De novo molecular design and generative models

Molecular design strategies are integral to therapeutic progress in drug discovery. Computational approaches for de novo molecular design have been developed over the past three decades and, recently, thanks in part to advances in machine learning (ML) and artificial intelligence (AI), the drug discovery field has gained practical experience. Here, we review these learnings and present de novo approaches according to the coarseness of their molecular representation: that is, whether molecular design is modeled on an atom-based, fragment-based, or reaction-based paradigm. Furthermore, we emphasize the value of strong benchmarks, describe the main challenges to using these methods in practice, and provide a viewpoint on further opportunities for exploration and challenges to be tackled in the upcoming years.

Automated summary

This review discusses the application of computational methods in drug discovery, focusing on molecular design strategies and de novo approaches. The methods of molecular design are categorized based on the coarseness of molecular representation, such as atom-based, fragment-based, or reaction-based paradigms. The importance of strong benchmarks, challenges in practical application, and potential opportunities for exploration and growth in the field are highlighted.