RNAmigos2: accelerated structure-based RNA virtual screening with deep graph learning
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 targ...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-03-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-57852-0 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849389824824311808 |
|---|---|
| author | Juan G. Carvajal-Patiño Vincent Mallet David Becerra Luis Fernando Niño Vasquez Carlos Oliver Jérôme Waldispühl |
| author_facet | Juan G. Carvajal-Patiño Vincent Mallet David Becerra Luis Fernando Niño Vasquez Carlos Oliver Jérôme Waldispühl |
| author_sort | Juan G. Carvajal-Patiño |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d227bb65640b4d0b87b27b5e14f9dc1a |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-d227bb65640b4d0b87b27b5e14f9dc1a2025-08-20T03:41:50ZengNature PortfolioNature Communications2041-17232025-03-0116111210.1038/s41467-025-57852-0RNAmigos2: accelerated structure-based RNA virtual screening with deep graph learningJuan G. Carvajal-Patiño0Vincent Mallet1David Becerra2Luis Fernando Niño Vasquez3Carlos Oliver4Jérôme Waldispühl5School of Computer Science, McGill UniversityLIX, Ecole PolytechniqueSchool of Computer Science, McGill UniversityUniversidad Nacional de Colombia - Sede Bogotá - Facultad de Ingeniería - Depto. de Ingeniería de Sistemas e IndustrialMax Planck Institute of BiochemistrySchool of Computer Science, McGill UniversityAbstract 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.https://doi.org/10.1038/s41467-025-57852-0 |
| spellingShingle | Juan G. Carvajal-Patiño Vincent Mallet David Becerra Luis Fernando Niño Vasquez Carlos Oliver Jérôme Waldispühl RNAmigos2: accelerated structure-based RNA virtual screening with deep graph learning Nature Communications |
| title | RNAmigos2: accelerated structure-based RNA virtual screening with deep graph learning |
| title_full | RNAmigos2: accelerated structure-based RNA virtual screening with deep graph learning |
| title_fullStr | RNAmigos2: accelerated structure-based RNA virtual screening with deep graph learning |
| title_full_unstemmed | RNAmigos2: accelerated structure-based RNA virtual screening with deep graph learning |
| title_short | RNAmigos2: accelerated structure-based RNA virtual screening with deep graph learning |
| title_sort | rnamigos2 accelerated structure based rna virtual screening with deep graph learning |
| url | https://doi.org/10.1038/s41467-025-57852-0 |
| work_keys_str_mv | AT juangcarvajalpatino rnamigos2acceleratedstructurebasedrnavirtualscreeningwithdeepgraphlearning AT vincentmallet rnamigos2acceleratedstructurebasedrnavirtualscreeningwithdeepgraphlearning AT davidbecerra rnamigos2acceleratedstructurebasedrnavirtualscreeningwithdeepgraphlearning AT luisfernandoninovasquez rnamigos2acceleratedstructurebasedrnavirtualscreeningwithdeepgraphlearning AT carlosoliver rnamigos2acceleratedstructurebasedrnavirtualscreeningwithdeepgraphlearning AT jeromewaldispuhl rnamigos2acceleratedstructurebasedrnavirtualscreeningwithdeepgraphlearning |