RNA design & discovery
Predict secondary structures and ensembles, analyze RNA-RNA interactions, solve inverse-folding problems, and model RNA-containing biomolecular complexes.
Trusted by researchers at leading biotech, pharmaceutical, academic, and materials organizations
Sequence, structure, and interaction
Use thermodynamic ensembles for secondary structure questions, inverse folding for sequence design, and all-atom co-folding when molecular partners matter.
Estimate minimum-free-energy structures, base-pair probabilities, local accessibility, and alternative conformations for an RNA sequence.
Model RNA-RNA pairing, duplex formation, and interaction accessibility before evaluating a larger biomolecular assembly.
Generate sequences for a target fold, perform 3D inverse folding, and predict complexes containing RNA, proteins, ligands, or ions.
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Match the method to the structural scale, preserve temperature and energy assumptions, and experimentally validate designed sequences and interactions.
Predict RNA secondary structure and pairing probabilities.
Model the joint structure of two interacting RNAs.
Estimate local accessibility across an RNA sequence.
Evaluate RNA interaction energy and accessibility.
Design sequences for three-dimensional RNA structures.
Design sequences for a target secondary structure.
From the Neurosnap blog
Structure guide
Review minimum-free-energy structures, partition functions, and base-pair probabilities for RNA analysis.
Read articleDesign guide
Learn how three-dimensional structural context supports RNA inverse-folding workflows.
Read articleSecurity & privacy
Submitted data and generated results remain confidential, with ownership retained by your team.
Only you and authorized colleagues can access submitted data and results.
Your results and intellectual property remain yours. No royalties or equity.
Platform controls help protect sensitive computational research.
Onboarding and integration support help organizations scale confidently.
Design the RNA system
Explore ensembles, design target folds, and model molecular context before selecting constructs for experimental validation.
