AI-Native RNA Design Platform

Design therapeutically active RNA at molecular precision

From sequence to 3D structure prediction, energy-based ranking and sequence optimisation — all in one AI-driven platform built for RNA drug discovery. EU based.

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3D
Structure Prediction
Sequence Optimisation
ΔG
Energy-Based Ranking

// Core capabilities

Three models, one unified pipeline

Our platform integrates structure prediction, thermodynamic ranking and inverse design into a single coherent workflow for RNA therapeutic discovery.

01Sequence to 3D structure prediction
Predict the full three-dimensional tertiary structure of any RNA sequence using our deep learning model trained on experimental structural data. Resolves atom-level geometry critical for drug binding site identification, siRNA off-target assessment and mRNA stability design.
02Energy-based candidate ranking
Score and rank candidate sequences by predicted thermodynamic stability and free energy profile. Our ensemble scoring model combines physics-based energy terms with learned representations to surface candidates most likely to maintain their designed structure.
03Sequence optimisation (inverse design)
Start from a target structure or functional constraint and work backwards to optimised sequences. Our gradient-based inverse design engine explores sequence space far more efficiently than combinatorial screening.
04API and workflow integration
Integrate RNAfold into your existing discovery pipeline via our REST API. Output formats include PDB-compatible coordinate files, dot-bracket notation, energy tables and ranked FASTA libraries.

// How it works

From sequence to ranked candidate in minutes

Our three-stage pipeline replaces weeks of manual iterative design with a computational workflow that runs in your browser or via API.

01

Input your sequence

Paste or upload any RNA sequence. Define optional constraints: GC content range, modification sites, target binding regions.

02

AI predicts and ranks

Our neural network predicts full 3D tertiary structure. The energy model scores thermodynamic stability. The inverse design module generates optimised sequence variants.

03

Export ranked candidates

Receive a ranked library of optimised sequences with 3D coordinate files, energy scores and confidence metrics, ready for wet lab validation.

// Why RNAfold

Built for RNA, not adapted from protein tools

Most computational biology platforms were designed for proteins and retrofitted for RNA. We built from the ground up for the unique properties of RNA therapeutics.

[deltaG]

Native thermodynamic modelling

Energy-based ranking uses RNA-specific free energy parameters, not generic molecular mechanics. More accurate stability prediction for therapeutic RNA modalities.

[3D]

Tertiary structure, not just secondary

Go beyond dot-bracket notation. Our model predicts full 3D coordinates, essential for drug binding site analysis and delivery optimisation.

[inv]

Inverse design built in

Design backwards from a structural target. Gradient-based optimisation explores sequence space orders of magnitude faster than screening.

[API]

Pipeline-native integration

REST API with standard bioinformatics output formats. Fits into your existing LIMS, ELN or custom discovery pipeline.

[EU]

EU-based, GDPR-compliant

Incorporated in Malta. All data processing within EU jurisdiction. No sequence data leaves European infrastructure.

[upd]

Continuously improving model

Our model improves with every validated sequence in the platform. Customers benefit from accuracy gains as the experimental data pool grows.

// Live demo

See the reconstruction in action

Select a benchmark RNA target and explore the predicted 3D structure.

59 nt

HIV-1 TAR RNA

Conformational flexibility benchmark. Two binding-competent states relevant to Tat protein inhibition and antiviral drug design.

antiviralconformation switching
Reconstruct
112 nt

FMN Riboswitch

Bacterial gene-expression regulator with characterised apo and holo conformations. Target of antibiotic Ribocil.

antibiotic targetriboswitch
Reconstruct
94 nt

RNA-Puzzles P16

Blind prediction benchmark. Demonstrates near-native fold recovery at experimental precision.

benchmarknear-native
Reconstruct

// Get started

Ready to accelerate your RNA design workflow?

We are onboarding our first pilot customers now. Request access and we will schedule a 30-minute demonstration on your sequences.

Early access · Pilot pricing · Malta-based · EU data residency