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Protein Binding

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GraphsformerCPI: Graph Transformer for Compound-Protein Interaction Prediction.

Interdisciplinary sciences, computational life sciences Mar 8, 2024
Accurately predicting compound-protein interactions (CPI) is a critical task in computer-aided drug design. In recent years, the exponential growth of compound activity and biomedical data has highlighted the need for efficient and interpretable pred...
Protein Binding Proteins Drug Design Deep Learning Caenorhabditis elegans Humans Algorithms Animals
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DeePNAP: A Deep Learning Method to Predict Protein-Nucleic Acid Binding Affinity from Their Sequences.

Journal of chemical information and modeling Mar 8, 2024
Predicting the protein-nucleic acid (PNA) binding affinity solely from their sequences is of paramount importance for the experimental design and analysis of PNA interactions (PNAIs). A large number of currently developed models for binding affinity ...
Nucleic Acids Proteins Mutation Deep Learning Protein Binding
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Heterogeneous sampled subgraph neural networks with knowledge distillation to enhance double-blind compound-protein interaction prediction.

Structure (London, England : 1993) Mar 5, 2024
Identifying binding compounds against a target protein is crucial for large-scale virtual screening in drug development. Recently, network-based methods have been developed for compound-protein interaction (CPI) prediction. However, they are difficul...
Neural Networks, Computer Protein Binding Proteins Humans
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Repurposing FDA-approved drugs as NLRP3 inhibitors against inflammatory diseases: machine learning and molecular simulation approaches.

Journal of biomolecular structure & dynamics Feb 24, 2024
Activation of NLRP3 (NOD-like receptor family, pyrin domain-containing protein 3) has been associated with multiple chronic pathologies, including diabetes, atherosclerosis, and rheumatoid arthritis. Moreover, histone deacetylases (HDACs), specifical...
Inflammation United States Food and Drug Administration Molecular Docking Simulation United States Drug Approval Drug Repositioning Protein Binding NLR Family, Pyrin Domain-Containing 3 Protein Molecular Dynamics Simulation Humans Machine Learning Inflammasomes
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Design of target specific peptide inhibitors using generative deep learning and molecular dynamics simulations.

Nature communications Feb 21, 2024
We introduce a computational approach for the design of target-specific peptides. Our method integrates a Gated Recurrent Unit-based Variational Autoencoder with Rosetta FlexPepDock for peptide sequence generation and binding affinity assessment. Sub...
Peptides Deep Learning Protein Binding NF-kappa B Molecular Dynamics Simulation beta Catenin
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graphLambda: Fusion Graph Neural Networks for Binding Affinity Prediction.

Journal of chemical information and modeling Feb 17, 2024
Predicting the binding affinity of protein-ligand complexes is crucial for computer-aided drug discovery (CADD) and the identification of potential drug candidates. The deep learning-based scoring functions have emerged as promising predictors of bin...
Protein Binding Proteins Ligands Neural Networks, Computer Drug Discovery
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An ensemble-based approach to estimate confidence of predicted protein-ligand binding affinity values.

Molecular informatics Feb 15, 2024
When designing a machine learning-based scoring function, we access a limited number of protein-ligand complexes with experimentally determined binding affinity values, representing only a fraction of all possible protein-ligand complexes. Consequent...
Proteins Ligands Machine Learning Protein Binding
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Machine Learning Methods as a Cost-Effective Alternative to Physics-Based Binding Free Energy Calculations.

Molecules (Basel, Switzerland) Feb 13, 2024
The rank ordering of ligands remains one of the most attractive challenges in drug discovery. While physics-based in silico binding affinity methods dominate the field, they still have problems, which largely revolve around forcefield accuracy and sa...
Thermodynamics Physics Protein Binding Cost-Benefit Analysis Proteins Machine Learning Binding Sites Ligands Retrospective Studies
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Predicting DNA structure using a deep learning method.

Nature communications Feb 9, 2024
Understanding the mechanisms of protein-DNA binding is critical in comprehending gene regulation. Three-dimensional DNA structure, also described as DNA shape, plays a key role in these mechanisms. In this study, we present a deep learning-based meth...
Protein Binding Proteins DNA Machine Learning Deep Learning
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A deep learning-based theoretical protocol to identify potentially isoform-selective PI3Kα inhibitors.

Molecular diversity Feb 2, 2024
Phosphoinositide 3-kinase alpha (PI3Kα) is one of the most frequently dysregulated kinases known for their pivotal role in many oncogenic diseases. While the side effects linked to existing drugs against PI3Kα-induced cancers provide an avenue for fu...
Deep Learning Protein Binding Molecular Docking Simulation Drug Design Ligands Protein Kinase Inhibitors Phosphoinositide-3 Kinase Inhibitors Class I Phosphatidylinositol 3-Kinases Phosphatidylinositol 3-Kinases Humans
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