Recently, research on the development of artificial intelligence (AI)-based computational
toxicology models that predict toxicity without the use of animal testing has emerged …

During drug development, safety is always the most important issue, including a variety of
toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial …

We developed a web application structured in a machine learning and molecular fingerprint
algorithm for the automatic calculation of the reaction rate constant of the oxidative …

Toxicity prediction is very important to public health. Among its many applications, toxicity
prediction is essential to reduce the cost and labor of a drug's preclinical and clinical trials …

As expenditure on drug development increases exponentially, the overall drug discovery
process requires a sustainable revolution. Since artificial intelligence (AI) is leading the …

In recent times, machine learning has become increasingly prominent in predictive
toxicology as it has shifted from in vivo studies toward in silico studies. Currently, in vitro …

The ever-increasing number of chemicals has raised public concerns due to their adverse
effects on human health and the environment. To protect public health and the environment …

Industrial Applications of Machine Learning shows how machine learning can be applied to
address real-world problems in the fourth industrial revolution, and provides the required …

We discover a robust self-supervised strategy tailored toward molecular representations for
generative masked language models through a series of tailored, in-depth ablations. Using …

A machine-learning model trained on the whole Cambridge Structural Database was
developed to assist high-throughput cocrystal screening. With only 2D structures taken as …