| license: apache-2.0 | |
| ## 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). | |
| [](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 | | |