AIMC Topic: Ligands

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Leveraging AlphaFold models to predict androgenic effects of endocrine-disrupting chemicals through zebrafish androgen receptor analysis.

Toxicology mechanisms and methods Mar 12, 2025
The androgen receptor (AR) activation by androgens is vital for tissue development, sexual differentiation, and reproductive attributes in zebrafish (). However, our understanding of the molecular mechanisms behind their activation remains limited. I...
Dihydrotestosterone Ligands Zebrafish Proteins Endocrine Disruptors Androgens Receptors, Androgen Testosterone Molecular Dynamics Simulation Animals Zebrafish Molecular Docking Simulation
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Fine-Tuned Deep Transfer Learning Models for Large Screenings of Safer Drugs Targeting Class A GPCRs.

Biochemistry Mar 8, 2025
G protein-coupled receptors (GPCRs) remain a focal point of research due to their critical roles in cell signaling and their prominence as drug targets. However, directly linking drug efficacy to the receptor-mediated activation of specific intracell...
Deep Learning Signal Transduction Drug Evaluation, Preclinical Ligands Receptors, G-Protein-Coupled Humans
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TrGPCR: GPCR-Ligand Binding Affinity Prediction Based on Dynamic Deep Transfer Learning.

IEEE journal of biomedical and health informatics Mar 6, 2025
Predicting G protein-coupled receptor (GPCR) -ligand binding affinity plays a crucial role in drug development. However, determining GPCR-ligand binding affinities is time-consuming and resource-intensive. Although many studies used data-driven metho...
Ligands Deep Learning Protein Binding Databases, Protein Receptors, G-Protein-Coupled Computational Biology Humans
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Development of DeepPQK and DeepQK sequence-based deep learning models to predict protein-ligand affinity and application in the directed evolution of ferulic esterase DLfae4.

International journal of biological macromolecules Mar 5, 2025
Affinity plays an essential role in the rate and stability of enzyme-catalyzed reactions, thus directly impacting the catalytic activity. In general, the predictive method for protein-ligand binding affinity mainly relies on high-resolution protein c...
Carboxylic Ester Hydrolases Binding Sites Ligands Models, Molecular Protein Binding Neural Networks, Computer Directed Molecular Evolution Amino Acid Sequence Deep Learning
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iScore: A ML-Based Scoring Function for De Novo Drug Discovery.

Journal of chemical information and modeling Mar 4, 2025
In the quest for accelerating de novo drug discovery, the development of efficient and accurate scoring functions represents a fundamental challenge. This study introduces iScore, a novel machine learning (ML)-based scoring function designed to predi...
Proteins Ligands Drug Discovery Protein Binding Neural Networks, Computer Machine Learning
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Skittles: GNN-Assisted Pseudo-Ligands Generation and Its Application for Binding Sites Classification and Affinity Prediction.

Proteins Feb 28, 2025
Nowadays, multiple solutions are known for identifying ligand-protein binding sites. Another important task is labeling each point of a binding site with the appropriate atom type, a process known as pseudo-ligand generation. The number of solutions ...
Machine Learning Algorithms Protein Binding Neural Networks, Computer Models, Molecular Ligands Binding Sites Proteins Databases, Protein Computational Biology
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Discriminating High from Low Energy Conformers of Druglike Molecules: An Assessment of Machine Learning Potentials and Quantum Chemical Methods.

Chemphyschem : a European journal of chemical physics and physical chemistry Feb 27, 2025
Accurate and efficient prediction of high energy ligand conformations is important in structure-based drug discovery for the exclusion of unrealistic structures in docking-based virtual screening and de novo design approaches. In this work, we constr...
Molecular Conformation Thermodynamics Pharmaceutical Preparations Quantum Theory Ligands Machine Learning Density Functional Theory
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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...
Artificial Intelligence Molecular Docking Simulation Drug Discovery Humans Proteins Ligands Binding Sites Deep Learning Protein Binding
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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...
Natural Language Processing Ligands Protein Binding Machine Learning Humans Proteins Drug Discovery
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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...
Humans ErbB Receptors Uncertainty Drug Discovery Proteins Deep Learning Ligands Protein Binding Neural Networks, Computer SARS-CoV-2
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