AI-Powered Biologics Discovery – From Binding Site to Optimized Lead
Biologics design programs generate data at every stage – structural, biophysical, and cellular – but most computational tools operate on a single stage in isolation.
The Ardigen Biologics Discovery Platform covers the full pipeline with a closed AI-LAB loop that feeds wet-lab validation back into the next design cycle – so each experimental result improves the next computational round, rather than a collection of models that require manual integration between stages.
Where computational biologics programs stall
MLOps friction & tool fragmentation
Computational biologists burn out writing “glue code” to connect isolated, open-source models rather than engineering therapeutics.
Data that doesn't improve the next stage
A binding site informs the design brief but doesn’t shape the generative model. Hit screening flags liabilities that never feed back into the next sequence design round.
Valuable assay data trapped in silos
Massive volumes of valuable wet-lab data (Yeast Display NGS, SPR/BLI assays, CMC failures) remain locked in ELNs, unable to be ingested by advanced AI and inform future design cycles.
Candidates selected on affinity alone
A platform that generates 100,000 sequences and screens them on affinity alone will advance leads that fail for developability, immunogenicity, or manufacturability downstream.
How Ardigen solves it
The platform covers five connected stages with a feedback loop that closes the cycle between computation and experiment: target characterization, binder design, hit screening, lead optimization, and manufacturability assessment. It is modular – engagements can begin at any stage depending on where your program is and what data you have. Multi-property assessment scores candidates on the key dimensions relevant to preclinical progression – not just binding. Modalities supported: peptides, macrocyclic peptides, miniproteins, antibodies, ADCs and PDCs, bispecifics, CAR-T and TCR-T constructs, mRNA, and molecular glues.
Target characterization & binding site identification
AI-driven surface fingerprinting combined with physics-based peptide docking to identify protein binding hotspots and characterize binding-site geometry – defining the structural context for binder design.
Binder design – generative AI at scale
Generative AI operates in sequence and 3D space, producing 126,800 AI-designed scaffolds filtered through five stages to 100 high-confidence binders per binding site, using RFdiffusion, Chroma, DiffAb, ESM2, IgLM, and PRISM – Ardigen’s proprietary Protein Language Model (PLM) for biological sequences.
Hit screening – multi-property assessment
Candidates scored simultaneously across safety (off-target via ARDiTox, immunogenicity), developability (stability, hydrophobicity, solubility, thermostability), and functionality (affinity, specificity, Fc effector function) – so screening surfaces leads that progress, not just leads that bind.
Lead optimization
Refinement of candidate affinity, selectivity, and stability by combining inverse folding with molecular dynamics ensemble sampling. Variants are scored using explicit physics-based binding free energy to balance potency, conformational stability, and key developability profiles.
Manufacturability & yield prediction
Assessment of expression yield, solubility, and biophysical properties relevant to scalable production – with PRISM-based yield prediction that demonstrated 20% improvement over a client’s existing method (Spearman’s ρ = 0.72–0.73).
Discuss your biologics program.
Scientific Data & Decision Packages
Ranked binder shortlist – 100 high-confidence binders per binding site, scored on binding, safety, developability, functionality, and immunogenicity, with rationale for wet-lab prioritization
Lead optimization package – sequence-specific mutation recommendations with predicted affinity, stability, and developability effects
ARDiTox off-target safety assessment for TCR and immunotherapy programs – proteome-wide off-target epitope ranking with safety scores and tissue expression context
Manufacturability & yield prediction – expression yield and biophysical property predictions for shortlisted leads.
Enterprise & Workflow Assets
Agentic workflow design and implementation for your priority multi-step research processes.
AI Discovery engine deployment – deployment of our proven discovery workflows directly into your secure cloud (VPC) or internal HPC infrastructure with zero proprietary SaaS lock-in.
AI-LAB loop feedback package – a structured framework and dataset ensuring that experimental validation actively feeds back into the next computational cycle.
ML-ready data pipelines – automated ingestion and FAIRification of historical wet-lab records, ELNs, and assay data to train internal predictive models.
Scientific confidence
Multi-property screening and ARDiTox integration verify that leads advancing to wet-lab validation are scored on dimensions that predict preclinical success – not just binding affinity.
Operational efficiency
Complete computational design campaigns in under 1 month – 3× faster than classical wet-lab-based approaches – with multi-property filtering concentrating experimental resources on leads likely to progress.
Decision quality
The AI-LAB loop transforms every experimental result into a data point that refines the next decision. Leads are ranked on the dimensions relevant to your program’s preclinical requirements.
Risk reduction
Developability and safety screening before wet-lab commitment reduces late-stage failures – and ARDiTox off-target epitope screening addresses critical off-target cross-reactivity liabilities in TCR programs.
Frequently Asked Questions
Can the platform be used if we already have a hit list – or does it require starting from target characterization?
The platform is modular and can be entered at any stage. With an existing hit list, lead optimization and multi-property screening are the relevant entry points. With a validated target but no binders, the pipeline starts at binding site identification. Early-stage with a novel target, full-pipeline engagement is available.
What is PRISM, and how does it differ from existing foundation models for biologics?
PRISM is Ardigen’s proprietary large language model for biological sequences, pre-trained on UniProt/UniRef50, UCSC genomes, and NCBI reference sequences. It supports property prediction across stability, expression, degradation, and developability, plus optimization and classification.
PRISM is integrated throughout the pipeline alongside established models like RFdiffusion, ESM2, DiffAb, and IgLM – providing broader design-space coverage than any single architecture.
How are off-target safety risks assessed within the platform?
For TCR-based and other immunotherapy programs where pHLA recognition is the mechanism of action, ARDiTox is integrated as a standard component of hit screening and lead selection – performing proteome-wide off-target epitope identification and generating a ranked safety score per candidate, in parallel with affinity and developability scoring.
How does the AI-LAB loop work in practice?
When wet-lab results – affinity measurements, cellular assays, structural data – are returned to the Ardigen team, they’re integrated into the model and ranking infrastructure to update predictions for the next design cycle.
In practice, the second round of design starts from a better model than the first.
Contact
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