Molecular Conformer Sampling Beyond Generations

Sampling the low-energy conformational states available to a molecule is a fundamental problem in computational chemistry and drug discovery, since molecular properties and downstream design decisions depend on the relevant three-dimensional geometries rather than on a single structure. Recent diffusion and flow-based generative models can efficiently sample conformer candidates from molecular graphs, but under a fixed output budget their samples do not cover the full set of relevant conformational states of a molecule, while practical benchmarks and downstream applications require a small ensemble that is both diverse and physically plausible. We present a post-generation selection framework that turns candidate pools from frozen, pre-trained generators into compact conformer ensembles without any retraining or fine-tuning of the underlying generator. The framework offers two selectors that share the same candidate pool and the same learned reference-likelihood classifier: CovSelect, a coverage-maximizing selector that greedily reaches as many distinct reference states as the pool allows, and RefSelect, a tunable selector that trades some of that coverage for higher precision and, at its balanced setting, matches or exceeds strong generative baselines on both measures at once. On the full GEOM-DRUGS test set, CovSelect reaches 91.33% mean recall (98.36% median), outperforming every raw generator we compare against, and RefSelect at its balanced setting exceeds the strongest raw baseline, DMT-L, on both recall and precision. Finally, across several out of distribution benchmarks (QMugs, pepconf, and Platinum), CovSelect substantially outperforms all raw generative baselines in recall and precision.