ConformerDiff-Adaptive: A Sampler-Agnostic Wrapper for Per-Molecule NFE Allocation in Conformer Generation

Sampling from diffusion-based molecular conformer generators is expensive: models such as GeoDiff use thousands of denoising steps for every molecule, irrespective of conformational complexity. We propose ConformerDiff-Adaptive, a backbone-frozen wrapper that allocates a global number-of-function-evaluations (NFE) budget heterogeneously across a batch of molecules to maximize aggregate coverage. The wrapper has three complementary components: an exact multi-choice knapsack allocator (which beats a greedy heuristic by 19.7 points on average); a calibrated monotone heavy-atom-count prior that carries the dominant in-distribution signal; and a low-weight LightGBM residual on cheap RDKit features for molecule-specific structure beyond size. On GeoDiff's published test_data_1k benchmark with 3-seed averaged ground truth, our wrapper outperforms the strongest baseline by +23.0 to +35.9 points absolute COV-R across the compute-constrained regime (20-50% of the published NFE budget, all p < 0.001, paired bootstrap). At 80% budget and above the advantage disappears: allocation degenerates to a uniform schedule, and we show this is intrinsic rather than a limitation of our predictor. The wrapper is sampler-agnostic across three substantially different samplers (Langevin dynamics, DDIM-style generalized, and DPM-Solver-2) and backbone-agnostic across GeoDiff and AGDIFF. The pipeline modifies no backbone parameters and requires about 3 GPU-hours of one-time per-deployment calibration. Code, trained predictors and calibration labels are available at https://anonymous.4open.science/r/ConformerDiff-B5F0