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 …

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

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 …

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 …

Quantitative Structure–Activity Relationship (QSAR) is a popular approach developed to
correlate chemical molecules with their biological activities based on their chemical …

Prediction of chemical bioactivity and physical properties has been one of the most
important applications of statistical and more recently, machine learning and artificial …

Background Structure–activity relationship models have been used to gain insight into
chemical and physical processes in biomedicine, toxicology, biotechnology, etc. for almost a …

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

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

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