Recently, using artificial intelligence (AI) in drug discovery has received much attention
since it significantly shortens the time and cost of developing new drugs. Deep learning (DL) …

Deep learning has disrupted nearly every field of research, including those of direct
importance to drug discovery, such as medicinal chemistry and pharmacology. This …

Predicting how a drug-like molecule binds to a specific protein target is a core problem in
drug discovery. An extremely fast computational binding method would enable key …

Illuminating interactions between proteins and small drug molecules is a long-standing
challenge in the field of drug discovery. Despite the importance of understanding these …

Drug repurposing or repositioning is a technique whereby existing drugs are used to treat
emerging and challenging diseases, including COVID-19. Drug repurposing has become a …

Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space,
perform particularly well in various tasks that process graph structure data. With the rapid …

Graph machine learning (GML) is receiving growing interest within the pharmaceutical and
biotechnology industries for its ability to model biomolecular structures, the functional …

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

Accurate quantification of protein–ligand interactions remains a key challenge to structure-
based drug design. However, traditional machine learning (ML)-based methods based on …

The development of new drugs is costly, time consuming and often accompanied with safety
issues. Drug repurposing can avoid the expensive and lengthy process of drug development …