Millions of experimental animals are widely used in the assessment of toxicological or
biological effects of manufactured nanomaterials in medical technology. However, the …

The field of computational chemistry has seen a significant increase in the integration of
machine learning concepts and algorithms. In this Perspective, we surveyed 179 open …

Commercial chemicals are used extensively across urban centres worldwide, posing a
potential exposure risk to 4.2 billion people. Harmful chemicals are often assessed on the …

p K a is an important property in the lead optimization process since the charge state of a
molecule in physiologic pH plays a critical role in its biological activity, solubility, membrane …

Harmful cyanobacterial blooms, which frequently contain toxic secondary metabolites, are
reported in aquatic environments around the world. More than two thousand cyanobacterial …

While many approaches to predict aqueous pKa values exist, the fast and accurate
prediction of non‐aqueous pKa values is still challenging. Based on the iBonD experimental …

Motivation The absorption, distribution, metabolism, excretion, and toxicity (ADMET) of drugs
plays a key role in determining which among the potential candidates are to be prioritized. In …

Machine Learning (ML) is increasingly applied to fill data gaps in assessments to quantify
impacts associated with chemical emissions and chemicals in products. However, the …

In this review, we describe the current status and challenges in applying machine-learning
techniques to the analysis and prediction of pharmacokinetic data. The theory of …

Integrating scientific principles into machine learning models to enhance their predictive
performance and generalizability is a central challenge in the development of AI for Science …