AIMC Topic: Ligands

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Rapid traversal of vast chemical space using machine learning-guided docking screens.

Nature computational science Mar 13, 2025
The accelerating growth of make-on-demand chemical libraries provides unprecedented opportunities to identify starting points for drug discovery with virtual screening. However, these multi-billion-scale libraries are challenging to screen, even for ...
Drug Discovery Algorithms Receptors, G-Protein-Coupled Small Molecule Libraries Molecular Docking Simulation Ligands Humans Machine Learning
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Join Persistent Homology (JPH)-Based Machine Learning for Metalloprotein-Ligand Binding Affinity Prediction.

Journal of chemical information and modeling Mar 13, 2025
With the crucial role of metalloproteins in respiration, oxidative stress protection, photosynthesis, and drug metabolism, the design and discovery of drugs that can target metalloproteins are extremely important. Recently, enormous potential has bee...
Protein Binding Machine Learning Ligands Metalloproteins
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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...
Animals Zebrafish Proteins Receptors, Androgen Androgens Dihydrotestosterone Endocrine Disruptors Molecular Dynamics Simulation Zebrafish Ligands Molecular Docking Simulation Testosterone
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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...
Drug Evaluation, Preclinical Ligands Signal Transduction Receptors, G-Protein-Coupled Humans Deep Learning
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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...
Protein Binding Computational Biology Receptors, G-Protein-Coupled Ligands Databases, Protein Humans Deep Learning
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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...
Deep Learning Protein Binding Neural Networks, Computer Directed Molecular Evolution Models, Molecular Amino Acid Sequence Carboxylic Ester Hydrolases Binding Sites Ligands
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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 Machine Learning Neural Networks, Computer
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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 ...
Algorithms Protein Binding Computational Biology Neural Networks, Computer Models, Molecular Proteins Machine Learning Databases, Protein Ligands Binding Sites
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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...
Pharmaceutical Preparations Ligands Machine Learning Quantum Theory Density Functional Theory Molecular Conformation Thermodynamics
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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...
Molecular Docking Simulation Drug Discovery Deep Learning Humans Artificial Intelligence Ligands Binding Sites Protein Binding Proteins
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