Accurate prediction of drug–target interactions (DTI) is crucial for drug discovery. Recently,
deep learning (DL) models for show promising performance for DTI prediction. However …

This review attempts a critical stock-taking of the current state of the science aimed at
predicting structural features of proteins from their amino acid sequences. At the primary …

If protein structure prediction methods are to make any impact on the impending onerous
task of analyzing the large numbers of unknown protein sequences generated by the …

The identification of drug-target interactions has great significance for pharmaceutical
scientific research. Since traditional experimental methods identifying drug-target …

The complex language of eukaryotic gene expression remains incompletely understood.
Despite the importance suggested by many proteins variants statistically associated with …

Sequence-derived structural and physicochemical features have frequently been used in the
development of statistical learning models for predicting proteins and peptides of different …

Nuclear receptors are key transcription factors that regulate crucial gene networks
responsible for cell growth, differentiation, and homeostasis. Nuclear receptors form a …

Automated prediction of subcellular localization of proteins is an important step in the
functional annotation of genomes. The existing subcellular localization prediction methods …

Protein–protein interactions (PPIs) are essential for various biological processes and
diseases. However, most existing computational methods for identifying PPI modulators …

Prediction of the drug–target affinity (DTA) plays an important role in drug discovery. Existing
deep learning methods for DTA prediction typically leverage a single modality, namely …