The Problem: Model Choice Is Too Often Guesswork

Protein language models have multiplied quickly. ESM, Ankh, ProtTrans, SaProt, discrete diffusion models, codon-aware models, and many others all promise useful protein representations.

But the right model depends on the task. A model that works well for enzyme classification may not be the best choice for localization, mutational effect prediction, solubility, or protein-protein interaction.

The field has a comparison problem. Different papers use different datasets, splits, pooling choices, probes, preprocessing steps, and reporting conventions. That makes it hard to tell whether one model is truly better or just evaluated differently.

Protify was built to make that comparison practical.

The Core Idea

Protify is an open-source, low-code platform for protein property prediction and model evaluation.

It gives researchers a unified way to run protein language models across many curated datasets and evaluation protocols. Instead of rebuilding a pipeline for every model and task, users can compare models under a shared framework.

The goal is not to crown a universal winner. The goal is to make model selection empirical.

What The Paper Showed

Protify supports a broad model and dataset landscape, spanning zero-shot evaluation, supervised learning, transfer learning, and lightweight scikit-learn workflows.

Across the case studies, one conclusion stood out: no single protein language model dominates everywhere. Rankings shift by task and protocol. Some models are strong for one biological property and weaker for another. Some training strategies are attractive when labels are scarce, while simpler approaches can be enough when embeddings already capture the right signal.

That is exactly why a platform like Protify matters. It lets researchers test the question they actually have, on the data they actually care about.

Why This Matters

Model selection has real cost. A poor choice can waste GPU time, delay experiments, or lead a team to trust a weak signal. A reproducible comparison framework lowers that risk.

Protify also lowers the barrier to entry. Not every lab has the time or infrastructure to wire together dozens of models, datasets, probes, and evaluation scripts. A low-code platform lets more teams benchmark seriously without becoming infrastructure teams first.

Connection to Synthyra

Protify is part of the research foundation behind Synthyra's model-development culture. It supports the habit of asking "which representation actually works for this biological question?" rather than assuming the biggest or newest model is best.

Synthyra products extend that mindset into production. We use open research and open-source tooling as a foundation, then build improved models, serving layers, validation workflows, and user-facing analysis around them.

What This Enables

For scientists, Protify makes benchmarking a normal part of the workflow. Try several models, compare them under consistent rules, choose the one that works for the task, and move forward.

For organizations, it supports more reliable model governance. Instead of relying on headline metrics, teams can document why a model was selected and how it behaved across relevant datasets.

That is the kind of infrastructure protein AI needs as it moves from demos into real research programs.

---

This blog post summarizes work in the following paper:

Protify: A low-code protein property prediction platform
Nikolaos Rafailidis, Logan Hallee, Tamar Peleg, Colin Horger, Luck Haviland, Jason P. Gleghorn
Manuscript draft, 2026