AIMC Topic: Ligands

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Mining for Potent Inhibitors through Artificial Intelligence and Physics: A Unified Methodology for Ligand Based and Structure Based Drug Design.

Journal of chemical information and modeling Jun 6, 2024
Determining the viability of a new drug molecule is a time- and resource-intensive task that makes computer-aided assessments a vital approach to rapid drug discovery. Here we develop a machine learning algorithm, iMiner, that generates novel inhibit...
Molecular Dynamics Simulation Molecular Docking Simulation Data Mining Drug Design Artificial Intelligence Ligands Proteins Machine Learning Algorithms
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Geometry Optimization Algorithms in Conjunction with the Machine Learning Potential ANI-2x Facilitate the Structure-Based Virtual Screening and Binding Mode Prediction.

Biomolecules May 31, 2024
Structure-based virtual screening utilizes molecular docking to explore and analyze ligand-macromolecule interactions, crucial for identifying and developing potential drug candidates. Although there is availability of several widely used docking pro...
Molecular Docking Simulation Protein Binding Ligands Binding Sites Machine Learning Algorithms
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PfgPDI: Pocket feature-enabled graph neural network for protein-drug interaction prediction.

Journal of bioinformatics and computational biology May 27, 2024
Biomolecular interaction recognition between ligands and proteins is an essential task, which largely enhances the safety and efficacy in drug discovery and development stage. Studying the interaction between proteins and ligands can improve the unde...
Proteins Deep Learning Humans Ligands Databases, Protein Protein Binding Neural Networks, Computer Computational Biology Drug Discovery Pharmaceutical Preparations
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Guided Docking as a Data Generation Approach Facilitates Structure-Based Machine Learning on Kinases.

Journal of chemical information and modeling May 15, 2024
Drug discovery pipelines nowadays rely on machine learning models to explore and evaluate large chemical spaces. While including 3D structural information is considered beneficial, structural models are hindered by the availability of protein-ligand ...
Phosphotransferases Ligands Protein Conformation Drug Discovery Protein Binding Protein Kinase Inhibitors Machine Learning Neural Networks, Computer Protein Kinases Molecular Docking Simulation
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MISATO: machine learning dataset of protein-ligand complexes for structure-based drug discovery.

Nature computational science May 10, 2024
Large language models have greatly enhanced our ability to understand biology and chemistry, yet robust methods for structure-based drug discovery, quantum chemistry and structural biology are still sparse. Precise biomolecule-ligand interaction data...
Ligands Machine Learning Drug Discovery Proteins Molecular Dynamics Simulation Quantum Theory
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Accurate structure prediction of biomolecular interactions with AlphaFold 3.

Nature May 8, 2024
The introduction of AlphaFold 2 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design. Here we describe our AlphaFold 3 model with a substantially...
Reproducibility of Results Molecular Docking Simulation Deep Learning Humans Software Antibodies Ions Ligands Protein Binding Proteins Models, Molecular Nucleic Acids Protein Conformation Antigens
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Unveiling G-protein coupled receptor kinase-5 inhibitors for chronic degenerative diseases: Multilayered prioritization employing explainable machine learning-driven multi-class QSAR, ligand-based pharmacophore and free energy-inspired molecular simulation.

International journal of biological macromolecules Apr 30, 2024
GRK5 holds a pivotal role in cellular signaling pathways, with its overexpression in cardiomyocytes, neuronal cells, and tumor cells strongly associated with various chronic degenerative diseases, which highlights the urgent need for potential inhibi...
Protein Binding Chronic Disease Molecular Dynamics Simulation Humans Machine Learning Molecular Docking Simulation Binding Sites Thermodynamics Pharmacophore G-Protein-Coupled Receptor Kinase 5 Ligands Quantitative Structure-Activity Relationship Protein Kinase Inhibitors
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Large-scale chemoproteomics expedites ligand discovery and predicts ligand behavior in cells.

Science (New York, N.Y.) Apr 26, 2024
Chemical modulation of proteins enables a mechanistic understanding of biology and represents the foundation of most therapeutics. However, despite decades of research, 80% of the human proteome lacks functional ligands. Chemical proteomics has advan...
Proteome Ligands Ubiquitin-Protein Ligases Drug Discovery Humans Small Molecule Libraries Protein Binding Machine Learning Proteomics
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Predicting FFAR4 agonists using structure-based machine learning approach based on molecular fingerprints.

Scientific reports Apr 24, 2024
Free Fatty Acid Receptor 4 (FFAR4), a G-protein-coupled receptor, is responsible for triggering intracellular signaling pathways that regulate various physiological processes. FFAR4 agonists are associated with enhancing insulin release and mitigatin...
Binding Sites Bayes Theorem Molecular Dynamics Simulation Ligands Molecular Docking Simulation Humans Protein Binding Machine Learning Receptors, G-Protein-Coupled
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HBCVTr: an end-to-end transformer with a deep neural network hybrid model for anti-HBV and HCV activity predictor from SMILES.

Scientific reports Apr 22, 2024
Hepatitis B and C viruses (HBV and HCV) are significant causes of chronic liver diseases, with approximately 350 million infections globally. To accelerate the finding of effective treatment options, we introduce HBCVTr, a novel ligand-based drug des...
Molecular Docking Simulation Drug Design Antiviral Agents Hepatitis B virus Hepatitis B Molecular Dynamics Simulation Humans Neural Networks, Computer Viral Nonstructural Proteins Hepacivirus Hepatitis C Ligands
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