Motivation Large-scale prediction of drug–target affinity (DTA) plays an important role in
drug discovery. In recent years, machine learning algorithms have made great progress in …

Many bioactive peptides demonstrated therapeutic effects over complicated diseases, such
as antiviral, antibacterial, anticancer, etc. It is possible to generate a large number of …

Abstract Machine learning (ML) applications have become ubiquitous in all fields of
research including protein science and engineering. Apart from protein structure and …

Motivation Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the
structures of large protein complexes. Picking single protein particles from cryo-EM …

With the increase in machine learning (ML) applications in different domains, incentives for
deceiving these models have reached more than ever. As data is the core backbone of ML …

The task of identifying protein–ligand interactions (PLIs) plays a prominent role in the field of
drug discovery. However, it is infeasible to identify potential PLIs via costly and laborious in …

Accurately predicting drug-target binding affinity plays a vital role in accelerating drug
discovery. Many computational approaches have been proposed due to costly and time …

Targeted therapies in cancer treatment can improve in vivo efficacy and reduce adverse
effects by altering the tissue exposure of specific biomolecules. However, there are still large …

Molecular dynamics simulation is a widely employed computational technique for studying
the dynamic behavior of molecular systems over time. By simulating macromolecular …

Developing new drugs is too expensive and time-consuming. Accurately predicting the
interaction between drugs and targets will likely change how the drug is discovered …