AIMC Topic: Proteins

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CrypTothML: An Integrated Mixed-Solvent Molecular Dynamics Simulation and Machine Learning Approach for Cryptic Site Prediction.

International journal of molecular sciences May 14, 2025
Cryptic sites, which are transient binding sites that emerge through protein conformational changes upon ligand binding, are valuable targets for drug discovery, particularly for allosteric modulators. However, identifying these sites remains challen...
Molecular Dynamics Simulation Ligands Protein Binding Proteins Solvents Binding Sites Machine Learning Drug Discovery
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Prediction and design of thermostable proteins with a desired melting temperature.

Scientific reports May 14, 2025
The stability of proteins at higher temperatures is crucial for their functionality, which is measured by their melting temperature (Tm). The Tm is the temperature at which 50% of the protein loses its native structure and activity. Existing methods ...
Protein Stability Transition Temperature Machine Learning Proteins
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High-Throughput Ligand Dissociation Kinetics Predictions Using Site Identification by Ligand Competitive Saturation.

Journal of chemical theory and computation May 13, 2025
The dissociation or off rate, , of a drug molecule has been shown to be more relevant to efficacy than affinity for selected systems, motivating the development of predictive computational methodologies. These are largely based on enhanced-sampling m...
Ligands Thermodynamics Kinetics Molecular Dynamics Simulation Machine Learning Proteins
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Accurate Predictions of Molecular Properties of Proteins via Graph Neural Networks and Transfer Learning.

Journal of chemical theory and computation May 13, 2025
Machine learning has emerged as a promising approach for predicting molecular properties of proteins, as it addresses limitations of experimental and traditional computational methods. Here, we introduce GSnet, a graph neural network (GNN) trained to...
Protein Conformation Graph Neural Networks Machine Learning Proteins Neural Networks, Computer
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EVOLVE: A Web Platform for AI-Based Protein Mutation Prediction and Evolutionary Phase Exploration.

Journal of chemical information and modeling May 12, 2025
While predicting structure-function relationships from sequence data is fundamental in biophysical chemistry, identifying prospective single-point and collective mutation sites in proteins can help us stay ahead in understanding their potential effec...
Entropy Evolution, Molecular Machine Learning Proteins Mutation Artificial Intelligence Software Internet
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A Database for Large-Scale Docking and Experimental Results.

Journal of chemical information and modeling May 12, 2025
The rapid expansion of readily accessible compounds over the past six years has transformed molecular docking, improving hit rates and affinities. While many millions of molecules may score well in a docking campaign, the results are rarely fully sha...
Databases, Factual Small Molecule Libraries Proteins Molecular Docking Simulation Ligands Machine Learning
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COLOR: A Compositional Linear Operation-Based Representation of Protein Sequences for Identification of Monomer Contributions to Properties.

Journal of chemical information and modeling May 12, 2025
The properties of biological materials like proteins and nucleic acids are largely determined by their primary sequence. Certain segments in the sequence strongly influence specific functions, but identifying these segments, or so-called motifs, is c...
Proteins Amino Acid Sequence Deep Learning
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Molecular surfaces modeling: Advancements in deep learning for molecular interactions and predictions.

Biochemical and biophysical research communications May 12, 2025
Molecular surface analysis can provide a high-dimensional, rich representation of molecular properties and interactions, which is crucial for enabling powerful predictive modeling and rational molecular design across diverse scientific and technologi...
Algorithms Deep Learning Proteins Models, Molecular Humans Artificial Intelligence Surface Properties
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ProtNote: a multimodal method for protein-function annotation.

Bioinformatics (Oxford, England) May 6, 2025
MOTIVATION: Understanding the protein sequence-function relationship is essential for advancing protein biology and engineering. However, <1% of known protein sequences have human-verified functions. While deep-learning methods have demonstrated prom...
Molecular Sequence Annotation Gene Ontology Proteins Databases, Protein Deep Learning Computational Biology Humans Software
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Topology-driven negative sampling enhances generalizability in protein-protein interaction prediction.

Bioinformatics (Oxford, England) May 6, 2025
MOTIVATION: Unraveling the human interactome to uncover disease-specific patterns and discover drug targets hinges on accurate protein-protein interaction (PPI) predictions. However, challenges persist in machine learning (ML) models due to a scarcit...
Humans Machine Learning Binding Sites Protein Interaction Mapping Proteins Databases, Protein Protein Binding Computational Biology
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