This work presents a novel and complementary metric for evaluating deep molecular generative models. The performance of seven different molecular generative models was compared by calculating what fraction of the structures, ring systems, and functional groups could be reproduced from the largely unseen reference set when using only a small fraction of GDB-13 for training.
Recommended citation: Zhang, Jie et al. (2021). “Comparative study of deep generative models on chemical space coverage.” J. Chem. Inf. Model.