Towards Sub-Second Molecular Docking as a Structural Primitive: A Quantized Consistency Diffusion Framework

Abstract (source: OpenReview · © authors)

Agent-centered scientific discovery is turning scientific models into always-on computational infrastructure. In this paradigm, AI agents coordinate tools, interpret feedback, and drive high-frequency research loops, requiring domain models that are both accurate and callable in real time. Molecular docking exposes this bottleneck: it provides essential structural feedback for drug discovery, yet current high-fidelity docking and co-folding models remain limited by iterative generative refinement and heavy computation. We present a compute-efficient co-folding framework that turns molecular docking into a sub-second structural primitive. Because docking methods operate under different levels of structural prior, we report accuracy under information-level-matched protocols, comparing blind settings with blind generative methods and interface-informed settings with surface- or interface-informed baselines. Our framework combines two ideas. First, Progressive Consistency Regularization (PCR) compresses diffusion dynamics into reliable few-step inference through reconstruction-anchored consistency tuning. Second, Residual-Safe Quantization preserves high-fidelity residual streams and geometry-sensitive operations in BF16 while quantizing selected compute-intensive linear transformations. Our model achieves state-of-the-art docking accuracy under the matched interface-informed protocol, reports blind docking performance separately under the matched blind protocol, and generates five conformations for a representative 256-token complex in 0.17 seconds on a single NVIDIA H20 GPU, delivering a $>300\times$ speedup over AlphaFold3 under the benchmarked setting. Together, these results move molecular docking from an offline generative simulator toward a real-time structural primitive for agent-centered drug discovery.

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