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

The discovery and development of new drugs are extremely long and costly processes.
Recent progress in artificial intelligence has made a positive impact on the drug …

Human immunodeficiency virus type 1 (HIV-1), a lentivirus that causes acquired
immunodeficiency syndrome (AIDS), poses a serious threat to global public health. Since …

Drug toxicity prediction is an important step in ensuring patient safety during drug design
studies. While traditional preclinical studies have historically relied on animal models to …

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 …

Large Language Models (LLMs) represent the recent success of deep learning in achieving
remarkable human-like predictive performance. It has become a mainstream strategy to …

Chemical space modelling has great importance in unveiling and visualising latent
information, which is critical in predictive toxicology related to drug discovery process. While …

Approved drugs for sale must be effective and safe, implying that the drug's advantages
outweigh its known harmful side effects. Side effects (SE) of drugs are one of the common …

Efficient target identification for bioactive compounds, including novel synthetic analogs, is
crucial for accelerating the drug discovery pipeline. However, the process of target …

Abstract Graph Neural Networks (GNNs) excel in compound property and activity prediction,
but the choice of molecular graph representations significantly influences model learning …