Learning Molecules as Conformer Sets for Novel-Scaffold Selection at Billion Scale

Molecular representations based on 1D, 2D, or a single 3D structure do not capture the conformational flexibility that governs binding, limiting the discovery of active compounds with novel scaffolds. We propose ZAO, a foundation model that represents each molecule as a conformer set and encodes 10 conformers per molecule as a single input. It has 238 million parameters and is pretrained on a corpus of 11 billion conformers, 12.4 times the size of the largest published conformer corpus. ZAO's frozen representation is stable under conformer regeneration, remains effective in regions structurally distant from known active compounds, and is complementary to 2D fingerprints. We evaluate this representation prospectively for the discovery of novel active compounds. From a 1.1-billion-molecule virtual library, we select 9,220 compounds across 31 targets, score them with absolute binding free-energy calculations, and compare the outcome with that of a Boltz-2 plus ESPSim pipeline. To the best of our knowledge, no prior evaluation of a molecular foundation model has measured the ability to select novel active compounds at this scale. Median per-target hit rates (ΔG ≤ -10 kcal/mol) are 26.4% versus 16.0%, with ZAO's rate higher on 22 of 30 targets. ZAO's advantage is greatest on targets regarded as difficult, including protein-protein interactions. At ΔG ≤ -12 kcal/mol, the gap widens: 14.6% versus 5.2%. ZAO and Boltz-2 agree only weakly, and the hit rate for compound-target pairs that both methods judge active is 53.0%, indicating that conformer-set representations can complement structure-based scoring. We also release ZAO's weights.