AIMC Topic: Proteins

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When Simulations Meet Machine Learning: Redefining Molecular Docking for Protein-Glycosaminoglycan Systems.

Journal of computational chemistry Jun 30, 2025
Glycosaminoglycans (GAGs) are linear, negatively charged carbohydrates that modulate enzymatic activity in the extracellular matrix. Their high flexibility and specificity in protein-GAG interactions pose challenges for both experimental and computat...
Machine Learning Molecular Docking Simulation Glycosaminoglycans Ligands Binding Sites Proteins Molecular Dynamics Simulation
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DTBA-net: Drug-Target Binding Affinity prediction using feature selection in hybrid CNN model.

Journal of computer-aided molecular design Jun 16, 2025
In drug discovery, virtual screening and repositioning rely on accurate Drug-Target Binding Affinity (DTBA) prediction to develop effective therapies. However, DTBA prediction remains challenging due to limited annotated datasets, high-dimensional bi...
Neural Networks, Computer Algorithms Drug Discovery Humans Proteins Deep Learning Binding Sites Protein Binding
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PLPTP: A Motif-based Interpretable Deep Learning Framework Based on Protein Language Models for Peptide Toxicity Prediction.

Journal of molecular biology Jun 15, 2025
Peptide toxicity prediction holds significant importance in drug development and biotechnology, as accurately identifying toxic peptide sequences is crucial for designing safer peptide-based drugs. This study proposes a deep learning-based model for ...
Computational Biology Peptides Amino Acid Motifs Humans Proteins Amino Acid Sequence Deep Learning Neural Networks, Computer
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NepoIP/MM: Toward Accurate Biomolecular Simulation with a Machine Learning/Molecular Mechanics Model Incorporating Polarization Effects.

Journal of chemical theory and computation Jun 10, 2025
Machine learning force fields offer the ability to simulate biomolecules with quantum mechanical accuracy while significantly reducing computational costs, attracting a growing amount of attention in biophysics. Meanwhile, by leveraging the efficienc...
Molecular Dynamics Simulation Quantum Theory Dipeptides Water Proteins Static Electricity Machine Learning
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Extracting Residue Solvent Exposure from Covalent Labeling Data with Machine Learning: A Hybrid Approach for Protein Structure Prediction.

Journal of the American Society for Mass Spectrometry Jun 4, 2025
Hydroxyl radical protein footprinting (HRPF) coupled with mass spectrometry yields information about residue solvent exposure and protein topology. However, data from these experiments are sparse and require computational interpretation to generate u...
Proteins Models, Molecular Hydroxyl Radical Algorithms Machine Learning Protein Footprinting Solvents Mass Spectrometry Protein Conformation
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Predicting Protein Function in the AI and Big Data Era.

Biochemistry Jun 3, 2025
It is an exciting time for researchers working to link proteins to their functions. Most techniques for extracting functional information from genomic sequences were developed several years ago, with major progress driven by the availability of big d...
Humans Proteins Big Data Databases, Protein Artificial Intelligence Computational Biology Deep Learning
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DeepAllo: allosteric site prediction using protein language model (pLM) with multitask learning.

Bioinformatics (Oxford, England) Jun 2, 2025
MOTIVATION: Allostery, the process by which binding at one site perturbs a distant site, is being rendered as a key focus in the field of drug development with its substantial impact on protein function. The identification of allosteric pockets (site...
Software Computational Biology Machine Learning Models, Molecular Proteins Allosteric Site Databases, Protein
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The signed two-space proximity model for learning representations in protein-protein interaction networks.

Bioinformatics (Oxford, England) Jun 2, 2025
MOTIVATION: Accurately predicting complex protein-protein interactions (PPIs) is crucial for decoding biological processes, from cellular functioning to disease mechanisms. However, experimental methods for determining PPIs are computationally expens...
Protein Interaction Maps Computational Biology Protein Interaction Mapping Proteins Machine Learning Algorithms
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Computational methods for modeling protein-protein interactions in the AI era: Current status and future directions.

Drug discovery today Jun 1, 2025
The modeling of protein-protein interactions (PPIs) has been revolutionized by artificial intelligence, with deep learning and end-to-end frameworks such as AlphaFold and its derivatives now dominating the field. This review surveys the current compu...
Protein Interaction Mapping Proteins Models, Molecular Humans Artificial Intelligence Protein Binding Molecular Docking Simulation Computational Biology
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Characterization of conformational flexibility in protein structures by applying artificial intelligence to molecular modeling.

Journal of structural biology Jun 1, 2025
Recent AI applications have revolutionized the modeling of structurally unresolved protein regions, thereby complementing traditional computational methods. These state-of-the-art techniques can generate numerous candidate structures, significantly e...
Thermodynamics Protein Folding Proteins Models, Molecular Protein Conformation Ubiquitin Molecular Dynamics Simulation Artificial Intelligence
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