Drug discovery is adapting to novel technologies such as data science, informatics, and
artificial intelligence (AI) to accelerate effective treatment development while reducing costs …

For a new molecular entity (NME) to become a drug, it is not only essential to have the right
biological activity also be safe and efficient, but it is also required to have a favorable …

ADMETlab 3.0 is the second updated version of the web server that provides a
comprehensive and efficient platform for evaluating ADMET-related parameters as well as …

Because undesirable pharmacokinetics and toxicity of candidate compounds are the main
reasons for the failure of drug development, it has been widely recognized that absorption …

A closed-loop, autonomous molecular discovery platform driven by integrated machine
learning tools was developed to accelerate the design of molecules with desired properties …

Solubility plays a critical role in many aspects of research (drugs to materials). Solubility
parameters are very useful for selecting appropriate solvents/non-solvents for various …

Organic solar cells are the most promising candidates for future commercialization. This goal
can be quickly achieved by designing new materials and predicting their performance …

The power conversion efficiency (PCE) of organic solar cells (OSCs) is increasing
continuously, however, commercialization is far from being achieved due to the very high …

Adverse drug reactions (ADRs) and drug-induced toxicity are major challenges in drug
discovery, threatening patient safety and dramatically increasing healthcare expenditures …

We present a group contribution method (SoluteGC) and a machine learning model
(SoluteML) to predict the Abraham solute parameters, as well as a machine learning model …