Efficient drug delivery systems (DDSs) play a pivotal role in ensuring pharmaceuticals'
targeted and effective administration. However, the intricate interplay between drug …

Drug–drug interactions (DDI) are a critical aspect of drug research that can have adverse
effects on patients and can lead to serious consequences. Predicting these events …

Artificial intelligence (AI) in the form of deep learning has promise for drug discovery and
chemical biology, for example, to predict protein structure and molecular bioactivity, plan …

Lung cancer has been the most common and the leading cause of cancer deaths globally.
Besides clinicopathological observations and traditional molecular tests, the advent of …

In recent years, the rapid growth of biological data has increased interest in using
bioinformatics to analyze and interpret this data. Proteomics, which studies the structure …

Unexpected drug–drug interactions (DDIs) are important issues for both pharmaceutical
research and clinical applications due to the high risk of causing severe adverse drug …

Drug development is expensive, time-consuming, and has a high failure rate. In recent
years, artificial intelligence (AI) has emerged as a transformative tool in drug discovery …

The field of drug discovery has experienced a remarkable transformation with the advent of
artificial intelligence (AI) and machine learning (ML) technologies. However, as these AI and …

Recently, artificial intelligence (AI) techniques have been increasingly used to overcome the
challenges in drug discovery. Although traditional AI techniques generally have high …

Co-administration of two or more drugs simultaneously can result in adverse drug reactions.
Identifying drug-drug interactions (DDIs) is necessary, especially for drug development and …