Machine learning has a vast range of applications. In particular, advanced machine learning
methods are routinely and increasingly used in quantitative structure activity relationship …

Background In-silico quantitative structure–activity relationship (QSAR) models based tools
are widely used to screen huge databases of compounds in order to determine the …

Background Several QSAR methodology developments have shown promise in recent
years. These include the consensus approach to generate the final prediction of a model …

Quantitative structure–activity relationship (QSAR) modeling is a well-known in silico
technique with extensive applications in several major fields such as drug design, predictive …

Background Research efforts in the field of descriptive and predictive Quantitative Structure-
Activity Relationships or Quantitative Structure–Property Relationships produce around one …

Quantitative structure-activity relationship (QSAR) is a statistical modelling approach that
can be used in drug discovery, environmental fate modeling, property and activity prediction …

A key challenge in the field of Quantitative Structure Activity Relationships (QSAR) is how to
effectively treat experimental error in the training and evaluation of computational models. It …

Publisher Summary This chapter provides an overview of machine learning techniques that
have recently appeared in the computational chemistry literature. The natural fit between …

After nearly five decades “in the making”, QSAR modeling has established itself as one of
the major computational molecular modeling methodologies. As any mature research …

QSAR models capable of predicting biological, toxicity, and pharmacokinetic properties
were widely used to search lead bioactive molecules in chemical databases. The dataset's …