DeepDesign Engineering

DeepDesign Engineering

From Lead to Best-in-Class. Faster

Finding a hit is the start. Getting it to clinical candidate is the challenge. DeepDesign Engineering combines library-based, in silico, and structure-guided approaches to optimise antibody leads across every parameter that matters — affinity, humanness, cross-reactivity, developability, and pH dependency — with the speed and precision your programme requires.

Discuss Your Engineering Programme

From Lead to Best-in-Class. Faster

Finding a hit is the start. Getting it to clinical candidate is the challenge. DeepDesign Engineering combines library-based, in silico, and structure-guided approaches to optimise antibody leads across every parameter that matters — affinity, humanness, cross-reactivity, developability, and pH dependency — with the speed and precision your programme requires.

Discuss Your Engineering Programme

From Lead to Best-in-Class. Faster

Finding a hit is the start. Getting it to clinical candidate is the challenge. DeepDesign Engineering combines library-based, in silico, and structure-guided approaches to optimise antibody leads across every parameter that matters — affinity, humanness, cross-reactivity, developability, and pH dependency — with the speed and precision your programme requires.

Discuss Your Engineering Programme

>20

Tumbler campaigns completed

1,000-fold

affinity improvement demonstrated

4 months

multiparameter optimisation with Tumbler

80%

timeline reduction possible with in silico approach

What DeepDesign Engineering Solves

Every lead has a profile — and every profile has gaps. Affinity that needs improving. Immunogenicity that needs managing. Species cross-reactivity that needs inducing. Developability liabilities that will surface later if not addressed now. DeepDesign Engineering addresses all of them, with a strategy matched to your molecule's specific risk profile and your programme's timeline.

What DeepDesign Engineering Solves

Every lead has a profile — and every profile has gaps. Affinity that needs improving. Immunogenicity that needs managing. Species cross-reactivity that needs inducing. Developability liabilities that will surface later if not addressed now. DeepDesign Engineering addresses all of them, with a strategy matched to your molecule's specific risk profile and your programme's timeline.

What DeepDesign Engineering Solves

Every lead has a profile — and every profile has gaps. Affinity that needs improving. Immunogenicity that needs managing. Species cross-reactivity that needs inducing. Developability liabilities that will surface later if not addressed now. DeepDesign Engineering addresses all of them, with a strategy matched to your molecule's specific risk profile and your programme's timeline.

Engineering Objectives

DeepDesign approaches can be applied to:

  • Affinity maturation

  • Humanisation

  • Introduction of cross-reactivity (e.g. species cross-reactivity, bi-epitopic binding)

  • Developability enhancement — liability removal, thermostability, aggregation resistance

  • pH dependency engineering

  • Sequence diversification

Engineering Objectives

DeepDesign approaches can be applied to:

  • Affinity maturation

  • Humanisation

  • Introduction of cross-reactivity (e.g. species cross-reactivity, bi-epitopic binding)

  • Developability enhancement — liability removal, thermostability, aggregation resistance

  • pH dependency engineering

  • Sequence diversification

Engineering Objectives

DeepDesign approaches can be applied to:

  • Affinity maturation

  • Humanisation

  • Introduction of cross-reactivity (e.g. species cross-reactivity, bi-epitopic binding)

  • Developability enhancement — liability removal, thermostability, aggregation resistance

  • pH dependency engineering

  • Sequence diversification

DeepDesign approaches can be applied to:

  • Affinity maturation

  • Humanisation

  • Introduction of cross-reactivity (e.g. species cross-reactivity, bi-epitopic binding)

  • Developability enhancement — liability removal, thermostability, aggregation resistance

  • pH dependency engineering

  • Sequence diversification

DeepDesign in the FairJourney Bio Value Chain

DeepDesign Engineering sits at the Lead Improvement stage — immediately after hit selection, and directly feeding into lead characterisation, candidate selection, and downstream development.

Explore discovery-to-IND

Explore discovery-to-IND

Three Approaches. One Goal: Your Best Candidate

The Platform

DeepDesign Engineering is not a single-method platform. It combines three complementary approaches — library-based engineering, the Tumbler platform, and in silico design — each optimised for a different balance of speed, precision, and complexity. The right approach, or combination of approaches, is selected based on your molecule's specific engineering objectives.

Three Approaches. One Goal: Your Best Candidate

The Platform

DeepDesign Engineering is not a single-method platform. It combines three complementary approaches — library-based engineering, the Tumbler platform, and in silico design — each optimised for a different balance of speed, precision, and complexity. The right approach, or combination of approaches, is selected based on your molecule's specific engineering objectives.

Three Approaches. One Goal: Your Best Candidate

The Platform

DeepDesign Engineering is not a single-method platform. It combines three complementary approaches — library-based engineering, the Tumbler platform, and in silico design — each optimised for a different balance of speed, precision, and complexity. The right approach, or combination of approaches, is selected based on your molecule's specific engineering objectives.

Library-Based Engineering — High-Precision Design

Custom libraries are designed based on the project goal, using natural antibody diversity combined with structural and in silico data. This is a two-step process: first exploring optimisation space in individual CDRs, then combining all optimised CDR pools into a combinatorial library covering all six CDRs simultaneously. This allows precise definition of positional diversity at every position.

Library formats supported


  • Fab, scFv, VHH, VHH-Fc

Screening technologies


  • Phage display — high sequence coverage and diversity of selection strategies

  • Yeast display — multi-parameter selection in a eukaryotic display system

  • Mammalian display — simultaneous binding and developability enhancement

Selection capabilities


  • Affinity improvement, pH dependency, species cross-reactivity assessable before production using periplasmic extracts

  • Focused and customisable screening cascade to generate relevant data as early as possible

NGS integration

NGS differential enrichment and variant mining analysis identify rare clones and expand the accessible sequence space beyond what standard screening recovers.

Library-Based Engineering — High-Precision Design

Custom libraries are designed based on the project goal, using natural antibody diversity combined with structural and in silico data. This is a two-step process: first exploring optimisation space in individual CDRs, then combining all optimised CDR pools into a combinatorial library covering all six CDRs simultaneously. This allows precise definition of positional diversity at every position.

Library formats supported


  • Fab, scFv, VHH, VHH-Fc

Screening technologies


  • Phage display — high sequence coverage and diversity of selection strategies

  • Yeast display — multi-parameter selection in a eukaryotic display system

  • Mammalian display — simultaneous binding and developability enhancement

Selection capabilities


  • Affinity improvement, pH dependency, species cross-reactivity assessable before production using periplasmic extracts

  • Focused and customisable screening cascade to generate relevant data as early as possible

NGS integration

NGS differential enrichment and variant mining analysis identify rare clones and expand the accessible sequence space beyond what standard screening recovers.

Library-Based Engineering — High-Precision Design

Custom libraries are designed based on the project goal, using natural antibody diversity combined with structural and in silico data. This is a two-step process: first exploring optimisation space in individual CDRs, then combining all optimised CDR pools into a combinatorial library covering all six CDRs simultaneously. This allows precise definition of positional diversity at every position.

Library formats supported


  • Fab, scFv, VHH, VHH-Fc

Screening technologies


  • Phage display — high sequence coverage and diversity of selection strategies

  • Yeast display — multi-parameter selection in a eukaryotic display system

  • Mammalian display — simultaneous binding and developability enhancement

Selection capabilities


  • Affinity improvement, pH dependency, species cross-reactivity assessable before production using periplasmic extracts

  • Focused and customisable screening cascade to generate relevant data as early as possible

NGS integration

NGS differential enrichment and variant mining analysis identify rare clones and expand the accessible sequence space beyond what standard screening recovers.

Tumbler™ — Multi-Parameter Optimisation in a Single Step

Tumbler is FairJourney Bio's proprietary platform for one-step multi-parameter antibody optimisation. Starting from one or a small number of lead clones, Tumbler builds a custom library of at least 500 million variants by superimposing all six CDRs simultaneously — drawing on parental CDRs, near-sequence variants, and SuperHuman or Cosmic library diversity — then screens this library with bespoke panning strategies to drive selection across multiple objectives at once.

What Tumbler can optimise simultaneously


  • Affinity maturation — over 5-fold improvement over parental demonstrated

  • Humanisation — reduce immunogenicity while expanding lead series

  • Induction of cross-reactivity — bi-epitopic paratopes, species cross-reactivity

  • Clinical profile enhancement — liability removal, thermostability, IP coverage

Tumbler library


  • Liability-free scFv library of 5×10⁸–10⁹ unique variants of the parental antibody

  • Explores diversity across all six CDRs simultaneously

  • CDR shuffling of parental variants plus SuperHuman or Cosmic CDRs, providing far-sequence space variants that are key in engineering novel functionality into the parental clone

  • Virtually no paratope redundancy: 99.8% of paratopes

Tumbler in practice


  • Completed over 20 Tumbler campaigns across diverse target types and therapeutic areas

  • Used in >70% of Tumbler campaigns with the Cosmic Library since 2022

  • Clients own their Tumbler Library and all data generated, royalty-free

  • Starts with a free pre-project risk assessment — bioinformatic analysis of candidate clone(s) suitability, approximately 1 week turnaround

Sequence-Activity Relationship (SAR) analysis

Tumbler generates rich SAR data — positional log2 fold-changes of each amino acid across engineering objectives — providing mechanistic insight into which residues drive affinity, cross-reactivity, or developability. This data is optimal for powering machine learning training sets and AI-based antibody optimisation models.

Tumbler™ — Multi-Parameter Optimisation in a Single Step

Tumbler is FairJourney Bio's proprietary platform for one-step multi-parameter antibody optimisation. Starting from one or a small number of lead clones, Tumbler builds a custom library of at least 500 million variants by superimposing all six CDRs simultaneously — drawing on parental CDRs, near-sequence variants, and SuperHuman or Cosmic library diversity — then screens this library with bespoke panning strategies to drive selection across multiple objectives at once.

What Tumbler can optimise simultaneously


  • Affinity maturation — over 5-fold improvement over parental demonstrated

  • Humanisation — reduce immunogenicity while expanding lead series

  • Induction of cross-reactivity — bi-epitopic paratopes, species cross-reactivity

  • Clinical profile enhancement — liability removal, thermostability, IP coverage

Tumbler library


  • Liability-free scFv library of 5×10⁸–10⁹ unique variants of the parental antibody

  • Explores diversity across all six CDRs simultaneously

  • CDR shuffling of parental variants plus SuperHuman or Cosmic CDRs, providing far-sequence space variants that are key in engineering novel functionality into the parental clone

  • Virtually no paratope redundancy: 99.8% of paratopes

Tumbler in practice


  • Completed over 20 Tumbler campaigns across diverse target types and therapeutic areas

  • Used in >70% of Tumbler campaigns with the Cosmic Library since 2022

  • Clients own their Tumbler Library and all data generated, royalty-free

  • Starts with a free pre-project risk assessment — bioinformatic analysis of candidate clone(s) suitability, approximately 1 week turnaround

Sequence-Activity Relationship (SAR) analysis

Tumbler generates rich SAR data — positional log2 fold-changes of each amino acid across engineering objectives — providing mechanistic insight into which residues drive affinity, cross-reactivity, or developability. This data is optimal for powering machine learning training sets and AI-based antibody optimisation models.

Tumbler™ — Multi-Parameter Optimisation in a Single Step

Tumbler is FairJourney Bio's proprietary platform for one-step multi-parameter antibody optimisation. Starting from one or a small number of lead clones, Tumbler builds a custom library of at least 500 million variants by superimposing all six CDRs simultaneously — drawing on parental CDRs, near-sequence variants, and SuperHuman or Cosmic library diversity — then screens this library with bespoke panning strategies to drive selection across multiple objectives at once.

What Tumbler can optimise simultaneously


  • Affinity maturation — over 5-fold improvement over parental demonstrated

  • Humanisation — reduce immunogenicity while expanding lead series

  • Induction of cross-reactivity — bi-epitopic paratopes, species cross-reactivity

  • Clinical profile enhancement — liability removal, thermostability, IP coverage

Tumbler library


  • Liability-free scFv library of 5×10⁸–10⁹ unique variants of the parental antibody

  • Explores diversity across all six CDRs simultaneously

  • CDR shuffling of parental variants plus SuperHuman or Cosmic CDRs, providing far-sequence space variants that are key in engineering novel functionality into the parental clone

  • Virtually no paratope redundancy: 99.8% of paratopes

Tumbler in practice


  • Completed over 20 Tumbler campaigns across diverse target types and therapeutic areas

  • Used in >70% of Tumbler campaigns with the Cosmic Library since 2022

  • Clients own their Tumbler Library and all data generated, royalty-free

  • Starts with a free pre-project risk assessment — bioinformatic analysis of candidate clone(s) suitability, approximately 1 week turnaround

Sequence-Activity Relationship (SAR) analysis

Tumbler generates rich SAR data — positional log2 fold-changes of each amino acid across engineering objectives — providing mechanistic insight into which residues drive affinity, cross-reactivity, or developability. This data is optimal for powering machine learning training sets and AI-based antibody optimisation models.

In Silico Approach — Rational Design for Speed and Precision

With the aid of physics-informed and ML-based tools, DeepDesign's in silico approach designs and filters a large number of sequence variants computationally, generating a selected set of candidates for production and characterisation. This approach can reduce engineering timelines by up to 80% compared to library-based screening — and is particularly powerful for properties that are difficult to select for experimentally, such as developability, humanness, and immunogenicity potential.


  • Physics- and AI-based tools for variant design and modelling

  • Down-selection of promising variants before any experimental work begins

  • ISRA — FairJourney Bio's proprietary pipeline for in silico developability risk assessment and sequence optimisation

  • Timeline reduction of up to 80% versus library-based approaches for appropriate engineering objectives

In Silico Approach — Rational Design for Speed and Precision

With the aid of physics-informed and ML-based tools, DeepDesign's in silico approach designs and filters a large number of sequence variants computationally, generating a selected set of candidates for production and characterisation. This approach can reduce engineering timelines by up to 80% compared to library-based screening — and is particularly powerful for properties that are difficult to select for experimentally, such as developability, humanness, and immunogenicity potential.


  • Physics- and AI-based tools for variant design and modelling

  • Down-selection of promising variants before any experimental work begins

  • ISRA — FairJourney Bio's proprietary pipeline for in silico developability risk assessment and sequence optimisation

  • Timeline reduction of up to 80% versus library-based approaches for appropriate engineering objectives

In Silico Approach — Rational Design for Speed and Precision

With the aid of physics-informed and ML-based tools, DeepDesign's in silico approach designs and filters a large number of sequence variants computationally, generating a selected set of candidates for production and characterisation. This approach can reduce engineering timelines by up to 80% compared to library-based screening — and is particularly powerful for properties that are difficult to select for experimentally, such as developability, humanness, and immunogenicity potential.


  • Physics- and AI-based tools for variant design and modelling

  • Down-selection of promising variants before any experimental work begins

  • ISRA — FairJourney Bio's proprietary pipeline for in silico developability risk assessment and sequence optimisation

  • Timeline reduction of up to 80% versus library-based approaches for appropriate engineering objectives

Structure-First Engineering — Cryo-EM Guided Design

DeepDesign Engineering now integrates directly with FairJourney Bio's AtomicLevel Cryo-EM platform to offer structure-first engineering. Epitope and paratope structure determination by cryo-EM provides the 3D information needed to guide library design or in silico design approaches — identifying the key interacting residues and the molecular basis of affinity and specificity before engineering begins.


  • Structure-guided design of highly focused libraries — significantly reducing the experimental sequence space

  • Complete coverage of the relevant design space in downstream screening

  • Seamless integration between cryo-EM structural output and DeepDesign library or in silico design workflows


Cryo-EM structure determination: 1–3 weeks. Library approach timeline: 12–14 weeks. In silico approach timeline: 5–6 weeks

Structure-First Engineering — Cryo-EM Guided Design

DeepDesign Engineering now integrates directly with FairJourney Bio's AtomicLevel Cryo-EM platform to offer structure-first engineering. Epitope and paratope structure determination by cryo-EM provides the 3D information needed to guide library design or in silico design approaches — identifying the key interacting residues and the molecular basis of affinity and specificity before engineering begins.


  • Structure-guided design of highly focused libraries — significantly reducing the experimental sequence space

  • Complete coverage of the relevant design space in downstream screening

  • Seamless integration between cryo-EM structural output and DeepDesign library or in silico design workflows


Cryo-EM structure determination: 1–3 weeks. Library approach timeline: 12–14 weeks. In silico approach timeline: 5–6 weeks

Structure-First Engineering — Cryo-EM Guided Design

DeepDesign Engineering now integrates directly with FairJourney Bio's AtomicLevel Cryo-EM platform to offer structure-first engineering. Epitope and paratope structure determination by cryo-EM provides the 3D information needed to guide library design or in silico design approaches — identifying the key interacting residues and the molecular basis of affinity and specificity before engineering begins.


  • Structure-guided design of highly focused libraries — significantly reducing the experimental sequence space

  • Complete coverage of the relevant design space in downstream screening

  • Seamless integration between cryo-EM structural output and DeepDesign library or in silico design workflows


Cryo-EM structure determination: 1–3 weeks. Library approach timeline: 12–14 weeks. In silico approach timeline: 5–6 weeks

Precise Strategy. Measurable Outcomes

Process & Deliverables  

Every DeepDesign Engineering campaign starts with a strategy designed for your molecule — the right combination of library-based, in silico, and structure-guided approaches matched to your engineering objectives, timeline, and risk tolerance.

Precise Strategy. Measurable Outcomes

Process & Deliverables  

Every DeepDesign Engineering campaign starts with a strategy designed for your molecule — the right combination of library-based, in silico, and structure-guided approaches matched to your engineering objectives, timeline, and risk tolerance.

Precise Strategy. Measurable Outcomes

Process & Deliverables  

Every DeepDesign Engineering campaign starts with a strategy designed for your molecule — the right combination of library-based, in silico, and structure-guided approaches matched to your engineering objectives, timeline, and risk tolerance.

Workflow

Pre-project Risk Assessment

free, ~1 week)

Bioinformatic analysis of candidate clone(s) characterises suitability for Tumbler and outlines feasible engineering paths forward. This is provided free of charge before any commitment to a campaign.

QC

Week 1–2

Antigen proteins and cell lines verified and quality-controlled.

Framework Scouting

Week 2–6

Framework compatibility verified. Framework grafting performed. In silico custom library design initiated.

Library Build

Week 6–12

Library containing at least 500 million variants of the selected lead antibody built, exploring diversity across all six CDRs simultaneously.

Select

Week 12–15

High-affinity selection with bespoke phage panning strategies, controlling for cross-reactivity and selectivity.

Screen

Week 15–20

Binding and sequences confirmed. Bioinformatic gatekeeping applied. Kinetics and cell binding assessed.

Reformat & Characterise

Post Week 20

scFvs converted to multiple formats. VHHs converted to Fc-VHH. Cell binding, functional assays (e.g. ligand blocking), and developability assessments performed.

Timelines by Approach

Tumbler

Multiparameter optimisation in as little as 4 months

In silico approach

up to 80% timeline reduction versus library-based for appropriate objectives; 5–6 weeks for variant design and down-selection

Library-based engineering

12–14 weeks

Cryo-EM structure determination

1–3 weeks, runs in parallel

What You Receive

  • Optimised antibody variants in the agreed final format (IgG, Fab, scFv, VHH, VHH-Fc, bispecific)

  • Binding and kinetics data for all characterised variants

  • Affinity, selectivity, and species cross-reactivity assessment

  • Developability profiling: thermostability, aggregation resistance, liability flags

  • SAR analysis: positional amino acid contribution data for key engineering objectives

  • NGS data and variant mining analysis

  • Intermediate reports and Certificate of Analysis at each workpackage end

  • Final project report: design rationale, decision log, variant series summary

  • Full data and Tumbler Library ownership — royalty-free

Integration

DeepDesign Engineering outputs feed directly into FullSpectrum Characterization for biophysical and biochemical profiling, AtomicLevel Cryo-EM for structural confirmation and structure-guided next iterations, and AnyFormat Manufacturing for cell line development and GMP manufacturing — all under a single project lead with full data continuity.

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