ArticleScientific reports2022
Decoding the protein-ligand interactions using parallel graph neural networks.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Building Explainable Graph Neural Network by Sparse Learning for the Drug-Protein Binding Prediction.Journal of computational biology : a journal of computational molecular cell biology · 2025Article
- Natural Language Processing Methods for the Study of Protein-Ligand Interactions.Journal of chemical information and modeling · 2025Review
- Improved Prediction of Ligand-Protein Binding Affinities by Meta-modeling.Journal of chemical information and modeling · 2024Article
- Interformer: an interaction-aware model for protein-ligand docking and affinity prediction.Nature communications · 2024Article
- CACTUS: Chemistry Agent Connecting Tool Usage to Science.ACS omega · 2024Article
- Article
- Prediction of mutation-induced protein stability changes based on the geometric representations learned by a self-supervised method.BMC bioinformatics · 2024Article
- Proximity Graph Networks: Predicting Ligand Affinity with Message Passing Neural Networks.Journal of chemical information and modeling · 2024Article
- GATSol, an enhanced predictor of protein solubility through the synergy of 3D structure graph and large language modeling.BMC bioinformatics · 2024Article
- Antibody design using deep learning: from sequence and structure design to affinity maturation.Briefings in bioinformatics · 2024Review
- Directional ΔJournal of chemical information and modeling · 2024Article
- Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody-Antigen Interactions.Bioengineering (Basel, Switzerland) · 2024Review
- How Deep Learning in Antiviral Molecular Profiling Identified Anti-SARS-CoV-2 Inhibitors.Biomedicines · 2023Article
- Graph identification of proteins in tomograms (GRIP-Tomo).Protein science : a publication of the Protein Society · 2023Article
- Prediction of the tetramer protein complex interaction based on CNN and SVM.Frontiers in genetics · 2023Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein-ligand interactions (PLIs) are essential for biochemical functionality and their identification is crucial for estimating biophysical properties for rational therapeutic design. Currently, experimental characterization of these properties is the most accurate method, however, this is very time-consuming and labor-intensive. A number of computational methods have been developed in this context but most of the existing PLI prediction heavily depends on 2D protein sequence data. Here, we present a novel parallel graph neural network (GNN) to integrate knowledge representation and reasoning for PLI prediction to perform deep learning guided by expert knowledge and informed by 3D structural data. We develop two distinct GNN architectures: [Formula: see text] is the base implementation that employs distinct featurization to enhance domain-awareness, while [Formula: see text] is a novel implementation that can predict with no prior knowledge of the intermolecular interactions. The comprehensive evaluation demonstrated that GNN can successfully capture the binary interactions between ligand and protein's 3D structure with 0.979 test accuracy for [Formula: see text] and 0.958 for [Formula: see text] for predicting activity of a protein-ligand complex. These models are further adapted for regression tasks to predict experimental binding affinities and [Formula: see text] crucial for compound's potency and efficacy. We achieve a Pearson correlation coefficient of 0.66 and 0.65 on experimental affinity and 0.50 and 0.51 on [Formula: see text] with [Formula: see text] and [Formula: see text], respectively, outperforming similar 2D sequence based models. Our method can serve as an interpretable and explainable artificial intelligence (AI) tool for predicted activity, potency, and biophysical properties of lead candidates. To this end, we show the utility of [Formula: see text] on SARS-Cov-2 protein targets by screening a large compound library and comparing the prediction with the experimentally measured data.
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