---
language: en
tags:
- transformers
- protein
- peptide-receptor
license: apache-2.0
datasets:
- custom
---



## Model Description


This model predicts receptor classes, identified by their PDB IDs, from peptide sequences using the [ESM2](https://huggingface.co/docs/transformers/model_doc/esm) (Evolutionary Scale Modeling) protein language model with esm2_t6_8M_UR50D pre-trained weights. The model is fine-tuned for receptor prediction using datasets from [PROPEDIA](http://bioinfo.dcc.ufmg.br/propedia2/) and [PepNN](https://www.nature.com/articles/s42003-022-03445-2), as well as novel peptides experimentally validated to bind to their target proteins, with binding conformations determined using ClusPro, a protein-protein docking tool. The name `pep2rec_cppp` reflects the model's ability to predict peptide-to-receptor relationships, leveraging training data from ClusPro, PROPEDIA, and PepNN.
It's particularly useful for researchers and practitioners in bioinformatics, drug discovery, and related fields, aiming to understand or predict peptide-receptor interactions.

## How to Use

Here is how to predict the receptor class for a peptide sequence using this model:

```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from joblib import load

MODEL_PATH = "littleworth/esm2_t6_8M_UR50D_pep2rec_cppp"
model = AutoModelForSequenceClassification.from_pretrained(MODEL_PATH)
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)

LABEL_ENCODER_PATH = f"{MODEL_PATH}/label_encoder.joblib"
label_encoder = load(LABEL_ENCODER_PATH)


input_sequence = "GNLIVVGRVIMS"

inputs = tokenizer(input_sequence, return_tensors="pt", truncation=True, padding=True)

with torch.no_grad():
    outputs = model(**inputs)
    probabilities = torch.softmax(outputs.logits, dim=1)
    predicted_class_idx = probabilities.argmax(dim=1).item()

predicted_class = label_encoder.inverse_transform([predicted_class_idx])[0]

class_probabilities = probabilities.squeeze().tolist()
class_labels = label_encoder.inverse_transform(range(len(class_probabilities)))

sorted_indices = torch.argsort(probabilities, descending=True).squeeze()
sorted_class_labels = [class_labels[i] for i in sorted_indices.tolist()]
sorted_class_probabilities = probabilities.squeeze()[sorted_indices].tolist()

print(f"Predicted Receptor Class: {predicted_class}")
print("Top 10 Class Probabilities:")
for label, prob in zip(sorted_class_labels[:10], sorted_class_probabilities[:10]):
    print(f"{label}: {prob:.4f}")


```

Which gives this output:

```
Predicted Receptor Class: 1JXP
Top 10 Class Probabilities:
1JXP: 0.7793
2OIN: 0.0058
1A1R: 0.0026
2QV1: 0.0025
3KEE: 0.0022
3KF2: 0.0016
5LAS: 0.0016
1QD6: 0.0014
6ME1: 0.0013
2XCF: 0.0013
```

## Evaluation Results

The model was evaluated on a held-out test set, yielding the following metrics:

```
{
  "train/loss": 0.7338,
  "train/grad_norm": 4.333151340484619,
  "train/learning_rate": 2.3235385792411667e-8,
  "train/epoch": 10,
  "train/global_step": 352910,
  "_timestamp": 1711654529.5562913,
  "_runtime": 204515.04906344414,
  "_step": 715,
  "eval/loss": 0.7718502879142761,
  "eval/accuracy": 0.7761048124023759,
  "eval/runtime": 2734.4878,
  "eval/samples_per_second": 34.416,
  "eval/steps_per_second": 34.416,
  "train/train_runtime": 204505.5285,
  "train/train_samples_per_second": 13.806,
  "train/train_steps_per_second": 1.726,
  "train/total_flos": 143220103846625280,
  "train/train_loss": 1.0842229404661865,
  "_wandb": {
    "runtime": 204514
  }
}
```