Quantitative Structure-Activity Relationship

Shared Consensus Machine Learning Models for Predicting Blood Stage Malaria Inhibition.

Journal of chemical information and modeling Mar 17, 2017

The development of new antimalarial therapies is essential, and lowering the barrier of entry for the screening and discovery of new lead compound classes can spur drug development at organizations that may not have large compound screening libraries...

Machine Learning Temperature Drug Discovery Malaria Antimalarials Quantitative Structure-Activity Relationship Models, Theoretical Bayes Theorem ROC Curve

View on PubMed DOI

In Silico Prediction of Physicochemical Properties of Environmental Chemicals Using Molecular Fingerprints and Machine Learning.

Journal of chemical information and modeling Jan 9, 2017

There are little available toxicity data on the vast majority of chemicals in commerce. High-throughput screening (HTS) studies, such as those being carried out by the U.S. Environmental Protection Agency (EPA) ToxCast program in partnership with the...

Informatics Vapor Pressure Machine Learning Computer Simulation Quantitative Structure-Activity Relationship Chemical Phenomena Water Environmental Pollutants Transition Temperature Solubility

View on PubMed DOI

Distinguishing between expert and statistical systems for application under ICH M7.

Regulatory toxicology and pharmacology : RTP Jan 3, 2017

Models, Statistical Data Interpretation, Statistical Molecular Structure Animals Risk Assessment Quantitative Structure-Activity Relationship Drug Contamination Mutagenicity Tests Expert Systems Mutagens Humans Computer Simulation Reproducibility of Results Patient Safety Policy Making Drug and Narcotic Control

View on PubMed DOI

A new model of flavonoids affinity towards P-glycoprotein: genetic algorithm-support vector machine with features selected by a modified particle swarm optimization algorithm.

Archives of pharmacal research Dec 27, 2016

Flavonoids exhibit a high affinity for the purified cytosolic NBD (C-terminal nucleotide-binding domain) of P-glycoprotein (P-gp). To explore the affinity of flavonoids for P-gp, quantitative structure-activity relationship (QSAR) models were develop...

Quantitative Structure-Activity Relationship Protein Domains ATP Binding Cassette Transporter, Subfamily B, Member 1 Datasets as Topic Flavonoids Models, Chemical Protein Binding Support Vector Machine

View on PubMed DOI

The role of different sampling methods in improving biological activity prediction using deep belief network.

Journal of computational chemistry Nov 14, 2016

Thousands of molecules and descriptors are available for a medicinal chemist thanks to the technological advancements in different branches of chemistry. This fact as well as the correlation between them has raised new problems in quantitative struct...

Neural Networks, Computer Computer Simulation Drug Design Algorithms Models, Theoretical Quantitative Structure-Activity Relationship

View on PubMed DOI

Informing the Human Plasma Protein Binding of Environmental Chemicals by Machine Learning in the Pharmaceutical Space: Applicability Domain and Limits of Predictability.

Journal of chemical information and modeling Nov 3, 2016

The free fraction of a xenobiotic in plasma (F) is an important determinant of chemical adsorption, distribution, metabolism, elimination, and toxicity, yet experimental plasma protein binding data are scarce for environmentally relevant chemicals. T...

Pharmaceutical Preparations Humans Quantitative Structure-Activity Relationship Protein Binding Blood Proteins Environment Support Vector Machine

View on PubMed DOI

Performance of Deep and Shallow Neural Networks, the Universal Approximation Theorem, Activity Cliffs, and QSAR.

Molecular informatics Oct 26, 2016

Neural networks have generated valuable Quantitative Structure-Activity/Property Relationships (QSAR/QSPR) models for a wide variety of small molecules and materials properties. They have grown in sophistication and many of their initial problems hav...

Small Molecule Libraries Molecular Docking Simulation Drug Design Quantitative Structure-Activity Relationship Machine Learning

View on PubMed DOI

QSPR studies for predicting polarity parameter of organic compounds in methanol using support vector machine and enhanced replacement method.

SAR and QSAR in environmental research Sep 23, 2016

In the present work, enhanced replacement method (ERM) and support vector machine (SVM) were used for quantitative structure-property relationship (QSPR) studies of polarity parameter (p) of various organic compounds in methanol in reversed phase liq...

Algorithms Quantitative Structure-Activity Relationship Organic Chemicals Molecular Structure Methanol Support Vector Machine

View on PubMed DOI

Support vector regression-guided unravelling: antioxidant capacity and quantitative structure-activity relationship predict reduction and promotion effects of flavonoids on acrylamide formation.

Scientific reports Sep 2, 2016

We used the support vector regression (SVR) approach to predict and unravel reduction/promotion effect of characteristic flavonoids on the acrylamide formation under a low-moisture Maillard reaction system. Results demonstrated the reduction/promotio...

Glycosides Support Vector Machine Flavonoids Oxidation-Reduction Acrylamide Antioxidants Linear Models Maillard Reaction Quantitative Structure-Activity Relationship Hydroxyl Radical

View on PubMed DOI

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 Interactions Drug Discovery Quantitative Structure-Activity Relationship Support Vector Machine Informatics

View on PubMed DOI

AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Quantitative Structure-Activity Relationship

Shared Consensus Machine Learning Models for Predicting Blood Stage Malaria Inhibition.

In Silico Prediction of Physicochemical Properties of Environmental Chemicals Using Molecular Fingerprints and Machine Learning.

Distinguishing between expert and statistical systems for application under ICH M7.

A new model of flavonoids affinity towards P-glycoprotein: genetic algorithm-support vector machine with features selected by a modified particle swarm optimization algorithm.

The role of different sampling methods in improving biological activity prediction using deep belief network.

Informing the Human Plasma Protein Binding of Environmental Chemicals by Machine Learning in the Pharmaceutical Space: Applicability Domain and Limits of Predictability.

Performance of Deep and Shallow Neural Networks, the Universal Approximation Theorem, Activity Cliffs, and QSAR.

QSPR studies for predicting polarity parameter of organic compounds in methanol using support vector machine and enhanced replacement method.

Support vector regression-guided unravelling: antioxidant capacity and quantitative structure-activity relationship predict reduction and promotion effects of flavonoids on acrylamide formation.

Prediction of Activity Cliffs Using Condensed Graphs of Reaction Representations, Descriptor Recombination, Support Vector Machine Classification, and Support Vector Regression.

Popular Topics

Recent Journals