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bioRxiv · 10.64898/2026.06.10.731383

MAHLER: Integrating Metadynamics and Inverse Folding to Predict Antibody-Antigen Kinetics

Teng, D.·Pitman, M.·Jha, P. K.·Sood, A.·Rufa, D.·Ryczko, K.·Bortolato, A.·Tiwary, P.
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Abstract

Binding kinetics are crucial for antibody function, shaping pharmacokinetics and in vivo efficacy beyond what equilibrium affinity captures. We present "Metadynamics-Anchored Hybrid Learning for Engineering off-Rates (MAHLER)", a fully open-source machine learning/physics hybrid method that predicts relative antibody-antigen residence times at scale. Incorporating inverse-folding models into molecular dynamics simulations, MAHLER shows first-in-class screening-grade accuracy in calculating relative antibody-antigen dissociation kinetics across a family of point mutants. After initial antigen-specific setup, each prediction takes only 4 minutes on a single NVIDIA A100 GPU, compared to days even with already enhanced molecular dynamics simulations. This provides practical kinetics-aware complement to current computational design approaches that focus primarily on binding affinity for antibody-antigen complexes.

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Teng, D., Pitman, M., Jha, P. K., Sood, A., Rufa, D., Ryczko, K., Bortolato, A., Tiwary, P.. 2026-06-12. MAHLER: Integrating Metadynamics and Inverse Folding to Predict Antibody-Antigen Kinetics. https://doi.org/10.64898/2026.06.10.731383

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