From structures to dynamics: scaling AI for molecular dynamics in drug discovery

Molecular dynamics simulations help drug discovery researchers evaluate how drug candidates and protein targets behave over time, but their high computational cost limits their use at scale. EGInterpolator, developed with Stanford University and accepted at ICLR 2026, addresses the shortage of expensive molecular dynamics trajectory data by first pretraining a diffusion model on abundant static three-dimensional molecular conformer data, then fine-tuning it on dynamics data to generate trajectories. On the DRUGS benchmark, the approach outperformed the prior GeoTDM method, reducing divergence from reference simulations by approximately 73% for bond angles, 78% for bond lengths, and 24% for torsional motion, while ablation tests indicated that structural pretraining was central to these gains. The method was also extended to tetrapeptides and protein monomers, suggesting a data-efficient path for AI-driven molecular simulation in drug discovery, supported by GPU-based infrastructure such as Lambda’s NVIDIA-powered training environment.

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