Exposure to environmental organic pollutants has triggered significant ecological impacts
and adverse health outcomes, which have been received substantial and increasing …

Across the spectrum of industrial sectors, including pharmaceuticals, chemicals, personal
care products, food additives and their associated regulatory agencies, there is a need to …

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 …

Chemical toxicity evaluations for drugs, consumer products, and environmental chemicals
have a critical impact on human health. Traditional animal models to evaluate chemical …

Machine learning classification algorithms are widely used for the prediction and
classification of the different properties of molecules such as toxicity or biological activity …

The application of Explainable Artificial Intelligence (XAI) in the field of chemistry has
garnered growing interest for its potential to justify the prediction of black-box machine …

Lipophilicity is one of the key properties of a potential drug that determines the solubility, the
ability to penetrate through cell barriers, and transport to the molecular target. It affects …

Abstract Machine learning (ML) is becoming capable of transforming biomolecular
interaction description and calculation, promising an impact on molecular and drug design …

Mycobacterium tuberculosis is the bacterial strain that causes tuberculosis (TB). However,
multidrug-resistant and extensively drug-resistant tuberculosis are significant obstacles to …

Four decades of the Human Genome Project and its consequences have shown how the
entrepreneurial state, through significant investment into science, can drive scientific …