AIMC Topic: Ligands

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Boosting Docking-Based Virtual Screening with Deep Learning.

Journal of chemical information and modeling Nov 29, 2016
In this work, we propose a deep learning approach to improve docking-based virtual screening. The deep neural network that is introduced, DeepVS, uses the output of a docking program and learns how to extract relevant features from basic data such as...
Humans Neural Networks, Computer Software Drug Design ROC Curve Proteins Ligands Algorithms Molecular Docking Simulation Computer-Aided Design
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Development of pharmacophore-based classification model for activators of constitutive androstane receptor.

Drug metabolism and pharmacokinetics Nov 17, 2016
Constitutive androstane receptor (CAR) is predominantly expressed in the liver and is important for regulating drug metabolism and transport. Despite its biological importance, there have been few attempts to develop in silico models to predict the a...
Ligands Humans Receptors, Cytoplasmic and Nuclear Neural Networks, Computer Constitutive Androstane Receptor Support Vector Machine
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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 ...
Humans Pan troglodytes Structural Homology, Protein Macaca Gorilla gorilla Epitopes Alleles Histocompatibility Antigens Class I Protein Binding Mice Ligands Machine Learning Gene Expression Oligopeptides Binding Sites Software Protein Interaction Domains and Motifs Animals
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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...
Ligands Humans Transcription Factors Protein Binding Protein Conformation Software Pyrazines Molecular Docking Simulation Nuclear Proteins
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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...
Machine Learning Algorithms Prospective Studies Molecular Docking Simulation Humans Ligands Binding Sites HSP90 Heat-Shock Proteins Protein Binding Computational Biology Proteins
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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...
Ligands Computer Graphics Neural Networks, Computer Machine Learning Molecular Structure Pharmaceutical Preparations Computer-Aided Design Drug Design
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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...
Amino Acid Motifs Receptors, GABA-A Machine Learning Databases, Protein Algorithms Amino Acids Support Vector Machine Brain Ligands Decision Making Models, Statistical Humans Computer Simulation Decision Trees Reproducibility of Results Computational Biology
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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 ...
Informatics Ligands Software Molecular Weight Automation Algorithms Organic Chemicals Support Vector Machine
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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...
Protein Conformation Substrate Specificity Protease Inhibitors User-Computer Interface Databases, Pharmaceutical Drug Evaluation, Preclinical Models, Molecular Machine Learning Small Molecule Libraries Ligands Peptides Peptide Hydrolases
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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 ...
User-Computer Interface Protein Binding Support Vector Machine Ligands Crystallography, X-Ray Drug Evaluation, Preclinical Protein Kinases Molecular Docking Simulation Small Molecule Libraries
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