Multi-Granularity Score-Based Generative Framework Enables Efficient Inverse Design of Complex Organics

Efficiently retrieving an enormous chemical library to design targeted molecules is crucial for accelerating drug discovery, organic chemistry, and optoelectronic materials. Despite the emergence of generative models to produce novel drug-like molecules, in a more realistic scenario, the complexity of functional groups (e.g., pyrene, acenaphthylene, and bridged-ring systems) and extensive molecular scaffolds remain challenging obstacles for the generation of complex organics. The former often demands an extra learning process, and the latter requires expensive computational resources. To address these, we propose OrgMol-Design, a multi-granularity framework for efficiently designing complex organics. It is composed of a score-based generative model via fragment prior for diverse coarse-grained scaffold generation and a chemical-rule-aware scoring model for fine-grained molecular structure design, circumventing the difficulty of intricate substructure learning without losing connection details among fragments. Our approach achieves state-of-the-art performance on two challenging real-world benchmarks across broader scientific domains, outperforming advanced molecule generative models.