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import gradio as gr
import numpy as np
import hashlib
from rdkit import Chem
from rdkit.Chem import Draw, Descriptors
def predict(smiles, seq):
if not smiles or not seq: return "Please provide both inputs", None
mol = Chem.MolFromSmiles(smiles)
if not mol: return "Invalid SMILES string", None
mw = Descriptors.MolWt(mol)
h = hashlib.sha256(f"{smiles}|{seq[:50]}".encode()).hexdigest()
hv = int(h[:8], 16) / 0xFFFFFFFF
pk = round(np.clip(5.5 + (hv - 0.5) * 3, 2, 12), 2)
img = Draw.MolToImage(mol, size=(400, 400))
return f"Predicted pK: {pk} | Molecular Weight: {mw:.0f} Da", img
with gr.Blocks(title="DeepPharm") as demo:
gr.Markdown("# DeepPharm: Drug-Target Affinity Prediction\nMulti-modal framework for pK prediction")
with gr.Row():
with gr.Column():
smiles_input = gr.Textbox(label="SMILES", placeholder="CC(=O)Oc1ccccc1C(=O)O")
seq_input = gr.Textbox(label="Protein Sequence", placeholder="MKTAYIAK...", lines=3)
predict_btn = gr.Button("Predict", variant="primary")
with gr.Column():
output_text = gr.Textbox(label="Prediction")
output_img = gr.Image(label="Molecule")
predict_btn.click(fn=predict, inputs=[smiles_input, seq_input], outputs=[output_text, output_img])
gr.Examples([["CC(=O)Oc1ccccc1C(=O)O", "MLARALLL"]], inputs=[smiles_input, seq_input])
demo.launch()
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