  Deep generative diffusion models are a promising avenue for 3D de novo
molecular design in materials science and drug discovery. However, their
utility is still limited by suboptimal performance on large molecular
structures and limited training data. To address this gap, we explore the
design space of E(3)-equivariant diffusion models, focusing on previously
unexplored areas. Our extensive comparative analysis evaluates the interplay
between continuous and discrete state spaces. From this investigation, we
present the EQGAT-diff model, which consistently outperforms established models
for the QM9 and GEOM-Drugs datasets. Significantly, EQGAT-diff takes continuous
atom positions, while chemical elements and bond types are categorical and uses
time-dependent loss weighting, substantially increasing training convergence,
the quality of generated samples, and inference time. We also showcase that
including chemically motivated additional features like hybridization states in
the diffusion process enhances the validity of generated molecules. To further
strengthen the applicability of diffusion models to limited training data, we
investigate the transferability of EQGAT-diff trained on the large PubChem3D
dataset with implicit hydrogen atoms to target different data distributions.
Fine-tuning EQGAT-diff for just a few iterations shows an efficient
distribution shift, further improving performance throughout data sets.
Finally, we test our model on the Crossdocked data set for structure-based de
novo ligand generation, underlining the importance of our findings showing
state-of-the-art performance on Vina docking scores.
