Due to the massive data sets available for drug candidates, modern drug discovery has
advanced to the big data era. Central to this shift is the development of artificial intelligence …

Highlights•Drug discovery has been advanced to a big data era with a large amount of
public data sources available.•Ten V features (volume, velocity, variety, veracity, validity …

Machine learning has become a crucial tool in drug discovery and chemistry at large, eg, to
predict molecular properties, such as bioactivity, with high accuracy. However, activity …

At the end of her academic career, the author summarizes the main aspects of QSAR
modeling, giving comments and suggestions according to her 23 years' experience in QSAR …

The ability of molecular property prediction is of great significance to drug discovery, human
health, and environmental protection. Despite considerable efforts, quantitative prediction of …

Quantitative structure–activity relationship modeling is one of the major computational tools
employed in medicinal chemistry. However, throughout its entire history it has drawn both …

The accurate modeling and prediction of small molecule properties and bioactivities depend
on the critical choice of molecular representation. Decades of informatics-driven research …

QSARINS (QSAR‐INSUBRIA) is a new software for the development and validation of
multiple linear regression Quantitative Structure‐Activity Relationship (QSAR) models by …

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 …

Molecular modelers and cheminformaticians typically analyze experimental data generated
by other scientists. Consequently, when it comes to data accuracy, cheminformaticians are …