AIMC Topic: Ligands

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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 Molecular Dynamics Simulation Humans Thermodynamics Protein Binding Ligands Machine Learning 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 Ligands Drug Discovery Deep Learning Molecular Imaging Humans Algorithms
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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...
Humans Protein Binding Machine Learning Ligands Dihydroorotate Dehydrogenase Molecular Docking Simulation Molecular Structure Enzyme Inhibitors Oxidoreductases Acting on CH-CH Group Donors Molecular Dynamics Simulation Binding Sites
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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 Drug Discovery Humans Receptors, G-Protein-Coupled Machine Learning
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Prediction of aptamer affinity using an artificial intelligence approach.

Journal of materials chemistry. B Sep 18, 2024
Aptamers are oligonucleotide sequences that can connect to particular target molecules, similar to monoclonal antibodies. They can be chosen by systematic evolution of ligands by exponential enrichment (SELEX), and are modifiable and can be synthesiz...
Humans Artificial Intelligence Molecular Docking Simulation Aptamers, Nucleotide Molecular Dynamics Simulation Ligands
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Combined Physics- and Machine-Learning-Based Method to Identify Druggable Binding Sites Using SILCS-Hotspots.

Journal of chemical information and modeling Sep 16, 2024
Identifying druggable binding sites on proteins is an important and challenging problem, particularly for cryptic, allosteric binding sites that may not be obvious from X-ray, cryo-EM, or predicted structures. The Site-Identification by Ligand Compet...
Proteins Molecular Dynamics Simulation Ligands Protein Conformation Machine Learning Binding Sites Drug Discovery Protein Binding
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Automated design of multi-target ligands by generative deep learning.

Nature communications Sep 11, 2024
Generative deep learning models enable data-driven de novo design of molecules with tailored features. Chemical language models (CLM) trained on string representations of molecules such as SMILES have been successfully employed to design new chemical...
Ligands Polypharmacology Proteins Models, Chemical Humans Drug Design Drug Discovery Deep Learning
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Linear symmetric self-selecting 14-bit kinetic molecular memristors.

Nature Sep 11, 2024
Artificial Intelligence (AI) is the domain of large resource-intensive data centres that limit access to a small community of developers. Neuromorphic hardware promises greatly improved space and energy efficiency for AI but is presently only capable...
Thermodynamics Fourier Analysis Kinetics Ligands Neural Networks, Computer Signal-To-Noise Ratio Artificial Intelligence Signal Processing, Computer-Assisted
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In silico assessments of the small molecular boron agents to pave the way for artificial intelligence-based boron neutron capture therapy.

European journal of medicinal chemistry Sep 6, 2024
Boron neutron capture therapy (BNCT) is a highly targeted, selective and effective technique to cure various types of cancers, with less harm to the healthy cells. In principle, BNCT treatment needs to distribute the boron (B) atoms inside the tumor ...
Humans Molecular Docking Simulation Molecular Structure Molecular Dynamics Simulation Artificial Intelligence Computer Simulation Neoplasms Boron Compounds Boron Neutron Capture Therapy Boron Ligands Small Molecule Libraries
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