How can we explore model uncertainty to improve generative molecular design?

In our recent paper entitled “Enhancing generative molecular design via uncertainty-guided fine-tuning of variational autoencoders” just featured on the cover page of RSC Molecular Systems Design & Engineering (MSDE)‘s Feb. 2026 issue, we propose an uncertainty-aware model optimization strategy.

Specifically, our strategy takes the latent points found by any latent space optimization approach and explores the uncertainty classes of generative molecular design models (in this case, variational autoencoders) through their low-dimensional active subspace to find the models that improve the properties of the molecules corresponding to those latent points.

Further details of our work can be found at: https://pubs.rsc.org/en/content/articlelanding/2025/me/d5me00081e