AIMC Topic: Ligands

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Visualization and Interpretation of Support Vector Machine Activity Predictions.

Journal of chemical information and modeling Jun 2, 2015
Support vector machines (SVMs) are among the preferred machine learning algorithms for virtual compound screening and activity prediction because of their frequently observed high performance levels. However, a well-known conundrum of SVMs (and other...
Mitogen-Activated Protein Kinase 14 Receptor, Cannabinoid, CB2 Linear Models Protein Kinase Inhibitors Receptors, Dopamine D2 Humans Software Drug Evaluation, Preclinical Ligands Algorithms Support Vector Machine
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Machine-learning scoring functions for identifying native poses of ligands docked to known and novel proteins.

BMC bioinformatics Apr 17, 2015
BACKGROUND: Molecular docking is a widely-employed method in structure-based drug design. An essential component of molecular docking programs is a scoring function (SF) that can be used to identify the most stable binding pose of a ligand, when boun...
Proteins Ligands Binding Sites Humans Artificial Intelligence Software Drug Design Drug Discovery Protein Binding
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Multi-Step Protocol for Automatic Evaluation of Docking Results Based on Machine Learning Methods--A Case Study of Serotonin Receptors 5-HT(6) and 5-HT(7).

Journal of chemical information and modeling Apr 8, 2015
Molecular docking, despite its undeniable usefulness in computer-aided drug design protocols and the increasing sophistication of tools used in the prediction of ligand-protein interaction energies, is still connected with a problem of effective resu...
Ligands Automation Protein Conformation Machine Learning Algorithms Molecular Docking Simulation Receptors, Adrenergic, beta-2 Receptors, Serotonin
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Clustering molecular dynamics trajectories for optimizing docking experiments.

Computational intelligence and neuroscience Mar 22, 2015
Molecular dynamics simulations of protein receptors have become an attractive tool for rational drug discovery. However, the high computational cost of employing molecular dynamics trajectories in virtual screening of large repositories threats the f...
Proteins Molecular Dynamics Simulation Algorithms Artificial Intelligence Ligands Cluster Analysis Software
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BgN-Score and BsN-Score: bagging and boosting based ensemble neural networks scoring functions for accurate binding affinity prediction of protein-ligand complexes.

BMC bioinformatics Feb 23, 2015
BACKGROUND: Accurately predicting the binding affinities of large sets of protein-ligand complexes is a key challenge in computational biomolecular science, with applications in drug discovery, chemical biology, and structural biology. Since a scorin...
Humans Neural Networks, Computer Artificial Intelligence Algorithms Ligands Models, Statistical Protein Binding Binding Sites Proteins Computational Biology
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Machine learning in computational docking.

Artificial intelligence in medicine Feb 16, 2015
OBJECTIVE: The objective of this paper is to highlight the state-of-the-art machine learning (ML) techniques in computational docking. The use of smart computational methods in the life cycle of drug design is relatively a recent development that has...
Databases, Protein Data Interpretation, Statistical Quantum Theory Protein Binding Ligands Humans Computational Biology Protein Conformation Machine Learning Proteins
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PENG: a neural gas-based approach for pharmacophore elucidation. method design, validation, and virtual screening for novel ligands of LTA4H.

Journal of chemical information and modeling Feb 9, 2015
The pharmacophore concept is commonly employed in virtual screening for hit identification. A pharmacophore is generally defined as the three-dimensional arrangement of the structural and physicochemical features of a compound responsible for its aff...
Benchmarking Ligands High-Throughput Screening Assays Thermodynamics Protein Binding Humans Epoxide Hydrolases Aminopeptidases Enzyme Inhibitors Neural Networks, Computer Algorithms Crystallography, X-Ray Models, Molecular Prospective Studies Reproducibility of Results
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The ligand binding mechanism to purine nucleoside phosphorylase elucidated via molecular dynamics and machine learning.

Nature communications Jan 27, 2015
The study of biomolecular interactions between a drug and its biological target is of paramount importance for the design of novel bioactive compounds. In this paper, we report on the use of molecular dynamics (MD) simulations and machine learning to...
Kinetics Molecular Dynamics Simulation Ligands Pyrrolidines Protein Multimerization Pyrimidinones Thermodynamics Time Factors Protein Binding Cluster Analysis Humans Machine Learning Algorithms Purine-Nucleoside Phosphorylase
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A comparative study of family-specific protein-ligand complex affinity prediction based on random forest approach.

Journal of computer-aided molecular design Dec 20, 2014
The assessment of binding affinity between ligands and the target proteins plays an essential role in drug discovery and design process. As an alternative to widely used scoring approaches, machine learning methods have also been proposed for fast pr...
Artificial Intelligence Computer-Aided Design Drug Design Protein Binding Humans Carbonic Anhydrases Proteins Ligands Databases, Protein HIV Protease Models, Biological Trypsin
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Supervised machine learning and molecular docking modeling to identify potential Anti-Parkinson's agents.

Journal of molecular graphics & modelling Sep 1, 2025
Parkinson's disease is a neurodegenerative condition that affects the brain's neurons, and causes malfunction of nerve cells and their death. A neurotransmitter called dopamine interacts with the part of the brain in charge of coordination and moveme...
Humans Machine Learning Supervised Machine Learning Molecular Docking Simulation Antiparkinson Agents Ligands Protein Binding Receptor, Adenosine A2A Parkinson Disease
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