app.py

import gradio as gr
import plotly.graph_objects as go
import numpy as np
import pandas as pd
from model.model import DTIModel


dt_str = "14062024_0910"


def make_spider_plot(predictions, model_names, smiles_list):
    fig = go.Figure()

    for i, (prediction, smiles) in enumerate(zip(predictions, smiles_list)):
        fig.add_trace(go.Scatterpolar(
            r=prediction,
            theta=model_names,
            fill='toself',
            name=smiles
        ))

    fig.update_layout(
        polar=dict(
            radialaxis=dict(
                visible=True,
                range=[0, 1]
            )),
        showlegend=True
    )

    return fig


def predict_and_plot(amino_acid_sequence, smiles_input, datasets):
    model_ensemble = {}

    gbm_model_paths = {
        "BindingDB": f"model/xgb_models/xgb_model_BindingDB_{dt_str}_bt_optimized_0.json",
        "BioSNAP": f"model/xgb_models/xgb_model_BIOSNAP_full_data_{dt_str}_bt_optimized_0.json",
        "DAVIS": f"model/xgb_models/xgb_model_DAVIS_{dt_str}_bt_optimized_0.json",
        "BarlowDTI XXL": f"model/xgb_models/{dt_str}_barlowdti_xxl_model.json",
    }

    for model in datasets:
        print(f"Loading model {model}")

        model_ensemble[model] = DTIModel(
            bt_model_path=f"model/stash/{dt_str}",
            gbm_model_path=gbm_model_paths[model],
        )

    smiles_list = smiles_input.strip().split('\n')
    predictions = []
    for model in model_ensemble.values():
        model_predictions = model.predict(smiles_list, amino_acid_sequence)
        predictions.append(model_predictions)
    
    predictions = np.array(predictions).transpose().tolist()
    
    df = pd.DataFrame(predictions, index=smiles_list, columns=datasets).reset_index()
    df.columns = ["SMILES"] + datasets
    
    fig = make_spider_plot(predictions, datasets, smiles_list)
    
    return fig, df


dataset_names = [
    "BarlowDTI XXL",
    "BindingDB",
    "BioSNAP",
    "DAVIS",
]

title = "Predict Drug-Target Interactions with <span style='font-variant:small-caps;'>BarlowDTI</span>"

description = """
Enter the amino acid sequence and SMILES to get interaction predictions visualized as a spider graph and in a table. 
The values can be interpreted as the probability of interaction between the drug and the target (0 = no interaction, 1 = interaction).

Thank you for using <span style='font-variant:small-caps;'>BarlowDTI</span>!

Note: Inference may take longer, you can upgrade to a paid GPU-enabled plan for faster inference.
"""

article = """
This interface lets the scientific community use <span style='font-variant:small-caps;'>BarlowDTI</span><sub>XXL</sub> to predict drug-target interactions. 
The model ensemble consists of four models trained on different datasets: our own curated and refined dataset based on 
[Golts et. al](https://doi.org/10.48550/arXiv.2401.17174) 
in combination with 
[BindingDB](https://doi.org/10.1093/nar/gkl999), 
[BioSNAP](https://snap.stanford.edu/index.html), and 
[DAVIS](https://doi.org/10.1038/nbt.1990).

If you use our approach in your research, please cite our paper:
```
@misc{schuh2024barlowtwinsdeepneural,
      title={Barlow Twins Deep Neural Network for Advanced 1D Drug-Target Interaction Prediction}, 
      author={Maximilian G. Schuh and Davide Boldini and Stephan A. Sieber},
      year={2024},
      eprint={2408.00040},
      archivePrefix={arXiv},
      primaryClass={q-bio.BM},
      url={https://arxiv.org/abs/2408.00040},
}
```
"""

theme = gr.themes.Base(
    primary_hue="violet",
    font=[gr.themes.GoogleFont('IBM Plex Sans'), 'ui-sans-serif', 'system-ui', 'sans-serif'],
)

iface = gr.Interface(
    fn=predict_and_plot,
    inputs=[
        gr.Textbox(label="Protein Sequence", info="Just one sequence is allowed. Remove FASTA syntax (e.g. >ABC)."), 
        gr.Textbox(label="Molecule SMILES", info="One per line, multiple allowed."),
        gr.CheckboxGroup(choices=dataset_names, label="Select Models for Prediction", value="BarlowDTI XXL")
    ],
    outputs=[
        gr.Plot(label="Predictions Visualization"), 
        gr.DataFrame(label="Predictions DataFrame"),
    ],
    title=title,
    description=description,
    article=article,
    theme=theme
)

iface.launch()