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 …

This chapter comprehensively explores the pivotal role of artificial intelligence (AI) in drug
discovery and development, encapsulating its potentials, methodologies, real-world …

Recent advances and achievements of artificial intelligence (AI) as well as deep and graph
learning models have established their usefulness in biomedical applications, especially in …

Drug repositioning, the strategy of redirecting existing drugs to new therapeutic purposes, is
pivotal in accelerating drug discovery. While many studies have engaged in modeling …

The high failure rate of clinical trials in Alzheimer's disease (AD) and AD-related dementia
(ADRD) is due to a lack of understanding of the pathophysiology of disease, and this deficit …

Within drug discovery, the goal of AI scientists and cheminformaticians is to help identify
molecular starting points that will develop into safe and efficacious drugs while reducing …

Deep learning methods have been considered promising for accelerating molecular
screening in drug discovery and material design. Due to the limited availability of labelled …

Deep learning has achieved remarkable success in learning representations for molecules,
which is crucial for various biochemical applications, ranging from property prediction to …

A ChatGPT-like system for drug compounds could be a game-changer in pharmaceutical
research, accelerating drug discovery, enhancing our understanding of structure-activity …

Conventional COVID-19 testing methods have some flaws: they are expensive and time-
consuming. Chest X-ray (CXR) diagnostic approaches can alleviate these flaws to some …