ESDM: Efficient Spectrum-Conditioned Molecular Diffusion Models for molecular structure elucidation

De novo molecular generation from tandem mass spectrometry (MS/MS) has become an important direction for unknown compound identification and molecular discovery. Diffusion-based methods have demonstrated strong molecular reconstruction capability by modeling conditional graph generation processes. However, their inference procedure still relies on long sequential reverse diffusion, resulting in substantial computational overhead and limited practical applicability in high-throughput scenarios. To address this issue, we propose ESDM, an efficient spectrum-conditioned molecular diffusion framework integrating Sampling Acceleration and Sampling Corrector. During training, a lightweight Conditional Timestep Tuner is optimized jointly with the diffusion denoising objective to learn spectrum-conditioned timestep preferences. During inference, the learned timestep allocation guides Sampling Acceleration to construct reduced reverse diffusion trajectories through non-uniform timestep scheduling and jump-step transitions, while Sampling Corrector improves sampling stability via lightweight posterior refinement on edge logits. Experimental results on the NPLIB1 and MassSpecGym benchmarks demonstrate that ESDM significantly reduces inference cost, with inference-time reductions of up to 58.9% on NPLIB1 and 62.8% on MassSpecGym, while maintaining competitive molecular reconstruction performance. Compared with DiffMS, ESDM achieves a favorable balance between generation quality and computational efficiency under multiple accelerated sampling configurations, highlighting its practical value for large-scale molecular generation and high-throughput molecular annotation.