A Highly Efficient and Chemical Motif-Preserving Molecule Generation Platform
Peizhi Niu, Yu-Hsiang Wang, Vishal Rana, Chetan Rupakheti, Abhishek Pandey, Olgica Milenkovic
We introduce a new graph diffusion model for small drug molecule generation which simultaneously offers a $10$-fold reduction in the number of diffusion steps when compared to other existing methods, preservation of small molecule graph motifs via ring compression, and a $3$% improvement in SMILES validity over the DiGress model across all real-world molecule benchmarking datasets. Furthermore, our approach outperforms the state-of-the-art DeFoG method with respect to motif-conservation by roughly $4$%, as evidenced by high ChEMBL-likeness, QED and a newly introduced shingles distance score. The key ideas behind our approach are to use a combination of deterministic and random subgraph perturbations, so that the node and edge noise schedules are codependent; to modify the loss function of the training process in order to exploit the deterministic component of the schedule; and, to ``compress'' a collection of highly relevant carbon ring structures into supernodes in a way that allows for simple subsequent integration into the molecule scaffold.