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 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 2

SUBMITTER: Carvajal-Patino JG 

PROVIDER: S-EPMC11928640 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature