AIMC Topic: Ligands

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Deep learning-based dipeptidyl peptidase IV inhibitor screening, experimental validation, and GaMD/LiGaMD analysis.

BMC biology Jul 1, 2025
BACKGROUND: Dipeptidyl peptidase-4 (DPP4) is considered a crucial enzyme in type 2 diabetes (T2D) treatment, targeted by inhibitors due to its role in cleaving glucagon-like peptide-1 (GLP-1). In this study, a novel DPP4 inhibitor screening strategy ...
Humans Molecular Docking Simulation Deep Learning Drug Evaluation, Preclinical Ligands Dipeptidyl Peptidase 4 Diabetes Mellitus, Type 2 Molecular Dynamics Simulation Dipeptidyl-Peptidase IV Inhibitors
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ERRα-Predictor: A Framework of Ensemble Models for Prediction of ERRα Binders, Antagonists, and Agonists Using Artificial Intelligence.

Journal of chemical information and modeling Jun 30, 2025
Estrogen-related receptor α (ERRα) is considered a promising target for the treatment of cancer and metabolic diseases. The development of comprehensive predictive models for ERRα binders, antagonists, and agonists is of significant importance. In th...
Machine Learning Receptors, Estrogen Ligands Humans Neural Networks, Computer ERRalpha Estrogen-Related Receptor Protein Binding Artificial Intelligence
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In-silico guided identification and studies of potential FFAR4 agonists for type 2 diabetes mellitus therapy.

Expert opinion on drug discovery Jun 28, 2025
BACKGROUND: The activation of free fatty acid receptor 4 (FFAR4) enhances insulin sensitivity and glucose uptake while mitigating inflammation. It is a promising therapeutic approach for managing type 2 diabetes mellitus (T2DM).
Diabetes Mellitus, Type 2 Insulin Resistance Hypoglycemic Agents Glucose Humans Molecular Dynamics Simulation Animals Ligands Cell Survival Receptors, G-Protein-Coupled
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LumiCharge: Spherical Harmonic Convolutional Networks for Atomic Charge Prediction in Drug Discovery.

The journal of physical chemistry letters Jun 16, 2025
Atomic charge is crucial in drug design for analyzing reactive sites and interactions between ligands and targets. While quantum mechanical methods offer high accuracy, they are generally computationally costly. Conversely, empirical approaches, whil...
Quantum Theory Ligands Receptors, Androgen Machine Learning Drug Discovery
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Improving Covalent and Noncovalent Molecule Generation via Reinforcement Learning with Functional Fragments.

Journal of chemical information and modeling Jun 5, 2025
Small-molecule drugs play a critical role in cancer therapy by selectively targeting key signaling pathways that drive tumor growth. While deep learning models have advanced drug discovery, there remains a lack of generative frameworks for covalent ...
Small Molecule Libraries Protein Kinase Inhibitors Agammaglobulinaemia Tyrosine Kinase Ligands ErbB Receptors Humans Molecular Docking Simulation Deep Learning Molecular Dynamics Simulation Drug Design
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Prediction of Ligand-Receptor Interactions Based on CatBoost and Deep Forest and Their Application in Cell-Cell Communication Analysis.

Journal of chemical information and modeling May 30, 2025
Cell-to-cell communication (CCC) is prominent for cell growth and development as well as tissue and organ formation. CCC inference can help us to deeply understand cellular interplay and discover potential therapeutic targets for complex diseases. Ce...
Ligands Boosting Machine Learning Algorithms Humans Computational Biology Cell Communication Deep Learning
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In Silico Screening of Small Molecule Inhibitors for Amyloid-β Aggregation.

Journal of chemical information and modeling May 29, 2025
The self-aggregation of amyloid-β (Aβ) into fibrils is a hallmark of Alzheimer's disease (AD). Inhibition of Aβ aggregation with small molecule compounds represents a promising therapeutic strategy for AD. However, designing effective ligands is chal...
Small Molecule Libraries Ligands Protein Aggregates Humans Amyloid beta-Peptides Machine Learning Molecular Dynamics Simulation Molecular Docking Simulation Drug Evaluation, Preclinical
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Modeling Active-State Conformations of G-Protein-Coupled Receptors Using AlphaFold2 via Template Bias and Explicit Protein Constrains.

Journal of chemical information and modeling May 29, 2025
AlphaFold2 and other deep learning tools represent the state of the art for protein structure prediction; however, they are still limited in their ability to model multiple protein conformations. Since the function of many proteins depends on their a...
Deep Learning Receptors, G-Protein-Coupled Ligands Protein Conformation Models, Molecular Humans
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CrypToth: Cryptic Pocket Detection through Mixed-Solvent Molecular Dynamics Simulations-Based Topological Data Analysis.

Journal of chemical information and modeling May 22, 2025
Some functional proteins undergo conformational changes to expose hidden binding sites when a binding molecule approaches their surface. Such binding sites are called cryptic sites and are important targets for drug discovery. However, it is still di...
Protein Conformation Binding Sites Ligands Proteins Solvents Molecular Dynamics Simulation
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On the Difficulty to Rescore Hits from Ultralarge Docking Screens.

Journal of chemical information and modeling May 22, 2025
Docking-based virtual screening tools customized to mine ultralarge chemical spaces are consistently reported to yield both higher hit rates and more potent ligands than that achieved by conventional docking of smaller million-sized compound librarie...
Quantum Theory Molecular Docking Simulation Proteins Protein Binding Ligands Machine Learning Drug Evaluation, Preclinical
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