README.md

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license: apache-2.0
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## Dyno Psi Overview

Dyno Psi-1 is a generative protein design model for *de novo* binder design. For more information, please see our [white paper](https://dynopsi.dynotx.com/dynopsi_whitepaper.pdf).

[![GitHub](https://img.shields.io/badge/GitHub-dynopsi-F1E0CC?logo=github&logoColor=black)](https://github.com/dynotx/dynopsi) 



### Description 

The Dyno Psi approach to binder design consists of a backbone generation model (Dyno Psi-1), a sequence design component, and *in silico* filters. Dyno Psi-1 samples new protein backbones via a flow-matching-based denoising process. Sequences are designed post hoc using ProteinMPNN, a state-of-the-art inverse folding model, and these candidates are filtered using a combination of physics-based and refolding confidence metrics.

This Hugging Face repository and corresponding [GitHub repository](https://github.com/dynotx/dynopsi) enable the use of the Dyno Psi-1 backbone generation model.

### Architecture & Training
Dyno Psi-1 is a ~200M-parameter non-equivariant transformer neural network. The architecture adapts key elements from the Proteina model, including triangle updates, conditioning via adaptive layer norms, and pair-biased multi-head attention, to the binder design setting.

Dyno Psi-1 was trained in multiple stages to improve its ability to generalize. Training began on a large-scale dataset of experimentally-resolved structures from the PDB and high-quality structure predictions from the AlphaFold Database (AFDB). The second stage added protein–protein interactions from the PDB as well as ~1 million cluster representatives of a custom curated set of synthetic domain-domain interaction pairs derived from AFDB monomers. This mixture of experimental and synthetic interaction data enables the model to learn diverse geometric and interface features relevant to *de novo* binder generation.

 #### Training Phases

| Phase | Steps | GPUs | Max Tokens | Data |
|-------|-------|------|------------|------|
| Monomer pretraining | 232k | 16× H200 | 256 | PDB monomers, AFDB monomers |
| Interface-aware pretraining | 143k | 96× H200 | 512 | PDB monomers, AFDB monomers, PDB multimers, ~ 4M synthetic intra-domain interaction pairs (~1M clusters) |
| Binder design fine-tuning | 154k | 96× H200 | 512 | PDB complexes, AF2-filtered synthetic interaction pairs |