Background Quantitative structure-activity relationship (QSAR) is a computational modeling
method for revealing relationships between structural properties of chemical compounds …

Aim: We introduce AutoQSAR, an automated machine-learning application to build, validate
and deploy quantitative structure–activity relationship (QSAR) models. Methodology/results …

Quantitative structure–activity relationship modeling using machine learning techniques
constitutes a complex computational problem, where the identification of the most …

Research on quantitative structure-activity relationships (QSAR) provides an effective
approach to determine new hits and promising lead compounds during drug discovery. In …

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

Effective drug discovery contributes to the treatment of numerous diseases but is limited by
high costs and long cycles. The Quantitative Structure–Activity Relationship (QSAR) method …

Although a wide variety of machine learning (ML) algorithms have been utilized to learn
quantitative structure–activity relationships (QSARs), there is no agreed single best …

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) methods are important for prediction of
biological effect of chemical compounds based on mathematical and statistical relations …

Advances in the development of high-throughput screening and automated chemistry have
rapidly accelerated the production of chemical and biological data, much of them freely …