The Evolution of Generative Chemistry in Medicinal Chemistry

Abstract

Generative chemistry is an emerging discipline that utilizes generative artificial intelligence (AI) models for the automated de novo design of small molecules. By learning patterns from existing chemical data, these models can generate novel structures with desired properties, thereby accelerating drug discovery. However, a significant gap remains between the potential of AI and its successful implementation in practical pharmaceutical applications. This review covers various infrastructural aspects of generative chemistry, including molecular representation, databases, and diverse model architectures such as generative adversarial networks, variational autoencoders, and diffusion models. Furthermore, key challenges associated with data quality, model selection, and synthesis feasibility are critically discussed. The review highlights that generative chemistry has evolved beyond simple structure generation to encompass the entire molecular design pipeline, including automated synthesis planning, retrosynthesis prediction, and multi‐objective optimization. Additionally, the selection of the most suitable model depends on specific objectives and the quality and diversity of the dataset, rather than a single superior architecture. Overall, a critical perspective is provided on how generative models are shaping the future of rational and reliable drug design.