v1.0.0

.gitignore

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+*.py[cod]
+*$py.class
+
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+
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app.py

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+import gradio as gr
+from src.predict import predict
+
+def update_amino_acid(safety_param, amino_acid):
+    if safety_param == "KIBA Score":
+        return gr.update(visible=True, value=amino_acid)
+    else:
+        return gr.update(visible=False)
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            safety_param = gr.Dropdown(["Skin Reaction", "KIBA Score", "Liver Safety"])
+            drug_name = gr.Textbox(label="Drug Name")
+            amino_acid = gr.Textbox(label="Amino Acid Name", visible=False)
+        with gr.Column():
+            safety_report = gr.Textbox(label="Drug Analysis")
+
+    analyze_drug_btn = gr.Button("Get Drug Analysis")
+
+
+    analyze_drug_btn.click(
+        fn=predict,
+        inputs=[safety_param, drug_name, amino_acid],
+        outputs=safety_report,
+    )
+
+    safety_param.change(
+        fn=update_amino_acid,
+        inputs=[safety_param, amino_acid],
+        outputs=amino_acid,
+    )
+
+    examples = [
+        ["KIBA Score", "C1=CC=C(C=C1)C(=O)N", "RPDFCLEPPYTGPCKARIIRYFYNAKAGLCQTFVYGGCRAKRNNFKSAEDCMRTCGGA"],
+        ["KIBA Score", "C1=CC=C(C=C1)C(=O)N", "Humanin"],
+        ["Liver Safety", "benzamide", None],
+        ["Liver Safety", "CN1C(=O)CN=C(C2=CCCCC2)c2cc(Cl)ccc21", None],
+        ["Skin Reaction", "Amoxicillin", None],
+        ["Skin Reaction", "Atorvastatin", None]
+    ]
+
+    gr.Examples(
+        examples=examples,
+        inputs=[safety_param, drug_name, amino_acid],
+    )
+
+if __name__ == "__main__":
+    demo.queue(max_size=10).launch()

requirements.txt

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+gradio
+accelerate
+python-dotenv
+huggingface_hub
+transformers==4.48.3

src/model.py

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+import os
+import json
+from dotenv import load_dotenv
+from huggingface_hub import hf_hub_download
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+load_dotenv()
+HF_TOKEN = os.environ.get("HF_TOKEN")
+
+tdc_prompts_filepath = hf_hub_download(
+    repo_id="google/txgemma-2b-predict",
+    filename="tdc_prompts.json",  
+)
+
+tokenizer = AutoTokenizer.from_pretrained(
+    "google/txgemma-2b-predict", 
+    token = HF_TOKEN
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    "google/txgemma-2b-predict",
+    device_map = "auto",
+    token = HF_TOKEN
+)
+
+with open(tdc_prompts_filepath, "r") as f:
+    tdc_prompts = json.load(f)
+
+def txgemma_predict(prompt):
+    input_ids = tokenizer(prompt, return_tensors="pt").to("cpu")
+    outputs = model.generate(**input_ids)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+def predict_kiba_score(drug_smile, amino_acid):
+    TDC_PROMPT = tdc_prompts["KIBA"].replace("{Drug SMILES}", drug_smile).replace("{Target amino acid sequence}", amino_acid)
+    response = txgemma_predict(TDC_PROMPT)
+    return response.split("Answer:")[1].strip()
+
+def predict(task, drug_smile, amino_acid=None):
+    if task == "KIBA Score":
+        if amino_acid is None:
+            raise ValueError("amino_acid parameter is required for KIBA task")
+        kiba_score = predict_kiba_score(drug_smile, amino_acid)
+        return f"{kiba_score} Binding Affinity On Scale of 0-1000"
+
+
+    if task == "Skin Reaction":
+        TDC_PROMPT = tdc_prompts["Skin_Reaction"].replace("{Drug SMILES}", drug_smile)
+        response = txgemma_predict(TDC_PROMPT).split("Answer:")[1].strip()
+
+        if "(A)" in response: response = f"{drug_smile} does not cause a skin reaction!"
+        elif "(B)" in response: response = f"{drug_smile} causes a skin reaction!"
+
+        return response
+
+    if task == "Liver Safety":
+        TDC_PROMPT = tdc_prompts["DILI"].replace("{Drug SMILES}", drug_smile)
+        response = txgemma_predict(TDC_PROMPT).split("Answer:")[1].strip()
+
+        if "(A)" in response: response = f"{drug_smile} does not damage a liver!"
+        elif "(B)" in response: response = f"{drug_smile} can damage a liver!"

src/predict.py

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+from src.model import tdc_prompts, txgemma_predict
+
+def predict_kiba_score(drug_smile, amino_acid):
+    TDC_PROMPT = tdc_prompts["KIBA"].replace("{Drug SMILES}", drug_smile).replace("{Target amino acid sequence}", amino_acid)
+    response = txgemma_predict(TDC_PROMPT)
+    return response.split("Answer:")[1].strip()
+
+def predict(task, drug_smile, amino_acid=None):
+    if task == "KIBA Score":
+        if amino_acid is None:
+            raise ValueError("amino_acid parameter is required for KIBA task")
+        kiba_score = predict_kiba_score(drug_smile, amino_acid)
+        return f"{kiba_score} Binding Affinity On Scale of 0-1000"
+
+
+    if task == "Skin Reaction":
+        TDC_PROMPT = tdc_prompts["Skin_Reaction"].replace("{Drug SMILES}", drug_smile)
+        response = txgemma_predict(TDC_PROMPT).split("Answer:")[1].strip()
+
+        if "(A)" in response: response = f"{drug_smile} does not cause a skin reaction!"
+        elif "(B)" in response: response = f"{drug_smile} causes a skin reaction!"
+
+        return response
+    
+    if task == "Liver Safety":
+        TDC_PROMPT = tdc_prompts["DILI"].replace("{Drug SMILES}", drug_smile)
+        response = txgemma_predict(TDC_PROMPT).split("Answer:")[1].strip()
+
+        if "(A)" in response: response = f"{drug_smile} does not damage a liver!"
+        elif "(B)" in response: response = f"{drug_smile} can damage a liver!"
+
+        return response