AIMC Topic: Protein Binding

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Recent advances in AI-driven protein-ligand interaction predictions.

Current opinion in structural biology Feb 24, 2025
Structure-based drug discovery is a fundamental approach in modern drug development, leveraging computational models to predict protein-ligand interactions. AI-driven methodologies are significantly improving key aspects of the field, including ligan...
Protein Binding Proteins Binding Sites Ligands Drug Discovery Deep Learning Molecular Docking Simulation Humans Artificial Intelligence
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Natural Language Processing Methods for the Study of Protein-Ligand Interactions.

Journal of chemical information and modeling Feb 24, 2025
Natural Language Processing (NLP) has revolutionized the way computers are used to study and interact with human languages and is increasingly influential in the study of protein and ligand binding, which is critical for drug discovery and developmen...
Ligands Proteins Natural Language Processing Machine Learning Drug Discovery Humans Protein Binding
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Compact Assessment of Molecular Surface Complementarities Enhances Neural Network-Aided Prediction of Key Binding Residues.

Journal of chemical information and modeling Feb 21, 2025
Predicting interactions between proteins is fundamental for understanding the mechanisms underlying cellular processes, since protein-protein complexes are crucial in physiological conditions but also in many diseases, for example by seeding aggregat...
ROC Curve Protein Binding Hydrophobic and Hydrophilic Interactions Surface Properties Proteins Neural Networks, Computer Molecular Docking Simulation Binding Sites
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Estimating Absolute Protein-Protein Binding Free Energies by a Super Learner Model.

Journal of chemical information and modeling Feb 20, 2025
Protein-protein binding is central to most biochemical processes of all living beings. Its importance underlies mechanisms ranging from cell interactions to metabolic control, but also to biotechnology, such as the development of therapeutic monoclo...
Protein Binding Machine Learning Proteins Thermodynamics
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T-ALPHA: A Hierarchical Transformer-Based Deep Neural Network for Protein-Ligand Binding Affinity Prediction with Uncertainty-Aware Self-Learning for Protein-Specific Alignment.

Journal of chemical information and modeling Feb 18, 2025
There is significant interest in targeting disease-causing proteins with small molecule inhibitors to restore healthy cellular states. The ability to accurately predict the binding affinity of small molecules to a protein target in silico enables the...
Ligands ErbB Receptors Humans Uncertainty Neural Networks, Computer Protein Binding Proteins Drug Discovery SARS-CoV-2 Deep Learning
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Identification and understanding of allostery hotspots in proteins: Integration of deep mutational scanning and multi-faceted computational analyses.

Journal of molecular biology Feb 12, 2025
Motivated by recent deep mutational scanning (DMS) experiments, we have carried out a diverse set of computations to better understand the distribution and contributions of allostery hotspot residues in a transcription factor, TetR. These include ext...
Proteins Models, Molecular Mutation Thermodynamics Machine Learning Protein Binding Allosteric Site Computational Biology Molecular Dynamics Simulation Allosteric Regulation Protein Conformation
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CL-GNN: Contrastive Learning and Graph Neural Network for Protein-Ligand Binding Affinity Prediction.

Journal of chemical information and modeling Feb 6, 2025
In the realm of drug discovery and design, the accurate prediction of protein-ligand binding affinity is of paramount importance as it underpins the functional interactions within biological systems. This study introduces a novel self-supervised lear...
Neural Networks, Computer Machine Learning Proteins Ligands Graph Neural Networks Drug Discovery Protein Binding
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Protein ligand structure prediction: From empirical to deep learning approaches.

Current opinion in structural biology Feb 5, 2025
Protein-ligand structure prediction methods, aiming to predict the three-dimensional complex structure and binding energy of a compound and target protein, are essential in many structure-based drug discovery pipelines, including virtual screening an...
Protein Conformation Models, Molecular Ligands Drug Discovery Deep Learning Computational Biology Proteins Protein Binding
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ZeRPI: A graph neural network model for zero-shot prediction of RNA-protein interactions.

Methods (San Diego, Calif.) Jan 30, 2025
RNA-protein interactions are crucial for biological functions across multiple levels. RNA binding proteins (RBPs) intricately engage in diverse biological processes through specific RNA molecule interactions. Previous studies have revealed the indisp...
Machine Learning Computational Biology Algorithms Graph Neural Networks Humans RNA RNA-Binding Proteins Protein Binding Neural Networks, Computer
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MutualDTA: An Interpretable Drug-Target Affinity Prediction Model Leveraging Pretrained Models and Mutual Attention.

Journal of chemical information and modeling Jan 29, 2025
Efficient and accurate drug-target affinity (DTA) prediction can significantly accelerate the drug development process. Recently, deep learning models have been widely applied to DTA prediction and have achieved notable success. However, existing met...
Protein Binding Proteins Pharmaceutical Preparations Drug Discovery Deep Learning Binding Sites Humans
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