Developing new drugs remains prohibitively expensive, time-consuming, and often involves
safety issues. Accurate prediction of drug-target interactions (DTIs) can guide the drug …

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 …

Abstract Protein Structure Prediction is a central topic in Structural Bioinformatics. Since
the'60s statistical methods, followed by increasingly complex Machine Learning and recently …

Computer-aided drug design uses high-performance computers to simulate the tasks in drug
design, which is a promising research area. Drug–target affinity (DTA) prediction is the most …

Predicting residue‐residue distance relationships (eg, contacts) has become the key
direction to advance protein structure prediction since 2014 CASP11 experiment, while …

Obtaining an accurate description of protein structure is a fundamental step toward
understanding the underpinning of biology. Although recent advances in experimental …

Protein structure prediction is a way to bridge the sequence-structure gap, one of the main
challenges in computational biology and chemistry. Predicting any protein's accurate …

The screening of compound–protein interactions (CPIs) is one of the most crucial steps in
finding hit and lead compounds. Deep learning (DL) methods for CPI prediction can address …

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 …

Computationally predicting drug-target binding affinity (DTA) has attracted increasing
attention due to its benefit for accelerating drug discovery. Currently, numerous deep …