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Quantitative Structure-Activity Relationship

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Quantitative Structure Activity/Toxicity Relationship through Neural Networks for Drug Discovery or Regulatory Use.

Current topics in medicinal chemistry
Quantitative structure - activity relationship (QSAR) modelling is widely used in medicinal chemistry and regulatory decision making. The large amounts of data collected in recent years in materials and life sciences projects provide a solid foundati...

deepGraphh: AI-driven web service for graph-based quantitative structure-activity relationship analysis.

Briefings in bioinformatics
Artificial intelligence (AI)-based computational techniques allow rapid exploration of the chemical space. However, representation of the compounds into computational-compatible and detailed features is one of the crucial steps for quantitative struc...

Machine Learning and Artificial Intelligence in Toxicological Sciences.

Toxicological sciences : an official journal of the Society of Toxicology
Machine learning and artificial intelligence approaches have revolutionized multiple disciplines, including toxicology. This review summarizes representative recent applications of machine learning and artificial intelligence approaches in different ...

Polymer informatics for QSPR prediction of tensile mechanical properties. Case study: Strength at break.

The Journal of chemical physics
The artificial intelligence-based prediction of the mechanical properties derived from the tensile test plays a key role in assessing the application profile of new polymeric materials, especially in the design stage, prior to synthesis. This strateg...

Comparison of predictions of developmental toxicity for compounds of solvent data set.

SAR and QSAR in environmental research
We have considered a series of 235 compounds technically classified as solvents. Chemically, they belong to different classes. Their potential developmental toxicity was evaluated using two models available on platform VEGA HUB; model CAESAR and the ...

Machine Learning for In Silico ADMET Prediction.

Methods in molecular biology (Clifton, N.J.)
ADMET (absorption, distribution, metabolism, excretion, and toxicity) describes a drug molecule's pharmacokinetics and pharmacodynamics properties. ADMET profile of a bioactive compound can impact its efficacy and safety. Moreover, efficacy and safet...

Deep Neural Networks for QSAR.

Methods in molecular biology (Clifton, N.J.)
Quantitative structure-activity relationship (QSAR) models are routinely applied computational tools in the drug discovery process. QSAR models are regression or classification models that predict the biological activities of molecules based on the f...

Machine Learning Applied to the Modeling of Pharmacological and ADMET Endpoints.

Methods in molecular biology (Clifton, N.J.)
The well-known concept of quantitative structure-activity relationships (QSAR) has been gaining significant interest in the recent years. Data, descriptors, and algorithms are the main pillars to build useful models that support more efficient drug d...