Recently, there has been a significant increase in the use of deep learning techniques in the
molecular sciences, which have shown high performance on datasets and the ability to …

Predicting drug–target affinity (DTA) is beneficial for accelerating drug discovery. Graph
neural networks (GNNs) have been widely used in DTA prediction. However, existing …

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

The prediction of molecules toxicity properties plays an crucial role in the realm of the drug
discovery, since it can swiftly screen out the expected drug moleculars. The conventional …

The accuracy of thermochemical prediction methods is strongly dependent on the size of the
set of training data. Group additivity is an interpretable modeling strategy that can be …

Graph neural networks (GNNs) are emerging in chemical engineering for the end-to-end
learning of physicochemical properties based on molecular graphs. A key element of GNNs …

The discovery of superior molecular solutions through computational methods is critical for
innovative technologies and their role in addressing pressing resources, health, and …

Graph neural networks based on deep learning methods have been extensively applied to
the molecular property prediction because of its powerful feature learning ability and good …

Accurate prediction of drug–target binding affinity (DTA) is essential in the field of drug
discovery. Recently, scientists have been attempting to utilize artificial intelligence prediction …

Structure-based virtual screening has been a crucial tool in drug discovery for decades.
However, as the chemical space expands, the existing structure-based virtual screening …