This work was developed in the Wilke lab by Aaron Feller, as part of his PhD work the Department of Molecular Biosciences at The University of Texas at Austin.
Our support came from NIH grant 1R01 AI148419. Principle investigator C.O.W. was also supported by the Blumberg Centennial Professorship in Molecular Evolution and the Reeder Centennial Fellowship in Systematic and Evolutionary Biology at The University of Texas at Austin. Computational analyses were performed using the Biomedical Research Computing Facility at UT Austin, Center for Biomedical Research Support. RRID: SCR_021979. The authors thank Luiz Vieira for support and discussions on the topic of language model pretraining and finetuning.
PeptideCLM-23M is a peptide-specific pretrained chemical language model published in [future publication]. The base model was pretrained with masked language modeling on 23 million molecules, divided approximately into two halves: 12M small molecules and 11M peptides. Finetuned models are trained to predict membrane penetration for cyclic peptides from CycPeptMPDB.
This repository contains some example code for loading pre-trained weights, which include a Jupyter notebook example of model assessment and a python script for finetuning the pretrained model.
To install the required libraries (all necessary dependencies not listed), run:
pip install torch transformers datasets SmilesPE pandasSpecific installation instructions can be found on the respective github/PyPi/etc.
To use the pre-trained models, you can load them as follows:
from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained('aaronfeller/model_name') Replace 'model_name' with the desired model from the table below.
Refer to the example_training_script.py script in this repository for an example of how to finetune the pretrained model on your dataset. You can run the script with:
python example_finetuning_script.pyRequired libraries for loading this model include the Transformers library with its dependencies (PyTorch, etc.) and the tokenizer library SmilesPE along with its dependencies.
| Model name | Training dataset | Description |
|---|---|---|
PeptideCLM-23M-all |
PubChem + SureChEMBL + SmProt + RandPept | Fully pretrained PeptideCLM which performed best in finetuning on the downstream task of predicting peptide membrane penetration. |
PeptideCLM-11M-pep |
SmProt + RandPept | PeptideCLM pretrained on the peptide portion of pretraining data. |
PeptideCLM-12M-smol |
PubChem + SureChEMBL | PeptideCLM pretrained on the small molecule portion of pretraining data. |
All models hosted on huggingface can be loaded from huggingface.co/aaronfeller.
A custom tokenizer was both inspired by and built using Smiles Pair Encoding.
I attempted to port my custom tokenizer to HuggingFace, but was unable to. If anyone can sort this out, I'm happy to accept a pull request either here or to HuggingFace. For now, you can import the tokenizer as shown in either examples contained in this directory.
The pretraining dataset for the large model (23M) can be downloaded here: https://utexas.box.com/v/PeptideCLM-pretraining-data.
The clustered dataset of cyclic peptides analyzed with PAMPA from CycPeptMPDB is contained in the directory clustered_data with SMILES, permeability (PAMPA), and cluster number.
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.