Limitations of representation learning in small molecule property prediction.

Representation learning is making inroads into drug discovery. A study in emphasizes multiple limitations in property prediction. The results suggest that continued research and improvements are required for this specific area that coalesces machine learning and molecular medicine.

The rise of automated curiosity-driven discoveries in chemistry.

The quest for generating novel chemistry knowledge is critical in scientific advancement, and machine learning (ML) has emerged as an asset in this pursuit. Through interpolation among learned patterns, ML can tackle tasks that were previously deemed demanding to machines. This distinctive capacity of ML provides invaluable aid to bench chemists in their daily work.

A focus on the use of real-world datasets for yield prediction.

The prediction of reaction yields remains a challenging task for machine learning (ML), given the vast search spaces and absence of robust training data. Wiest, Chawla (https://doi.org/10.