AIMC Topic: Protein Binding

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ESPDHot: An Effective Machine Learning-Based Approach for Predicting Protein-DNA Interaction Hotspots.

Journal of chemical information and modeling Apr 8, 2024
Protein-DNA interactions are pivotal to various cellular processes. Precise identification of the hotspot residues for protein-DNA interactions holds great significance for revealing the intricate mechanisms in protein-DNA recognition and for providi...
Algorithms Protein Binding Neural Networks, Computer Machine Learning DNA DNA-Binding Proteins Thermodynamics Support Vector Machine Proteins
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Discovery of Covalent Lead Compounds Targeting 3CL Protease with a Lateral Interactions Spiking Neural Network.

Journal of chemical information and modeling Mar 23, 2024
Covalent drugs exhibit advantages in that noncovalent drugs cannot match, and covalent docking is an important method for screening covalent lead compounds. However, it is difficult for covalent docking to screen covalent compounds on a large scale b...
Deep Learning Antiviral Agents Molecular Docking Simulation Protein Binding Protease Inhibitors Humans COVID-19 COVID-19 Drug Treatment Coronavirus 3C Proteases Drug Discovery Neural Networks, Computer SARS-CoV-2
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EPDRNA: A Model for Identifying DNA-RNA Binding Sites in Disease-Related Proteins.

The protein journal Mar 16, 2024
Protein-DNA and protein-RNA interactions are involved in many biological processes and regulate many cellular functions. Moreover, they are related to many human diseases. To understand the molecular mechanism of protein-DNA binding and protein-RNA b...
RNA Binding Sites DNA DNA-Binding Proteins Algorithms Humans Protein Binding RNA-Binding Proteins Computational Biology Machine Learning Databases, Protein
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Directional Δ Neural Network (DrΔ-Net): A Modular Neural Network Approach to Binding Free Energy Prediction.

Journal of chemical information and modeling Mar 12, 2024
The protein-ligand binding free energy is a central quantity in structure-based computational drug discovery efforts. Although popular alchemical methods provide sound statistical means of computing the binding free energy of a large breadth of syste...
Entropy Neural Networks, Computer Protein Binding Proteins Ligands
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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...
Humans Algorithms Animals Caenorhabditis elegans Protein Binding Proteins Drug Design Deep Learning
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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 ...
Protein Binding Proteins Deep Learning Mutation Nucleic Acids
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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...
Humans Protein Binding Proteins Neural Networks, Computer
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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...
Molecular Dynamics Simulation Protein Binding Peptides beta Catenin NF-kappa B Deep Learning
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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...
Ligands Protein Binding Neural Networks, Computer Proteins 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...
Machine Learning Ligands Protein Binding Proteins
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