Fragment-Driven Progressive Alternating Diffusion for De Novo Molecular Design.

High reliability and creativity remain key goals for AI-driven de novo molecule design. In this work, we propose a fragment-driven progressive alternating diffusion (FDPAD) framework in a coarse-to-fine generation mode. By modeling molecules as fragment-structured graphs, FDPAD entails a progressive discrete diffusion process by randomly walking some sequences of fragment-structured units (FSU), thereby mitigating combinatorial complexities and facilitating the synthesis of intricate macroscopic structures. To delve deeper internal structures of FSU, we design two distinct diffusion processes: the conditioned fragment diffusion (CFD) and the inter-fragment bond diffusion (IBD). In CFD, a string-based diffusion probability model is proposed to enrich the diversity of fragments, leveraging the partially-generated molecule as condition. And in IBD, a graph-based diffusion model upon bond-related atom graph is proposed to boost the prediction of intricate chemical bond connections among molecular fragments. Through the interleaving of CFD and IBD processes, our model outperforms state-of-the-art algorithms in de novo molecular generation, particularly in generating novel and unique molecules.