A Multidimensional Taxonomy of Generative Artificial Intelligence Approaches for Drug Discovery
Trinidad Crozes, Vir Sabando, Axel J. Soto, Ignacio Ponzoni · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · 2026
ABSTRACT Generative artificial intelligence (GAI) methods have shown strong potential to accelerate drug discovery projects and de novo drug design. Yet, the fast pace of new proposals and the complexity of existing GAI methods configures a difficult scenario for professionals that aim to understand and effectively exploit these new tools. These issues are further exacerbated due to the plethora of different architectures for generative models, molecular representations, generation objectives and evaluation metrics. Moreover, other relevant aspects for understanding the inner workings of these models and their applications are scarcely addressed in the literature. For this reason, we propose different dimensions to taxonomically organize the wide range of GAI approaches used for drug discovery. These dimensions include the underlying computational model, the representation of molecules, and the building block for molecular generation. We also describe advantages and limitations of each family of methods, along with a comprehensive survey of metrics to evaluate performances from multiple perspectives. Besides, we explore how the concept of applicability domain applies to conditional generative methods, particularly in terms of quantifying the uncertainty associated with the generated molecules. As a final contribution, we describe and classify different approaches currently used in GAI‐driven drug design to enhance explainability. Finally, we discuss open challenges to strengthen the adoption of these generative models within research and industry. This article is categorized under: Technologies > Machine Learning Application Areas > Health Care