Characterization of small molecule latent spaces derived from a variety of generative molecule creation approaches

Abstract

The relatively recent emergence of deep learning-based AI has opened the door to generation of fit-for-purpose molecules for drug discovery and other applications. The advantage of generation over prediction lies in the vast chemical space that can be considered, going beyond molecules known to exist or which have been explicitly imagined or enumerated. Here we determine the size, global diversity, local diversity, and fit for purpose of molecules generated for a common target using a variety of generative approaches which vary with respect to number of targets included in the training and use or absence of 3D protein structural information. The results provide guidance on the use of specific approaches for lead finding or lead optimization.