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 …

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 …

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

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

Over the last decade, we have witnessed deep learning's (DL) revolutionizing countless
fields ranging from image processing to natural language processing. While most of the …

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

The typical drug development process involves multiple stages, including target
identification, target validation, lead discovery, lead optimizations, ADMET evaluation, and …

Abstract Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) caused a global
healthcare crisis due to COVID-19 pandemic. In absence of efficacious antiviral drugs …

A utilização de algoritmos de aprendizagem de máquina tem aumentadoexponencialmente
na pesquisa científica, especialmente devido a avanços recentes emtécnicas de …