Within drug discovery, the goal of AI scientists and cheminformaticians is to help identify
molecular starting points that will develop into safe and efficacious drugs while reducing …

Learning effective molecular feature representation to facilitate molecular property prediction
is of great significance for drug discovery. Recently, there has been a surge of interest in pre …

The precise prediction of molecular properties is essential for advancements in drug
development, particularly in virtual screening and compound optimization. The recent …

DNA Encoding, as a key step in DNA storage, plays an important role in reading and writing
accuracy and the storage error rate. However, currently, the encoding efficiency is not high …

Abstract Machine learning (ML) based interatomic potentials have transformed the field of
atomistic materials modelling. However, ML potentials depend critically on the quality and …

Optimizing the activities and properties of lead compounds is an essential step in the drug
discovery process. Despite recent advances in machine learning-aided drug discovery, most …

Effective transfer learning for molecular property prediction has shown considerable strength
in addressing insufficient labeled molecules. Many existing methods either disregard the …

Today, machine learning methods are widely employed in drug discovery. However, the
chronic lack of data continues to hamper their further development, validation, and …

Deep transfer learning has become increasingly prevalent in various fields such as industry
and medical science in recent years. To ensure the successful implementation of target …

The prediction of molecular properties is a crucial task in the field of drug discovery.
Computational methods that can accurately predict molecular properties can significantly …