RNA-EFM : Energy based Flow Matching for Protein-conditioned RNA Sequence-Structure Co-design

Abrar Rahman Abir, Liqing Zhang

Track: Machine learning: computational method and/or computational results

Nature Biotechnology: No

Keywords: Flow Matching, RNA Design, Generative Model

Abstract: Ribonucleic acids (RNAs) are essential biomolecules involved in gene regulation and molecular recognition. Designing RNA molecules that can bind specific protein targets is crucial for therapeutic applications but remains challenging due to the structural flexibility of RNA and the laborious nature of experimental techniques. We propose RNA-EFM, a novel Energy-based Flow Matching framework for protein conditioned RNA sequence and structure co-design. RNA-EFM integrates biophysical constraints, including the Lennard-Jones potential and sequence-derived free energy, to generate low-energy and biologically plausible RNA conformations. By incorporating an idempotent refinement strategy for iterative structural correction, RNA-EFM consistently outperforms existing baselines, achieving lower RMSD, higher lDDT, and superior sequence recovery across multiple evaluation splits.

Anonymization: This submission has been anonymized for double-blind review via the removal of identifying information such as names, affiliations, and identifying URLs.

Presenter: ~Abrar_Rahman_Abir1

Format: Yes, the presenting author will attend in person if this work is accepted to the workshop.

Funding: Yes, the presenting author of this submission falls under ICLR’s funding aims, and funding would significantly impact their ability to attend the workshop in person.

Submission Number: 36