The identification of interactions between drugs/compounds and their targets is crucial for
the development of new drugs. In vitro screening experiments (ie bioassays) are frequently …

Drug discovery is a cost and time-intensive process that is often assisted by computational
methods, such as virtual screening, to speed up and guide the design of new compounds …

Predicting drug–target interaction is key for drug discovery. Recent deep learning-based
methods show promising performance, but two challenges remain: how to explicitly model …

The development of new drugs is costly, time consuming and often accompanied with safety
issues. Drug repurposing can avoid the expensive and lengthy process of drug development …

Motivation Drug–target interaction (DTI) prediction is a foundational task for in-silico drug
discovery, which is costly and time-consuming due to the need of experimental search over …

Recent advancements in biological research leverage the integration of molecules, proteins,
and natural language to enhance drug discovery. However, current models exhibit several …

Structural and physiochemical descriptors extracted from sequence data have been widely
used to represent sequences and predict structural, functional, expression and interaction …

Identifying interactions between known drugs and targets is a major challenge in drug
repositioning. In silico prediction of drug–target interaction (DTI) can speed up the expensive …

Discovery and development of biopeptides are time‐consuming, laborious, and dependent
on various factors. Data‐driven computational methods, especially machine learning (ML) …

With the explosive growth of biological sequences generated in the post-genomic era, one
of the most challenging problems in bioinformatics and computational biology is to …