|
--- |
|
license: cc-by-nc-nd-4.0 |
|
extra_gated_fields: |
|
Name: text |
|
Company: text |
|
Country: country |
|
Specific date: date_picker |
|
I want to use this model for: |
|
type: select |
|
options: |
|
- Research |
|
- Education |
|
- label: Other |
|
value: other |
|
I agree to share generated sequences and associated data with authors before publishing: checkbox |
|
I agree not to file patents on any sequences generated by this model: checkbox |
|
I agree to use this model for non-commercial use ONLY: checkbox |
|
base_model: |
|
- facebook/esm2_t30_150M_UR50D |
|
pipeline_tag: fill-mask |
|
--- |
|
|
|
# MeMDLM: De Novo Membrane Protein Design with Masked Discrete Diffusion Language Models |
|
|
|
 |
|
|
|
|
|
[Masked Diffusion Language Models (MDLMs)](arxiv.org/pdf/2406.07524), introduced by Sahoo et al, provide strong generative capabilities to BERT-style models. In this work, we pre-train and fine-tune ESM-2-150M protein language model (pLM) on the MDLM objective to scaffold functional motifs and unconditionally generate realistic, high-quality membrane protein sequences. |
|
|
|
|
|
## Repository Authors |
|
[Shrey Goel](mailto:[email protected]), Undergraduate Student at Duke University <br> |
|
[Vishrut Thoutam](mailto:[email protected]), Student at High Technology High School <br> |
|
[Pranam Chatterjee](mailto:[email protected]), Assistant Professor at Duke University |
|
|
|
Reach out to us with any questions! |
