Platform Technology

From Molecule to Market, Faster.

AI That Tackles the Entire Pipeline

Starting with mRNA therapeutics. Specialized AI agents that design, predict, and explain — powered by a provenance-grounded world model.

30-50%
Time Saved
4
Pipeline Stages
1
Integrated Platform

The Drug Discovery Crisis

90%
of clinical trials fail
$2.6B
average cost per drug
10-15
years to market

Our Solution

↑
Higher Success Rates
Calibrated predictions improve trial design
↓
Lower Costs
Fewer failed trials, faster iterations
30-50%
Time Saved
Across every pipeline stage
Full Coverage

Every Stage, Accelerated

1

Target Discovery

2-3 years
6-18 months
2

Lead Optimization

2-4 years
8-18 months
3

Preclinical

1-2 years
6-12 months
4

Clinical Trials

6-8 years
3-5 years
5

Regulatory

1-2 years
6-12 months
How We Do It

Helixir™: One Platform, Molecule to Market

Powered by AgentFabric™: Coordinated AI Agents with Human Oversight

1. Discovery

AI-CoScientist Mode

Hypothesis generation, molecule design, optimization. Scientists review and approve.

2. Preclinical

Predictive Agents

Success prediction, biomarker discovery, IND prep. Teams review go/no-go.

3. Clinical

Trial Intelligence

Protocol drafting, enrollment simulation, data analysis. Medical officers approve.

4. Regulatory (Coming Soon)

Submission Agents

Dossier assembly, compliance checking, gap analysis. Reg affairs signs off.

AI drafts, analyzes, recommends • Humans review, refine, decide • Shared context across stages

AgentFabric™: How It Works

Specialized Agents
Foundation Models
Data Intelligence
Quality Control

Discovery Agents

Hypothesis, Design, Stability, Immunogenicity, Critique, Ranking

Clinical Agents

Protocol, Eligibility, Endpoint, Site Selection, Enrollment, QC

Regulatory Agents (Coming Soon)

Dossier, CMC, Compliance, Gap Analysis, Response, Format

Human Decision Checkpoints: Scientists Review → Medical Officers Approve → Reg Affairs Signs Off

Not replacing scientists — multiplying their capacity. AI handles volume. Humans own judgment.

Proprietary Technology

mRNA Foundation Model: Next-Gen mRNA Design

The first codon-level equivariant mRNA language model that explicitly encodes synonymous codon symmetries as cyclic subgroups of the 2D Special Orthogonal matrix (SO(2)).

By integrating group-theoretic priors with an auxiliary equivariance loss and symmetry-aware pooling, Equi-mRNA learns biologically grounded representations that outperform traditional models.

~10%
Accuracy improvement on expression & stability prediction
~4x
More realistic mRNA constructs (Fréchet BioDistance)
~28%
Better at preserving functional properties
SO(2)
Symmetry-aware codon encoding
Read the Paper

How Equi-mRNA Works

1
Codon-Level Tokenization
Encodes mRNA at codon level (3-nucleotide units)
2
Synonymous Symmetry Encoding
Maps codon degeneracy to SO(2) cyclic subgroups
3
Equivariance Loss
Auxiliary loss ensures symmetry preservation
4
Symmetry-Aware Pooling
Aggregates representations respecting biological structure
Applications
Expression PredictionStability AssessmentmRNA GenerationTherapeutics Design

Closed-Loop Validation

Helixir Platform

Design • Predict • Optimize • Protocol

↓ Predictions

Validation Partners

Multiple Universities Research Labs • Pharma Labs • CRO Network

↑ Experimental Results

Every cycle improves our foundation models

Validated Candidates

Molecules proven to work before you scale

Calibrated Confidence

Know where models are strong vs need more data

Data Moat

Training on results not in any public dataset

Pharma partners plug in their own labs. You keep your data. We improve predictions.

Deliverables

What You Get

Optimized therapeutic candidates
Clinical trial protocols
Patient screening pipelines
Success probability reports
Biomarker recommendations
Regulatory submission prep

Ready to Accelerate Your Pipeline?

See how our platform can reduce your timelines and improve your success rates.

Schedule a Demo View Results