Geometric deep learning (GDL) is based on neural network architectures that incorporate
and process symmetry information. GDL bears promise for molecular modelling applications …

Exploring drug–target interactions by biomedical experiments requires a lot of human,
financial, and material resources. To save time and cost to meet the needs of the present …

Drug-drug interaction (DDI) prediction is a challenging problem in pharmacology and
clinical application, and effectively identifying potential D-DIs during clinical trials is critical …

Expressive molecular representation plays critical roles in researching drug design, while
effective methods are beneficial to learning molecular representations and solving related …

Wastewater treatment plants (WWTPs) are energy-intensive facilities that play a critical role
in meeting stringent effluent quality regulations. Accurate prediction of energy consumption …

Deep learning (DL) provides opportunities for the identification of drug–target interactions
(DTIs). The challenges of applying DL lie primarily with the lack of interpretability. Also, most …

Introduction Golgi is one of the components of the inner membrane system in eukaryotic
cells. Its main function is to send the proteins involved in the synthesis of endoplasmic …

Molecular property prediction is an essential task in drug discovery. Most computational
approaches with deep learning techniques either focus on designing novel molecular …

Histone lysine crotonylation (Kcr) is a post-translational modification of histone proteins that
is involved in the regulation of gene transcription, acute and chronic kidney injury …

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 …