AIMC Topic: Ligands

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GeoNet enables the accurate prediction of protein-ligand binding sites through interpretable geometric deep learning.

Structure (London, England : 1993) Nov 1, 2024
The identification of protein binding residues is essential for understanding their functions in vivo. However, it remains a computational challenge to accurately identify binding sites due to the lack of known residue binding patterns. Local residue...
Ligands Binding Sites DNA Models, Molecular RNA Computational Biology Proteins Deep Learning Protein Binding
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Challenge for Deep Learning: Protein Structure Prediction of Ligand-Induced Conformational Changes at Allosteric and Orthosteric Sites.

Journal of chemical information and modeling Nov 1, 2024
In the realm of biomedical research, understanding the intricate structure of proteins is crucial, as these structures determine how proteins function within our bodies and interact with potential drugs. Traditionally, methods like X-ray crystallogra...
Allosteric Site Ligands Protein Conformation Deep Learning Proteins Databases, Protein Models, Molecular
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AlzyFinder: A Machine-Learning-Driven Platform for Ligand-Based Virtual Screening and Network Pharmacology.

Journal of chemical information and modeling Oct 31, 2024
Alzheimer's disease (AD), a prevalent neurodegenerative disorder, presents significant challenges in drug development due to its multifactorial nature and complex pathophysiology. The AlzyFinder Platform, introduced in this study, addresses these cha...
Humans Machine Learning Alzheimer Disease Ligands Network Pharmacology Drug Discovery User-Computer Interface Drug Evaluation, Preclinical
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Molecular tweaking by generative cheminformatics and ligand-protein structures for rational drug discovery.

Bioorganic chemistry Oct 28, 2024
The purpose of this review is two-fold: (1) to summarize artificial intelligence and machine learning approaches and document the role of ligand-protein structures in directing drug discovery; (2) to present examples of drugs from the recent literatu...
Proteins Molecular Structure Artificial Intelligence Ligands Cheminformatics Machine Learning Humans Drug Discovery
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Screening of BindingDB database ligands against EGFR, HER2, Estrogen, Progesterone and NF-κB receptors based on machine learning and molecular docking.

Computers in biology and medicine Oct 25, 2024
Breast cancer, the second most prevalent cancer among women worldwide, necessitates the exploration of novel therapeutic approaches. To target the four subgroups of breast cancer "hormone receptor-positive and HER2-negative, hormone receptor-positive...
Receptors, Progesterone Databases, Protein Female NF-kappa B Breast Neoplasms Ligands Molecular Docking Simulation Receptor, ErbB-2 Humans ErbB Receptors Machine Learning Receptors, Estrogen
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Towards novel small-molecule inhibitors blocking PD-1/PD-L1 pathway: From explainable machine learning models to molecular dynamics simulation.

International journal of biological macromolecules Oct 15, 2024
Molecular design of small-molecule inhibitors targeting programmed cell death-1 (PD-1)/programmed cell death ligand-1 (PD-L1) pathway has been recognized as an active research area by the clinical success of cancer immunotherapy. In recent years, usi...
Molecular Docking Simulation Drug Design Immune Checkpoint Inhibitors Humans Machine Learning Ligands Programmed Cell Death 1 Receptor Small Molecule Libraries B7-H1 Antigen Signal Transduction Molecular Dynamics Simulation
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A computational and machine learning approach to identify GPR40-targeting agonists for neurodegenerative disease treatment.

PloS one Oct 8, 2024
The G protein-coupled receptor 40 (GPR40) is known to exert a significant influence on neurogenesis and neurodevelopment within the central nervous system of both humans and rodents. Research findings indicate that the activation of GPR40 by an agoni...
Neurodegenerative Diseases Molecular Docking Simulation Humans Thermodynamics Molecular Dynamics Simulation Machine Learning Protein Binding Ligands Receptors, G-Protein-Coupled
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A deep learning framework combining molecular image and protein structural representations identifies candidate drugs for pain.

Cell reports methods Sep 27, 2024
Artificial intelligence (AI) and deep learning technologies hold promise for identifying effective drugs for human diseases, including pain. Here, we present an interpretable deep-learning-based ligand image- and receptor's three-dimensional (3D)-str...
Receptors, G-Protein-Coupled Pain Molecular Imaging Humans Ligands Drug Discovery Algorithms Deep Learning
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Development of a machine learning-based target-specific scoring function for structure-based binding affinity prediction for human dihydroorotate dehydrogenase inhibitors.

Journal of computational chemistry Sep 26, 2024
Human dihydroorotate dehydrogenase (hDHODH) is a flavin mononucleotide-dependent enzyme that can limit de novo pyrimidine synthesis, making it a therapeutic target for diseases such as autoimmune disorders and cancer. In this study, using the docking...
Protein Binding Molecular Structure Molecular Dynamics Simulation Ligands Enzyme Inhibitors Binding Sites Humans Machine Learning Molecular Docking Simulation Dihydroorotate Dehydrogenase Oxidoreductases Acting on CH-CH Group Donors
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A Machine Learning Algorithm Suggests Repurposing Opportunities for Targeting Selected GPCRs.

International journal of molecular sciences Sep 23, 2024
Repurposing utilizes existing drugs with known safety profiles and discovers new uses by combining experimental and computational approaches. The integration of computational methods has greatly advanced drug repurposing, offering a rational approach...
Drug Repositioning Ligands Algorithms Humans Drug Discovery Receptors, G-Protein-Coupled Machine Learning
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