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

This post details the application of Lambda's GPU infrastructure and AI capabilities to accelerate molecular dynamics simulations for drug discovery. It introduces EGInterpolator, a two-stage AI model that first learns molecular structures from abundant static conformer data and then fine-tunes on scarce molecular dynamics trajectory data to predict molecular motion. This approach significantly improves the accuracy of predicting bond angles, bond lengths, and torsional motion compared to previous methods, reducing the Jensen-Shannon Divergence from reference MD simulations. The research highlights the data-efficient and scalable path toward AI-driven molecular dynamics, demonstrating the shift of molecular simulation towards GPU-native AI workloads.