The therapeutic approach of targeted protein degradation (TPD) is gaining momentum due
to its potentially superior effects compared with protein inhibition. Recent advancements in …

Drug discovery aims to select proper targets and drug candidates to address unmet clinical
needs. The end-to-end drug discovery process includes all stages of drug discovery from …

Abstract Accurately predicting ADMET (Absorption, Distribution, Metabolism, Excretion, and
Toxicity) properties early in drug development is essential for selecting compounds with …

Oral medicines represent the largest pharmaceutical market area. To achieve a therapeutic
effect, a drug must penetrate the intestinal walls, the main absorption site for orally delivered …

Predicting solubility of small molecules is a very difficult undertaking due to the lack of
reliable and consistent experimental solubility data. It is well known that for a molecule in a …

Accurate prediction of thermodynamic solubility by machine learning remains a challenge.
Recent models often display good performances, but their reliability may be deceiving when …

Solubility is crucial for redox flow batteries because it affects their energy density. A data-
driven approach based on artificial intelligence/machine learning models can accelerate the …

The solubility of chemical substances in water is a critical parameter in pharmaceutical
development, environmental chemistry, agrochemistry, and other fields; however, accurately …

This paper sheds light on the shift that is taking place from the practice of 'coding', namely
developing programs as conventional in the software community, to the practice of 'curing' …

Hyperparameter optimization is very frequently employed in machine learning. However, an
optimization of a large space of parameters could result in overfitting of models. In recent …