Abstract Machine learning and artificial intelligence approaches have revolutionized
multiple disciplines, including toxicology. This review summarizes representative recent …

In 2016, the Frank R. Lautenberg Chemical Safety for the 21st Century Act became the first
US legislation to advance chemical safety evaluations by utilizing novel testing approaches …

In qualitative or quantitative studies of structure–activity relationships (SARs), machine
learning (ML) models are trained to recognize structural patterns that differentiate between …

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 …

Background In recent years, research in artificial neural networks has resurged, now under
the deep-learning umbrella, and grown extremely popular. Recently reported success of DL …

This paper is an overview of the most significant and impactful interpretation approaches of
quantitative structure–activity relationship (QSAR) models, their development, and …

The'quality'of small-molecule drug candidates, encompassing aspects including their
potency, selectivity and ADMET (absorption, distribution, metabolism, excretion and toxicity) …

The prediction of compound properties from chemical structure is a main task for machine
learning (ML) in medicinal chemistry. ML is often applied to large data sets in applications …

This paper is focused on modern approaches to machine learning, most of which are as yet
used infrequently or not at all in chemoinformatics. Machine learning methods are …

Deep learning approaches, especially convolutional neural network (CNN) models, have
achieved excellent performances in vibrational spectral analysis. The critical drawback of …