Ainin Sofia Jusoh, Muhammad Akmal Remli, Mohd Saberi Mohamad, Tristan Cazenave, Chin Siok Fong
Generative Artificial Intelligence (Generative AI) is transforming drug discovery by enabling advanced analysis of complex biological and chemical data. This review explores key Generative AI models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), flow-based models and Transformer-based models, with Transformers gaining prominence due to the abundance of text-based biological data and the success of language models like ChatGPT. The paper discusses molecular representations, performance evaluation metrics, and current trends in Generative AI-driven drug discovery, such as protein-protein interactions (PPIs), drug-target interactions (DTIs) and de-novo drug design. However, these approaches face significant challenges, including applicability domain issues, lack of interpretability, data scarcity, novelty, scalability, computational resource limitations, and the absence of standardized evaluation metrics. These challenges hinder model performance, complicate decision-making, and limit the generation of novel and viable drug candidates. To address these issues, strategies such as hybrid models, integration of multiomics datasets, explainable AI (XAI) techniques, data augmentation, transfer learning, and cloud-based solutions are proposed. Additionally, a curated list of databases supporting drug discovery research is provided. The review concludes by emphasizing the need for optimized AI models, robust validation methods, interdisciplinary collaboration, and future academic efforts to fully realize the potential of Generative AI in advancing drug discovery. © 2025 Elsevier Masson SAS
Institute for Artificial Intelligence and Big Data, Universiti Malaysia Kelantan, Kota Bharu, Kelantan, 16100, Malaysia; Faculty of Data Science and Computing, Universiti Malaysia Kelantan, Kota Bharu, Kelantan, 16100, Malaysia; Health Data Science Lab, Department of Genetics and Genomics, College of Medical and Health Sciences, United Arab Emirates University, Al Ain, 15551, United Arab Emirates; Faculty of Engineering and Technology, Multimedia University, Melaka, 75450, Malaysia; Department of Biosystems Engineering, Faculty of Agricultural Technology, Universitas Brawijaya, East Java, Malang, 65145, Indonesia; LAMSADE, Université Paris Dauphine - PSL, Paris, France; UKM Medical Molecular Biology Institute (UMBI), Kuala Lumpur, 56000, Malaysia
