Blog

Adaptyv API: Giving everyone access to a real lab

We're releasing the Adaptyv API, which gives you and your AI agents access to our wet-lab. You can now query our target catalogue, create experiments, track them through the full pipeline, get cost estimates, and pull structured results, all programmatically.

What happened in the Nipah Protein Design Competition so far? + Prediction markets for protein design

For our Nipah Binder competition, we give a short overview of the submitted designs, examine which design methods were most widely adopted, and highlight several noteworthy and creative community contributions. Plus, we opened a prediction market on Manifold for this competition so you can bet on hit rates, best performing models, and more

Designer Spotlight: Navigating the multi-property maze for therapeutic peptide design - Tong Chen

Tong Chen and colleagues from Pranam Chatterjee’s Programmable Biology Group at the University of Pennsylvania are showcasing their most recent model MOG-DFM - a discrete flow matching model capable of optimizing therapeutic peptides across multiple (up to 5) different properties

Proteinbase — the home of protein design data

Proteinbase is the hub for experimental protein design data. Browse thousands of novel proteins with standardized lab validation, computational predictions, and the negative results no one else shares.

Adaptyv is the cloud lab for protein designers — and now available for everyone

Case study: Designing modular protein sensors using BindCraft

In this blog post, we’re taking you through how we made de novo biosensors for maltose with BindCraft, explaining our computational pipeline and experimental validation. Ultimately, we prove that the combination of cutting-edge computational design and high-throughput screening can turn a biological concept into a functional tool in no time.

Designer Spotlight: ProtRL - Reinforcement learning and the Move 37 of protein engineering

We’re taking a look at ProtRL: a framework for aligning protein language models to your desired distributions using reinforcement learning. Filippo Stocco is telling us all about how it works, why reinforcement learning is important for protein engineering, and how these proteins perform when tested in our lab at Adaptyv.

Designer Spotlight: Can a language model reason about protein design?

In this designer spotlight, we take a look at Michael Hla’s recent Pro-1. It is a protein reasoning and optimization model capable to explain why it proposes a mutation. He tested FGF-1 designs in our lab at Adaptyv and managed to show significant melting temperature improvements, while still maintaining binding to their targets. One design even reached a melting temperature comparable to the most optimized FGF-1 variants in the literature!

Introducing BenchBB and the community paper of the Protein Design Competition

We wrote a community paper about our Protein Design Competition, teaming up with your favourite protein designers from both rounds. We close the paper by creating BenchBB, the Bench-tested Binder Benchmark — a curated set of 7 protein targets designed to capture diverse binder design challenges by remaining accessible enough for wide scale lab validation

Case study: Validating proteins designed by Microsoft Research’s EvoDiff

In this case study, we highlight how Microsoft Research used our automated lab to validate proteins generated by EvoDiff, their novel sequence-first protein design model, in just a few weeks.

Protein Design Competition: Has binder design been solved?

We analyze the results of our protein design competition where 130 designers created binders for EGFR. With a 5x improvement in success rates and some designs outperforming clinical antibodies, we explore what worked, what didn't, and what this means for the future of protein engineering

Protein Optimization 103: Racing to the Top 100

What a close race this one was! In this blog post, we look at how submissions evolved throughout our latest EGFR binder design competition. We highlight the most widespread model and design choices, how some people exploited loopholes in our scoring system, and argue if de novo design is reliable or not. We also have plenty of animations for you to click through!

Introducing the Adaptyv Bio API

Imagine if you could let your AI agent design novel proteins, autonomously test them in our wet lab and then improve itself based on the results.

Protein Optimization 102: Lessons from the protein design competition

The results of the first round of the EGFR binder design competition are here! In this blog post, we analyze the most common model and design choices, take a closer look at the strategies that yielded successful binders, and provide recommendations for your future design campaigns.

Protein Optimization 101: Insights from the literature

For this technical blog post, we surveyed the state of the art of using ML for protein optimization. We focused on adaptive, cost constrained, and multi-objective methods and have summarized the best approaches for you.

Case Study: Benchmarking RFdiffusion generated binders for IL-7Ra

Using our automated Affinity Characterization workflow we validated RFDiffusion designed binders in less than 24h

Generate lab data for your AI-designed proteins in just a few clicks

After months of testing with some of the best protein design teams in the world, we are excited to announce public beta access to our protein engineering platform.

Automancer: open-source, flexible lab automation software

Automancer is a user-friendly, modular, and accessible laboratory automation software that can integrate with a wide range of devices to design and execute experimental protocols.

Leveraging 3D protein structure for deep learning with ProteinFlow

ProteinFlow is a versatile, open-source Python library for processing protein structure data for deep learning applications.

Introducing Adaptyv Bio

Proteins are the most advanced nanotechnology we know of. At Adaptyv Bio, our mission is to make proteins easier to engineer.