AIMC Topic: Ligands

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Coverage bias in small molecule machine learning.

Nature communications Jan 9, 2025
Small molecule machine learning aims to predict chemical, biochemical, or biological properties from molecular structures, with applications such as toxicity prediction, ligand binding, and pharmacokinetics. A recent trend is developing end-to-end mo...
Algorithms Molecular Structure Ligands Machine Learning Small Molecule Libraries
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CovCysPredictor: Predicting Selective Covalently Modifiable Cysteines Using Protein Structure and Interpretable Machine Learning.

Journal of chemical information and modeling Jan 8, 2025
Targeted covalent inhibition is a powerful therapeutic modality in the drug discoverer's toolbox. Recent advances in covalent drug discovery, in particular, targeting cysteines, have led to significant breakthroughs for traditionally challenging targ...
Machine Learning Drug Discovery Proteins Humans Ligands Protein Conformation Cysteine Databases, Protein Models, Molecular
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Evaluation of Machine Learning/Molecular Mechanics End-State Corrections with Mechanical Embedding to Calculate Relative Protein-Ligand Binding Free Energies.

Journal of chemical theory and computation Jan 3, 2025
The development of machine-learning (ML) potentials offers significant accuracy improvements compared to molecular mechanics (MM) because of the inclusion of quantum-mechanical effects in molecular interactions. However, ML simulations are several ti...
Ligands Thermodynamics Machine Learning Protein Binding Molecular Dynamics Simulation Quantum Theory Proteins
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Unveiling key drivers of hepatocellular carcinoma: a synergistic approach with network pharmacology, machine learning-driven ligand discovery and dynamic simulations.

SAR and QSAR in environmental research Jan 3, 2025
Hepatocellular carcinoma (HCC) ranks fourth in cancer-related mortality worldwide. This study aims to uncover the genes and pathways involved in HCC through network pharmacology (NP) and to discover potential drugs via machine learning (ML)-based lig...
Network Pharmacology Liver Neoplasms Molecular Docking Simulation Carcinoma, Hepatocellular Quantitative Structure-Activity Relationship Molecular Dynamics Simulation Drug Discovery Proto-Oncogene Proteins c-akt Humans Ligands Glycogen Synthase Kinase 3 beta Machine Learning
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PharmRL: pharmacophore elucidation with deep geometric reinforcement learning.

BMC biology Dec 31, 2024
BACKGROUND: Molecular interactions between proteins and their ligands are important for drug design. A pharmacophore consists of favorable molecular interactions in a protein binding site and can be utilized for virtual screening. Pharmacophores are ...
Pharmacophore Humans Algorithms Proteins Ligands Binding Sites Drug Design Deep Learning
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Evaluations of the Perturbation Resistance of the Deep-Learning-Based Ligand Conformation Optimization Algorithm.

Journal of chemical information and modeling Dec 26, 2024
In recent years, the deep learning (DL) technique has rapidly developed and shown great success in scoring the protein-ligand binding affinities. The protein-ligand conformation optimization based on DL-derived scoring functions holds broad applicati...
Proteins Ligands Protein Conformation Molecular Docking Simulation Deep Learning Protein Binding Algorithms
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A 4D tensor-enhanced multi-dimensional convolutional neural network for accurate prediction of protein-ligand binding affinity.

Molecular diversity Dec 23, 2024
Protein-ligand interactions are the molecular basis of many important cellular activities, such as gene regulation, cell metabolism, and signal transduction. Protein-ligand binding affinity is a crucial metric of the strength of the interaction betwe...
Neural Networks, Computer Deep Learning Convolutional Neural Networks Models, Molecular Ligands Protein Binding Binding Sites Proteins
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BioStructNet: Structure-Based Network with Transfer Learning for Predicting Biocatalyst Functions.

Journal of chemical theory and computation Dec 20, 2024
Enzyme-substrate interactions are essential to both biological processes and industrial applications. Advanced machine learning techniques have significantly accelerated biocatalysis research, revolutionizing the prediction of biocatalytic activities...
Machine Learning Algorithms Ligands Deep Learning Biocatalysis Molecular Dynamics Simulation Enzymes
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Residue-Level Multiview Deep Learning for ATP Binding Site Prediction and Applications in Kinase Inhibitors.

Journal of chemical information and modeling Dec 17, 2024
Accurate identification of adenosine triphosphate (ATP) binding sites is crucial for understanding cellular functions and advancing drug discovery, particularly in targeting kinases for cancer treatment. Existing methods face significant challenges d...
Humans Molecular Docking Simulation Binding Sites Deep Learning Adenosine Triphosphate Drug Discovery Ligands Protein Kinase Inhibitors
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Combined usage of ligand- and structure-based virtual screening in the artificial intelligence era.

European journal of medicinal chemistry Dec 10, 2024
Drug design has always been pursuing techniques with time- and cost-benefits. Virtual screening, generally classified as ligand-based (LBVS) and structure-based (SBVS) approaches, could identify active compounds in the large chemical library to reduc...
Small Molecule Libraries Machine Learning Drug Design Drug Evaluation, Preclinical Ligands Molecular Structure Humans Artificial Intelligence
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