The accurate prediction of drug-target affinity (DTA) is a crucial step in drug discovery and
design. Traditional experiments are very expensive and time-consuming. Recently, deep …

Graph neural networks (GNNs) process the graph-structured data using neural networks
and have proven successful in various graph processing tasks. Currently, graph pooling …

Background Drug discovery is a complex and expensive procedure involving several timely
and costly phases through which new potential pharmaceutical compounds must pass to get …

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 …

Develo** new pharmaceutical compounds is a lengthy, costly, and intensive process. In
recent years, the development of Artificial Intelligence (AI), Machine Learning (ML), and …

Accurate calculation of drug-target affinity (DTA) is crucial for various applications in the
pharmaceutical industry, including drug screening, design, and repurposing. However …

Identifying new compounds that interact with a target is a crucial time-limiting step in the
initial phases of drug discovery. Compound–protein complex structure-based affinity …

Introduction Predicting TCR–peptide binding is a complex and significant computational
problem in systems immunology. During the past decade, a series of computational methods …

Bioactivity refers to the ability of a substance to induce biological effects within living
systems, often describing the influence of molecules, drugs, or chemicals on organisms. In …

Drug discovery is costly and time consuming, and modern drug discovery endeavors are
progressively reliant on computational methodologies, aiming to mitigate temporal and …