology · 2025Article
- Detection of Putative Ligand Dissociation Pathways in Proteins Using Site-Identification by Ligand Competitive Saturation.Journal of chemical information and modeling · 2025Article
- Synthesis, structural studies, and inhibitory potential of selected sulfonamide analogues: insights from in silico and in vitro analyses.EXCLI journal · 2025Article
- Incorporating Water Molecules into Highly Accurate Binding Affinity Prediction for Proteins and Ligands.International journal of molecular sciences · 2024Article
- De novo drug design through gradient-based regularized search in information-theoretically controlled latent space.Journal of computer-aided molecular design · 2024Article
- PGBind: pocket-guided explicit attention learning for protein-ligand docking.Briefings in bioinformatics · 2024Article
- Multiscale topology-enabled structure-to-sequence transformer for protein-ligand interaction predictions.Nature machine intelligence · 2024Article
- CENsible: Interpretable Insights into Small-Molecule Binding with Context Explanation Networks.Journal of chemical information and modeling · 2024Article
- Geometry-complete perceptron networks for 3D molecular graphs.Bioinformatics (Oxford, England) · 2024Article
- Prediction of protein-ligand binding affinity via deep learning models.Briefings in bioinformatics · 2024Review
- Structure-based, deep-learning models for protein-ligand binding affinity prediction.Journal of cheminformatics · 2024Review
- CENsible: Interpretable Insights into Small-Molecule Binding with Context Explanation Networks.bioRxiv : the preprint server for biology · 2023Article
- Oral IRAK-4 Inhibitor CA-4948 Is Blood-Brain Barrier Penetrant and Has Single-Agent Activity against CNS Lymphoma and Melanoma Brain Metastases.Clinical cancer research : an official journal of the American Association for Cancer Research · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
Computational drug design relies on the calculation of binding strength between two biological counterparts especially a chemical compound, i.e., a ligand, and a protein. Predicting the affinity of protein-ligand binding with reasonable accuracy is crucial for drug discovery, and enables the optimization of compounds to achieve better interaction with their target protein. In this paper, we propose a data-driven framework named DeepAtom to accurately predict the protein-ligand binding affinity. With 3D Convolutional Neural Network (3D-CNN) architecture, DeepAtom could automatically extract binding related atomic interaction patterns from the voxelized complex structure. Compared with the other CNN based approaches, our light-weight model design effectively improves the model representational capacity, even with the limited available training data. We carried out validation experiments on the PDBbind v.2016 benchmark and the independent Astex Diverse Set. We demonstrate that the less feature engineering dependent DeepAtom approach consistently outperforms the other baseline scoring methods. We also compile and propose a new benchmark dataset to further improve the model performances. With the new dataset as training input, DeepAtom achieves Pearson's R=0.83 and RMSE=1.23 pK units on the PDBbind v.2016 core set. The promising results demonstrate that DeepAtom models can be potentially adopted in computational drug development protocols such as molecular docking and virtual screening.
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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.