  A well-known limitation of existing molecular generative models is that the
generated molecules highly resemble those in the training set. To generate
truly novel molecules that may have even better properties for de novo drug
discovery, more powerful exploration in the chemical space is necessary. To
this end, we propose Molecular Out-Of-distribution Diffusion(MOOD), a
score-based diffusion scheme that incorporates out-of-distribution (OOD)
control in the generative stochastic differential equation (SDE) with simple
control of a hyperparameter, thus requires no additional costs. Since some
novel molecules may not meet the basic requirements of real-world drugs, MOOD
performs conditional generation by utilizing the gradients from a property
predictor that guides the reverse-time diffusion process to high-scoring
regions according to target properties such as protein-ligand interactions,
drug-likeness, and synthesizability. This allows MOOD to search for novel and
meaningful molecules rather than generating unseen yet trivial ones. We
experimentally validate that MOOD is able to explore the chemical space beyond
the training distribution, generating molecules that outscore ones found with
existing methods, and even the top 0.01% of the original training pool. Our
code is available at https://github.com/SeulLee05/MOOD.
