MARD-Mol: a hybrid autoregressive-diffusion paradigm for coarse-grained molecular modeling
Abstract Motivation Deep generative models have transformed drug molecule generation. However, molecules exhibit complex hierarchical structures, requiring models to simultaneously balance macroscopic topological coherence and microscopic chemical self-consistency. Although autoregressive (AR) and discrete diffusion paradigms are highly complementary, integrating their advantages within a unified architecture remains severely limited by traditional “atom-by-atom” fine-grained modeling. Results We propose MARD-Mol, a hybrid AR-diffusion framework based on motif-inspired units. By elevating the representation granularity from atoms to motif-inspired units and introducing a dual-stream hierarchical attention mechanism, it couples inter-unit AR global scaffold planning with intra-unit discrete diffusion generation. To support goal-directed drug discovery, we reformulate property optimization into an iterative “diagnose-and-repair” process, enabling targeted optimization of defective motifs while preserving the global scaffold. Extensive experiments demonstrate that MARD-Mol achieves an 86.0% Quality score in de novo generation and exhibits superior performance in fragment-constrained and multi-objective optimization, establishing a new paradigm for high-quality drug design. Availability and implementation The source code and datasets used in this study are available at GitHub: https://github.com/CSUBioGroup/MARD-Mol.