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

By combining metal nodes with organic linkers we can potentially synthesize millions of
possible metal–organic frameworks (MOFs). The fact that we have so many materials opens …

Owing to the rapid developments to improve the accuracy and efficiency of both
experimental and computational investigative methodologies, the massive amounts of data …

The annotation of small molecules remains a major challenge in untargeted mass
spectrometry-based metabolomics. We here critically discuss structured elucidation …

Abstract Structure-based virtual screening (SBVS) via docking has been used to discover
active molecules for a range of therapeutic targets. Chemical and protein data sets that …

At the end of her academic career, the author summarizes the main aspects of QSAR
modeling, giving comments and suggestions according to her 23 years' experience in QSAR …

The collection of chemical structure information and associated experimental data for
quantitative structure–activity/property relationship (QSAR/QSPR) modeling is facilitated by …

Decades of nanotoxicology research have generated extensive and diverse data sets.
However, data is not equal to information. The question is how to extract critical information …

Generative chemical language models (CLMs) can be used for de novo molecular structure
generation by learning from a textual representation of molecules. Here, we show that hybrid …

The n-octanol/water partition coefficient (log P o/w) is a key physicochemical parameter for
drug discovery, design, and development. Here, we present a physics-based approach that …