AIMC Topic: Drug Discovery

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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 Ligands Informatics Receptors, Serotonin Drug Discovery Structure-Activity Relationship
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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...
Drug Discovery Drug Therapy, Combination Humans Algorithms Computer Simulation Artificial Intelligence
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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 ...
Chemistry, Pharmaceutical Phosphotransferases (Alcohol Group Acceptor) ROC Curve Neural Networks, Computer Enzyme Inhibitors Computer Simulation Algorithms Drug Discovery
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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...
Humans Machine Learning Decision Support Techniques Drug Discovery Single-Cell Analysis Imaging, Three-Dimensional Precision Medicine Neoplasms Histocytochemistry Flow Cytometry Reference Values Systems Biology Genetic Heterogeneity Decision Making
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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...
Models, Neurological Molecular Structure Animals Capillary Permeability Models, Chemical Drug Discovery Models, Cardiovascular Neural Networks, Computer Organic Chemicals Blood-Brain Barrier
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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...
Pharmaceutical Research Software Algorithms 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 ...
Drug Discovery Support Vector Machine Quantitative Structure-Activity Relationship Informatics Drug Interactions
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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...
Models, Molecular Bayes Theorem Quantitative Structure-Activity Relationship Support Vector Machine Potassium Channel Blockers Machine Learning Cluster Analysis Drug Discovery
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Prediction of selective estrogen receptor beta agonist using open data and machine learning approach.

Drug design, development and therapy Jul 18, 2016
BACKGROUND: Estrogen receptors (ERs) are nuclear transcription factors that are involved in the regulation of many complex physiological processes in humans. ERs have been validated as important drug targets for the treatment of various diseases, inc...
Quantitative Structure-Activity Relationship Estrogen Receptor beta Databases, Factual Open Access Publishing Humans Computer Simulation Machine Learning Drug Discovery
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A renaissance of neural networks in drug discovery.

Expert opinion on drug discovery Jul 4, 2016
INTRODUCTION: Neural networks are becoming a very popular method for solving machine learning and artificial intelligence problems. The variety of neural network types and their application to drug discovery requires expert knowledge to choose the mo...
Structure-Activity Relationship Pharmaceutical Preparations Models, Biological Drug Design Drug Discovery Neural Networks, Computer Drug Delivery Systems Computer-Aided Design Humans
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