AIMC Topic: Quantitative Structure-Activity Relationship

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In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning.

Combinatorial chemistry & high throughput screening Jan 1, 2018
BACKGROUND: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors.
Drug Discovery Support Vector Machine Anticoagulants Models, Molecular Molecular Structure Linear Models Humans Machine Learning Quantitative Structure-Activity Relationship Computer Simulation Thermodynamics Thrombin
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Intelligently Applying Artificial Intelligence in Chemoinformatics.

Current topics in medicinal chemistry Jan 1, 2018
The intertwining of chemoinformatics with artificial intelligence (AI) has given a tremendous fillip to the field of drug discovery. With the rapid growth of chemical data from high throughput screening and combinatorial synthesis, AI has become an i...
Drug Discovery Data Mining High-Throughput Screening Assays Quantitative Structure-Activity Relationship Artificial Intelligence Machine Learning Algorithms
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Predicting Inhibitors for Multidrug Resistance Associated Protein-2 Transporter by Machine Learning Approach.

Combinatorial chemistry & high throughput screening Jan 1, 2018
BACKGROUND: The efflux transporter multidrug resistance associated protein-2 belongs to ATP-binding cassette superfamily which plays an important role in multidrug resistance and drugdrug interactions. Efflux transporters are considered to be importa...
Multidrug Resistance-Associated Protein 2 Quantitative Structure-Activity Relationship Databases, Pharmaceutical Multidrug Resistance-Associated Proteins Drug Discovery Support Vector Machine Machine Learning Molecular Structure Algorithms
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Approaching Pharmacological Space: Events and Components.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2018
With a view to introducing the concept of pharmacological space and its potential applications in investigating and predicting the toxic mechanisms of xenobiotics, this opening chapter describes the logical relations between conformational behavior, ...
Molecular Conformation Quantitative Structure-Activity Relationship Static Electricity Molecular Docking Simulation Drug Discovery Fuzzy Logic Molecular Dynamics Simulation Ligands
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Machine Learning Methods in Computational Toxicology.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2018
Various methods of machine learning, supervised and unsupervised, linear and nonlinear, classification and regression, in combination with various types of molecular descriptors, both "handcrafted" and "data-driven," are considered in the context of ...
Neural Networks, Computer Machine Learning Algorithms Linear Models Toxicology Quantitative Structure-Activity Relationship Computer Simulation Deep Learning Support Vector Machine
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[Construction of a High-precision Chemical Prediction System Using Human ESCs].

Yakugaku zasshi : Journal of the Pharmaceutical Society of Japan Jan 1, 2018
　Toxicity prediction based on stem cells and tissue derived from stem cells plays a very important role in the fields of biomedicine and pharmacology. Here we report on qRT-PCR data obtained by exposing 20 compounds to human embryonic stem (ES) cells...
Carcinogens Benzhydryl Compounds Permethrin Humans Embryonic Stem Cells Quantitative Structure-Activity Relationship Neurotoxins Support Vector Machine Toxicity Tests Phenols
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Machine Learning-Based Modeling of Drug Toxicity.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2018
Toxicity is an important reason for the failure of drug research and development (R&D). The traditional experimental testings for chemical toxicity profile are costly and time-consuming. Therefore, it is attractive to develop the effective and accura...
Molecular Structure Humans Internet Machine Learning Quantitative Structure-Activity Relationship Software Pharmaceutical Preparations Models, Biological Drug Design Sequence Homology, Amino Acid Drug-Related Side Effects and Adverse Reactions Carcinogenesis Ether-A-Go-Go Potassium Channels Ligands
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Editor's Highlight: Identification of Any Structure-Specific Hepatotoxic Potential of Different Pyrrolizidine Alkaloids Using Random Forests and Artificial Neural Networks.

Toxicological sciences : an official journal of the Society of Toxicology Dec 1, 2017
Pyrrolizidine alkaloids (PAs) are characteristic metabolites of some plant families and form a powerful defense mechanism against herbivores. More than 600 different PAs are known. PAs are ester alkaloids composed of a necine base and a necic acid, w...
Cyclic N-Oxides Algorithms Heterocyclic Compounds, 2-Ring Databases, Factual Pyrrolizidine Alkaloids Reproducibility of Results Humans Neural Networks, Computer Machine Learning Risk Assessment Liver Molecular Structure Quantitative Structure-Activity Relationship Dicarboxylic Acids Chemical and Drug Induced Liver Injury
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In silico prediction of multiple-category classification model for cytochrome P450 inhibitors and non-inhibitors using machine-learning method.

SAR and QSAR in environmental research Oct 1, 2017
The cytochrome P450 (CYP) enzyme superfamily is involved in phase I metabolism which chemically modifies a variety of substrates via oxidative reactions to make them more water-soluble and easier to eliminate. Inhibition of these enzymes leads to und...
Cytochrome P-450 Enzyme Inhibitors Computer Simulation Humans Bayes Theorem Machine Learning Quantitative Structure-Activity Relationship
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How good are publicly available web services that predict bioactivity profiles for drug repurposing?

SAR and QSAR in environmental research Oct 1, 2017
Drug repurposing provides a non-laborious and less expensive way for finding new human medicines. Computational assessment of bioactivity profiles shed light on the hidden pharmacological potential of the launched drugs. Currently, several freely ava...
Machine Learning Drug Repositioning Software Internet Quantitative Structure-Activity Relationship
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