AIMC Topic: Drug Discovery

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Classification of toxicity effects of biotransformed hepatic drugs using whale optimized support vector machines.

Journal of biomedical informatics Mar 8, 2017
Measuring toxicity is an important step in drug development. Nevertheless, the current experimental methods used to estimate the drug toxicity are expensive and time-consuming, indicating that they are not suitable for large-scale evaluation of drug ...
Forecasting Drug Discovery Drug-Related Side Effects and Adverse Reactions Chemical and Drug Induced Liver Injury Support Vector Machine Humans Algorithms
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Active learning for computational chemogenomics.

Future medicinal chemistry Mar 6, 2017
AIM: Computational chemogenomics models the compound-protein interaction space, typically for drug discovery, where existing methods predominantly either incorporate increasing numbers of bioactivity samples or focus on specific subfamilies of protei...
Drug Discovery Genomics Computational Biology Machine Learning Computer Simulation Ligands Databases, Chemical Binding Sites Proteins Models, Chemical
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Practical application of the Average Information Content Maximization (AIC-MAX) algorithm: selection of the most important structural features for serotonin receptor ligands.

Molecular diversity Feb 9, 2017
The Average Information Content Maximization algorithm (AIC-MAX) based on mutual information maximization was recently introduced to select the most discriminatory features. Here, this methodology was applied to select the most significant bits from ...
Machine Learning Drug Discovery Structure-Activity Relationship Receptors, Serotonin Ligands Informatics
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Continuous Adaptive Population Reduction (CAPR) for Differential Evolution Optimization.

SLAS technology Jan 31, 2017
Differential evolution (DE) has been applied extensively in drug combination optimization studies in the past decade. It allows for identification of desired drug combinations with minimal experimental effort. This article proposes an adaptive popula...
Humans Computer Simulation Drug Discovery Drug Therapy, Combination Artificial Intelligence Algorithms
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Classification of sphingosine kinase inhibitors using counter propagation artificial neural networks: A systematic route for designing selective SphK inhibitors.

SAR and QSAR in environmental research Jan 25, 2017
Accurate and robust classification models for describing and predicting the activity of 330 chemicals that are sphingosine kinase 1 (SphK1) and/or sphingosine kinase 2 (SphK2) inhibitors were derived. The classification models developed in this work ...
Drug Discovery ROC Curve Enzyme Inhibitors Algorithms Computer Simulation Chemistry, Pharmaceutical Phosphotransferases (Alcohol Group Acceptor) Neural Networks, Computer
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Biologically Relevant Heterogeneity: Metrics and Practical Insights.

SLAS discovery : advancing life sciences R & D Jan 6, 2017
Heterogeneity is a fundamental property of biological systems at all scales that must be addressed in a wide range of biomedical applications, including basic biomedical research, drug discovery, diagnostics, and the implementation of precision medic...
Flow Cytometry Reference Values Histocytochemistry Genetic Heterogeneity Imaging, Three-Dimensional Humans Systems Biology Machine Learning Drug Discovery Decision Making Precision Medicine Single-Cell Analysis Neoplasms Decision Support Techniques
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Prediction of blood-brain barrier permeability of organic compounds.

Doklady. Biochemistry and biophysics Nov 6, 2016
Using fragmental descriptors and artificial neural networks, a predictive model of the relationship between the structure of organic compounds and their blood-brain barrier permeability was constructed and the structural factors affecting the readine...
Neural Networks, Computer Blood-Brain Barrier Organic Chemicals Models, Neurological Molecular Structure Animals Models, Cardiovascular Models, Chemical Capillary Permeability Drug Discovery
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The Next Era: Deep Learning in Pharmaceutical Research.

Pharmaceutical research Sep 6, 2016
Over the past decade we have witnessed the increasing sophistication of machine learning algorithms applied in daily use from internet searches, voice recognition, social network software to machine vision software in cameras, phones, robots and self...
Software Algorithms Pharmaceutical Research Drug Discovery Neural Networks, Computer Machine Learning
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Prediction of Activity Cliffs Using Condensed Graphs of Reaction Representations, Descriptor Recombination, Support Vector Machine Classification, and Support Vector Regression.

Journal of chemical information and modeling Aug 26, 2016
Activity cliffs (ACs) are formed by structurally similar compounds with large differences in activity. Accordingly, ACs are of high interest for the exploration of structure-activity relationships (SARs). ACs reveal small chemical modifications that ...
Informatics Drug Interactions Quantitative Structure-Activity Relationship Drug Discovery Support Vector Machine
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ADMET Evaluation in Drug Discovery. 16. Predicting hERG Blockers by Combining Multiple Pharmacophores and Machine Learning Approaches.

Molecular pharmaceutics Jul 18, 2016
Blockade of human ether-à-go-go related gene (hERG) channel by compounds may lead to drug-induced QT prolongation, arrhythmia, and Torsades de Pointes (TdP), and therefore reliable prediction of hERG liability in the early stages of drug design is qu...
Bayes Theorem Support Vector Machine Machine Learning Cluster Analysis Quantitative Structure-Activity Relationship Drug Discovery Potassium Channel Blockers Models, Molecular
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