Classifier-free graph diffusion for molecular property targeting
Matteo Ninniri Marco Podda Davide Bacciu
This work focuses on the task of property targeting: that is, generating
molecules conditioned on target chemical properties to expedite candidate
screening for novel drug and materials development. DiGress is a recent
diffusion model for molecular graphs whose distinctive feature is allowing
property targeting through classifier-based (CB) guidance. While CB guidance
may work to generate molecular-like graphs, we hint at the fact that its
assumptions apply poorly to the chemical domain. Based on this insight we
propose a classifier-free DiGress (FreeGress), which works by directly
injecting the conditioning information into the training process. CF guidance
is convenient given its less stringent assumptions and since it does not
require to train an auxiliary property regressor, thus halving the number of
trainable parameters in the model. We empirically show that our model yields up
to 79% improvement in Mean Absolute Error with respect to DiGress on property
targeting tasks on QM9 and ZINC-250k benchmarks. As an additional contribution,
we propose a simple yet powerful approach to improve chemical validity of
generated samples, based on the observation that certain chemical properties
such as molecular weight correlate with the number of atoms in molecules.