AI for molecular dynamics has a data problem. The trajectories you need to train on are expensive. EGInterpolator (ICLR 2026, with Stanford) learns molecular structure first from abundant conformer data, then uses scarce MD data to learn motion. On the DRUGS b

AI for molecular dynamics has a data problem. The trajectories you need to train on are expensive. EGInterpolator (ICLR 2026, with Stanford) learns molecular structure first from abundant conformer data, then uses scarce MD data to learn motion. On the DRUGS benchmark, it reduced the gap to reference simulations by 73% for bond angles, 78% for bond lengths, and 24% for torsional motion. The structure-first ablation also matters. Removing pretraining increased mean JSD from 0.173 to 0.332 for bond angles and from 0.142 to 0.386 for bond lengths. 📷