AI Medical Compendium Topic:
Ligands

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Improved pan-specific prediction of MHC class I peptide binding using a novel receptor clustering data partitioning strategy.

HLA Oct 20, 2016
Pan-specific prediction of receptor-ligand interaction is conventionally done using machine-learning methods that integrates information about both receptor and ligand primary sequences. To achieve optimal performance using machine learning, dealing ...
Epitopes Alleles Histocompatibility Antigens Class I Ligands Protein Binding Mice Gene Expression Structural Homology, Protein Macaca Gorilla gorilla Pan troglodytes Oligopeptides Binding Sites Protein Interaction Domains and Motifs Software Animals Humans Machine Learning
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Correcting the impact of docking pose generation error on binding affinity prediction.

BMC bioinformatics Sep 22, 2016
BACKGROUND: Pose generation error is usually quantified as the difference between the geometry of the pose generated by the docking software and that of the same molecule co-crystallised with the considered protein. Surprisingly, the impact of this e...
Pyrazines Ligands Protein Conformation Protein Binding Humans Nuclear Proteins Software Transcription Factors Molecular Docking Simulation
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A D3R prospective evaluation of machine learning for protein-ligand scoring.

Journal of computer-aided molecular design Sep 3, 2016
We assess the performance of several machine learning-based scoring methods at protein-ligand pose prediction, virtual screening, and binding affinity prediction. The methods and the manner in which they were trained make them sufficiently diverse to...
Humans Machine Learning Ligands Binding Sites HSP90 Heat-Shock Proteins Protein Binding Computational Biology Proteins Algorithms Prospective Studies Molecular Docking Simulation
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Molecular graph convolutions: moving beyond fingerprints.

Journal of computer-aided molecular design Aug 24, 2016
Molecular "fingerprints" encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications. However, fingerprint representations necessarily emphasize particular aspects of the molecular structur...
Neural Networks, Computer Machine Learning Computer-Aided Design Ligands Computer Graphics Drug Design Molecular Structure Pharmaceutical Preparations
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In Silico Prediction of Gamma-Aminobutyric Acid Type-A Receptors Using Novel Machine-Learning-Based SVM and GBDT Approaches.

BioMed research international Aug 8, 2016
Gamma-aminobutyric acid type-A receptors (GABAARs) belong to multisubunit membrane spanning ligand-gated ion channels (LGICs) which act as the principal mediators of rapid inhibitory synaptic transmission in the human brain. Therefore, the category p...
Support Vector Machine Algorithms Ligands Decision Making Brain Humans Models, Statistical Amino Acid Motifs Computer Simulation Machine Learning Decision Trees Receptors, GABA-A Reproducibility of Results Databases, Protein Computational Biology Amino Acids
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Knodle: A Support Vector Machines-Based Automatic Perception of Organic Molecules from 3D Coordinates.

Journal of chemical information and modeling Jul 21, 2016
Here we address the problem of the assignment of atom types and bond orders in low molecular weight compounds. For this purpose, we have developed a prediction model based on nonlinear Support Vector Machines (SVM), implemented in a KNOwledge-Driven ...
Molecular Weight Automation Informatics Software Support Vector Machine Ligands Algorithms Organic Chemicals
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Protease Inhibitors in View of Peptide Substrate Databases.

Journal of chemical information and modeling Jun 9, 2016
Protease substrate profiling has nowadays almost become a routine task for experimentalists, and the knowledge on protease peptide substrates is easily accessible via the MEROPS database. We present a shape-based virtual screening workflow using vROC...
Peptide Hydrolases Databases, Pharmaceutical Substrate Specificity Protease Inhibitors Small Molecule Libraries Drug Evaluation, Preclinical Ligands User-Computer Interface Protein Conformation Models, Molecular Machine Learning Peptides
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Effect of Binding Pose and Modeled Structures on SVMGen and GlideScore Enrichment of Chemical Libraries.

Journal of chemical information and modeling May 24, 2016
Virtual screening consists of docking libraries of small molecules to a target protein followed by rank-ordering of the resulting structures using scoring functions. The ability of scoring methods to distinguish between actives and inactives depends ...
Ligands Crystallography, X-Ray Small Molecule Libraries Protein Binding Support Vector Machine Drug Evaluation, Preclinical Protein Kinases Molecular Docking Simulation User-Computer Interface
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Toward High-Throughput Predictive Modeling of Protein Binding/Unbinding Kinetics.

Journal of chemical information and modeling May 20, 2016
One of the unaddressed challenges in drug discovery is that drug potency determined in vitro is not a reliable indicator of drug activity in vivo. Accumulated evidence suggests that in vivo activity is more strongly correlated with the binding/unbind...
Computational Biology Kinetics Machine Learning Molecular Dynamics Simulation Protein Conformation Drug Discovery Thermodynamics Protein Binding Static Electricity Safety Ligands
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Machine learning optimization of cross docking accuracy.

Computational biology and chemistry May 4, 2016
Performance of small molecule automated docking programs has conceptually been divided into docking -, scoring -, ranking - and screening power, which focuses on the crystal pose prediction, affinity prediction, ligand ranking and database screening ...
Molecular Docking Simulation Machine Learning Drug Design Ligands
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