Deep neural networks offer considerable potential across a range of applications, from
advanced manufacturing to autonomous cars. A clear trend in deep neural networks is the …

Plenty of microRNAs (miRNAs) were discovered at a rapid pace in plants, green algae,
viruses and animals. As one of the most important components in the cell, miRNAs play a …

Advancement in the field of computational research has made it possible for the in silico
methods to offer significant benefits to both regulatory needs and requirements for risk …

Decades of nanotoxicology research have generated extensive and diverse data sets.
However, data is not equal to information. The question is how to extract critical information …

Toxicity prediction is a critical step in the drug discovery process that helps identify and
prioritize compounds with the greatest potential for safe and effective use in humans, while …

Machine learning (ML) and deep learning (DL) are a subset of artificial intelligence (AI) that
can automatically learn from data and can perform tasks such as predictions and decision …

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

The carcinogenicity of drugs can have a serious impact on human health, so carcinogenicity
testing of new compounds is very necessary before being put on the market. Currently, many …

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

We present a proof of concept that machine learning techniques can be used to predict the
properties of CNOHF energetic molecules from their molecular structures. We focus on a …