Independent scientific consulting

Molecular insight.
A clearer next step.

Peptide, protein, and antibody engineering at the interface of computational design and experimental biology.

I help biotech teams design and optimize molecules, plan experiments, and build the software and AI workflows behind discovery.

Discuss your program
Recognition, in three dimensions.

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FabTarget

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01 / Expertise

From the first binder
to a stronger discovery engine.

Four ways to move your science forward. Engage at one stage or connect the work across your program.

01 / DESIGN

Molecular design

Design the binder. Engineer the complete molecule.

De novo peptides, miniproteins, VHHs, and antibodies, translated into drug formats with expression, purification, and manufacturing in mind.

Explore capabilities
  • De novo binder design — sequence generation and candidate prioritization across peptides, miniproteins, VHHs, and antibodies.
  • Molecule & modality engineering — binder assembly, domain arrangement, linker geometry, valency, and fusion or multispecific architectures.
  • Structure prediction — modeling individual molecules and complexes to assess interfaces, geometry, and design hypotheses.

Typical deliverables Candidate sequences, structural models, construct designs, and a rationale for what to build next.

02 / ASSESS & TEST

Developability & validation

Anticipate liabilities. Plan the right experiments.

In silico screening paired with guidance on expression, purification, characterization, and binding kinetics.

Explore capabilities
  • Developability screening — hydrophobic and charged patches, aggregation and solubility risk, sequence liabilities, stability indicators, and predicted immunogenicity risk.
  • Experimental guidance — expression-system and construct strategy, purification planning, and biophysical characterization.
  • Binding kinetics — assay design, controls, fitting strategy, and interpretation of affinity, association, and dissociation data.

Typical deliverables A prioritized risk assessment, experimental recommendations, and interpretation of the resulting data.

03 / OPTIMIZE

Hit-to-lead optimization

Turn promising hits into better candidates.

Combine in silico affinity maturation, yeast-display strategy, and machine learning to guide each round of design and testing.

Explore capabilities
  • Computational maturation — propose and prioritize variants while balancing affinity, specificity, and developability.
  • Yeast-display guidance — library design, selection strategy, and interpretation of enrichment and sequencing data.
  • Learning from experiments — sequence–function modeling and machine-learning-guided selection of the next variants to test.

Typical deliverables Focused variant libraries, selection plans, and data-informed recommendations for the next optimization cycle.

04 / BUILD

Scientific software & AI

Give your team tools built around its science.

Custom software, agentic workflows, and data infrastructure for biotech teams, designed and implemented within your working environment.

Explore capabilities
  • Scientific agents & workflows — task-specific agents, tool integrations, and automation with appropriate scientist review.
  • Data infrastructure — connect sequence, structure, assay, and project data in usable databases and analysis pipelines.
  • Custom implementation — scientific applications and workflow development, including on-premises deployment, documentation, and team handoff.

Typical deliverables Implemented software and workflows, connected data, and documentation your team can use.

02 / Selected experience

Built at the intersection
of code and the bench.

Examples from my scientific and software development roles.

Kelp Bio / Co-founder & lead developer

From peptide data
to a working platform.

Built PepSAR, a peptide structure–activity relationship platform combining chemistry-aware analysis, machine learning, and interactive scientific software.

Commercially licensed to a clinical-stage oncology company.

Valora Therapeutics / Principal scientist

Design with experimental
decisions in mind.

Generated antibody, VHH, and miniprotein candidates; combined structure prediction and interface analysis with experimental triage and developability work.

Experience across bispecifics, cell engagers, humanization, and Fc engineering.

Vertex Pharmaceuticals / Protein science

Make the experiment
more informative.

Built and optimized a high-throughput reporter assay and developed analysis pipelines to investigate compound activity and off-target effects.

Reduced screening cost by approximately 10× in the assay developed.

Prior professional experience; these organizations are not presented as consulting clients.

03 / Working together

The right scope
for your question.

Start with the scientific decision you need to make. We’ll agree on the question, inputs, deliverables, and criteria for a useful result.

Tell me what you’re working on
Eduard Puig, founder of paratope bio

04 / Your scientific partner

Eduard Puig

Ph.D. San Diego, California

A computational protein engineer who understands what happens after the prediction.

My experience spans more than a decade in drug discovery, from structural biology and protein chemistry to therapeutic antibody engineering, machine learning, and scientific software.

Across roles at Valora Therapeutics, Vertex Pharmaceuticals, Scripps Research, and Kelp Bio, I’ve worked on both sides of the design–build–test cycle. That perspective shapes how I approach a consulting engagement: make the analysis rigorous, the recommendations practical, and the next experiment worth doing.

Ph.D. in BiochemistryUniversity of Barcelona
EMBO Postdoctoral FellowScripps Research
View professional background Selected publications

05 / Selected publications

The science behind
the practice.

Full publication list on Google Scholar

Let’s find the next step

What scientific question
are you working on?

Tell me a little about your program, the decision you need to make, and where you could use support.