Lambda reports EGInterpolator reduces molecular dynamics gaps on DRUGS
Lambda described EGInterpolator as ICLR 2026 work with Stanford that learns molecular structure from abundant conformer data before learning motion from scarce MD trajectories. On the DRUGS benchmark, it said the method reduced the gap to reference simulations by 73% for bond angles, 78% for bond lengths, and 24% for torsional motion.