MSF-HDiff: Molecular Semantic Field-Guided Multi-Agent Hierarchical Diffusion for Small-Molecule Antibiotic Discovery

The continued rise in antimicrobial resistance creates an urgent need to accelerate antibiotic discovery. Artificial intelligence (AI) has shown great promise in accelerating small-molecule antibiotic discovery, specifically generative models that can design de novo small molecules. However, existing generative models often struggle to simultaneously satisfy molecular requirements while making targeted chemical modifications to optimize desired properties. To address this, we introduce MSF-HDiff, a closed-loop framework that combines multi-agent reasoning with hierarchically conditioned diffusion for de novo small-molecule antibiotic discovery. Our model features a novel framework that integrates hierarchical condition search, transferable molecular semantic fields for property-directed chemical guidance, and reference-based feasibility control during diffusion sampling. This enables iterative molecular design that balances global antibiotic objectives with local chemical optimization. MSF-HDiff outperforms other generative models in generating de novo small molecules with strong antibacterial activity, favorable molecular properties, and high structural reliability as shown in Figure 1.