Deep learning bears promise for drug discovery, including advanced image analysis,
prediction of molecular structure and function, and automated generation of innovative …

The use and production of chemical compounds are subjected to strong legislative pressure.
Chemical toxicity and adverse effects derived from exposure to chemicals are key regulatory …

Drug discovery relies on the knowledge of not only drugs and targets, but also the
comparative agents and targets. These include poor binders and non-binders for developing …

Graph neural networks are able to solve certain drug discovery tasks such as molecular
property prediction and de novo molecule generation. However, these models are …

Radiotheranostics is a field of rapid growth with some approved treatments including 131 I
for thyroid cancer, 223 Ra for osseous metastases, 177 Lu-Dotatate for neuroendocrine …

QSAR. Despite this long tradition, machine learning methods gained substantial momentum
recently triggered by the success of deep learning in many application areas. 1 A wide …

SARS-CoV-2 is a highly contagious and dangerous coronavirus that first appeared in late
2019 causing COVID-19, a pandemic of acute respiratory illnesses that is still a threat to …

Drug discovery pipelines typically involve high‐throughput screening of large amounts of
compounds in a search of potential drugs candidates. As a chemical space of small organic …

Acinetobacter baumannii is an opportunistic ESKAPE pathogen which causes nosocomial
infections and can produce biofilms that act as resistant determinants. The role of quorum …

While a plethora of different protein–ligand docking protocols have been developed over the
past twenty years, their performances greatly depend on the provided input protein–ligand …