De novo designed mini-proteins to help drug hunters deliver life-saving treatments.




De novo designed mini-proteins to help drug hunters deliver life-saving treatments.

Why Ordaōs

At Ordaōs, we are on a mission to create novel therapies that reduce patient suffering, improve health, and extend life. We help our pharma and biotech partners around the world discover & deliver safer and more effective therapeutics in a fraction of the time with the novel power of our generative AI-designed miniPRO™ proteins.

Introducing miniPRO™

The Ordaōs solution, miniPRO™, is a class of mini-proteins that provide the power and performance of antibodies, but are more configurable, stable, and easier to manufacture.

Learn more

Introducing the Ordaōs Platform

We use our multitask meta-learning Design Engine to create novel mini-proteins from scratch.

The Design Engine leverages generative AI, proprietary multitask meta-learning, and reinforcement learning to create and evaluate de novo protein sequence, structure and physiochemical properties in silico to reliably and repeatedly deliver de novo miniPRO™ that meet the specifications of the target product profile.

Learn more

De Novo design case study

Designing de novo miniPRO™ binders to HER2 with generative AI resulting in leads that are 93% smaller than standard of care, within weeks

Learn more

About miniPRO Proteins

Introducing miniPRO™, a new class of mini-proteins that will revolutionize the role of proteins in drug discovery. Helping drug hunters create novel therapies for areas of high unmet need.

  • Configurable building block
  • 20x smaller than traditional monoclonal antibodies (40-160 amino acids) enabling them to more easily penetrate disease microenvironments for effective treatment
  • Sound structure with improved thermostability and solubility, and minimized aggregation and other developability risks to ensure the ease of manufacturing
  • Configurable binding, optimized for on-target vs off-target binding for selected epitope, with tuned affinity range and configurable isoelectric point
  • Humanized and optimized to meet client-specific MHC /peptide profiles to minimize immunogenicity

Optimization case study

Optimizing single domain VHH antibodies against SARS-CoV-2 S1 with generative AI resulting in therapeutic candidates that overcome evolutionary escape.

Learn more

About Ordaōs Design Engine

The Ordaōs Design Engine is a multitask meta-learning model that leverages continuous learning loops and proprietary data sets to translate human-defined product criteria into machine-designed miniPRO™.

Starting with amino acids, the engine generates, appraises, and ranks billions of protein sequences, hundreds of thousands of protein structures and other drug-like properties to create miniPRO™.

miniPRO™ are evaluated to provide intelligent feedback on multiple design objectives including protein structure, binding specificity and affinity, solubility, stability, immunogenicity, and developability. The analysis feeds the engine within a reinforcement learning loop, to iteratively improve and deliver optimal miniPRO™ that meet the client’s molecular target product profile (mTPP).

Benefits of Ordaōs System

  • Scalable
  • Repeatable
  • Highly Automated
  • Disease Agnostic
  • Perceptive System
  • Efficacy
  • Time to Value
  • IP Protected
"You accomplished in four-days what took us two-years to accomplish."
Dr. Philip Howe, Chair, Department of Chemistry,
Medical University of South Carolina

Partner with us

By maximizing drug candidate delivery speed, novelty, and probability of clinical success, we can provide the highest client confidence in every investigational new drug (IND) application.

What’s more, our clients see a return on their investment in weeks, rather than months or years.

Contact us
"The AI-based drug design engine Ordaōs has developed is a method with game-changing potential."
Dr. Ülo Palm, Chief Medical Officer, Vaxxinity
Ordaos is a resident company of Johnson & Johnson Innovation – JLABS

Ordaōs in the News

We are in the news because we’re making huge strides in human-enabled, machine-driven drug design.