This work was developed as a collaboration between Novo Nordisk and the Wilke lab at The University of Texas at Austin.
- Aaron L. Feller [1,2]* ([email protected])
- Maxim Secor [1]
- Sebastian Swanson [1]
- Claus O. Wilke [2]
- Kristine Deibler [1]
[1] Molecular AI, Novo Nordisk [2]; Integrative Biology, The University of Texas at Austin
PeptideMTR is transformer-based representation learning suite for therapeutic peptides. The project investigates how explicit physicochemical information (99 RDKit descriptors) used during training can enhance the predictive power of peptide models.
The framework benchmarks three distinct architectural approaches:
- MLM (Masked Language Modeling): Purely sequence-based learning via amino acid tokens.
- MTR-only: Regression models trained using a curated set of 99 RDKit physicochemical descriptors.
- MLM-MTR (Hybrid): A dual-objective architecture that leverages both latent sequence patterns and explicit chemical descriptors during the training phase.
This repository uses pyproject.toml for dependency management. We recommend using uv for an extremely fast and reproducible setup.
-
Clone the repository:
git clone https://github.com/aaronfeller/PeptideMTR.git cd PeptideMTR -
Install dependencies and create a virtual environment: Using
uv, you can sync the entire environment in seconds:uv sync -
Activate the environment:
source .venv/bin/activate # On macOS/Linux .venv\Scripts\activate # On Windows
Alternatively, you can install the packages using standard pip:
pip install .
PeptideMTR models are designed for ease of use. Regardless of the training objective (including the MTR variants), the finalized models accept a SMILES string as the primary input for inference.
All 9 model variants associated with the forthcoming paper are hosted on Hugging Face: huggingface.co/aaronfeller.
| Model Variant | Strategy | Training Features |
|---|---|---|
| PeptideMTR-MLM | Sequence Pre-training | Masked SMILES tokens |
| PeptideMTR-MTR | Multi-Target Regression | 99 RDKit Descriptors |
| PeptideMTR-Hybrid | Split-Head Architecture | Masked SMILES tokens & 99 RDKit Descriptors |
The project utilizes a custom tokenizer optimized for the peptide chemical space. This ensures robust handling of both standard and non-canonical amino acids, facilitating the mapping of SMILES strings to the model's latent space.
The training and validation data used to develop these models—including the 99 pre-computed RDKit descriptors and their corresponding biochemical targets—are available at PeptideMTR_pretraining_data
Contributions are welcome! Please submit a pull request or open an issue to discuss any changes.
The author(s) are protected under the MIT License - see the LICENSE file for details.
