An effective fragment-based dual conditional diffusion framework for molecular generation.

Area of Science:

• Computational chemistry
• Drug discovery
• Molecular modeling

Background:

• Fragment-based molecular generation is key in structure-based drug design (SBDD).
• Existing methods struggle to balance 3D structural constraints with chemical plausibility.
• This limitation stems from treating molecular scaffolds and R-groups indistinctly.

Purpose of the Study:

• To introduce a novel dual conditional diffusion framework, FDC-Diff, for fragment-based molecular generation.
• To integrate chemical priors and structural cues for enhanced molecule generation.
• To improve the generation of molecules that are chemically valid, synthetically feasible, and pharmacologically relevant.

Main Methods:

• FDC-Diff decomposes molecule generation into two stages: scaffold construction and R-group elaboration.
• The first stage builds a spatially constrained scaffold for global topology.
• The second stage adds R-groups for local semantics and property refinement, using curated reaction rules and a physics-chemistry-inspired refinement step.

Main Results:

• FDC-Diff achieves state-of-the-art performance on SBDD benchmarks.
• The model successfully generates chemically valid and spatially compatible molecules.
• FDC-Diff demonstrates superior pharmacological relevance compared to existing methods.

Conclusions:

• FDC-Diff offers a significant advancement in fragment-based molecular generation for SBDD.
• The framework's ability to handle distinct roles of scaffolds and R-groups enhances molecular design.
• FDC-Diff shows potential as a practical tool for accelerating drug discovery.