RNAmigos2: accelerated structure-based RNA virtual screening with deep graph learning

Juan G. Carvajal-Patiño, Vincent Mallet, David Becerra, Luis Fernando Niño Vasquez, Carlos Oliver () and Jérôme Waldispühl ()
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Juan G. Carvajal-Patiño: McGill University
Vincent Mallet: Ecole Polytechnique
David Becerra: McGill University
Luis Fernando Niño Vasquez: Universidad Nacional de Colombia - Sede Bogotá - Facultad de Ingeniería - Depto. de Ingeniería de Sistemas e Industrial
Carlos Oliver: Max Planck Institute of Biochemistry
Jérôme Waldispühl: McGill University

Nature Communications, 2025, vol. 16, issue 1, 1-12

Abstract: Abstract RNAs are a vast reservoir of untapped drug targets. Structure-based virtual screening (VS) identifies candidate molecules by leveraging binding site information, traditionally using molecular docking simulations. However, docking struggles to scale with large compound libraries and RNA targets. Machine learning offers a solution but remains underdeveloped for RNA due to limited data and practical evaluations. We introduce a data-driven VS pipeline tailored for RNA, utilizing coarse-grained 3D modeling, synthetic data augmentation, and RNA-specific self-supervision. Our model achieves a 10,000x speedup over docking while ranking active compounds in the top 2.8% on structurally distinct test sets. It is robust to binding site variations and successfully screens unseen RNA riboswitches in a 20,000-compound in-vitro microarray, with a mean enrichment factor of 2.93 at 1%. This marks the first experimentally validated success of structure-based deep learning for RNA VS.

Date: 2025
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DOI: 10.1038/s41467-025-57852-0

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