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Dockerfile

@@ -1,20 +1,30 @@
-FROM python:3.13.5-slim
-
-WORKDIR /app
+FROM python:3.10-slim
 
+# Install dependencies
 RUN apt-get update && apt-get install -y \
     build-essential \
-    curl \
+    python3-dev \
+    wget \
     git \
+    ca-certificates \
+    libxrender1 \
+    libglib2.0-0 \
+    libsm6 \
+    libxext6 \
+    libssl-dev \
+    libffi-dev \
     && rm -rf /var/lib/apt/lists/*
 
-COPY requirements.txt ./
-COPY src/ ./src/
+# Install RDKit via conda
+RUN pip install --upgrade pip
+RUN pip install streamlit pandas
+RUN pip install rdkit-pypi
 
-RUN pip3 install -r requirements.txt
+# Copy app files
+COPY app.py /app/app.py
+COPY requirements.txt /app/requirements.txt
+COPY sample_molecules.csv /app/sample_molecules.csv
 
-EXPOSE 8501
-
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+WORKDIR /app
 
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0"]

README.md

@@ -0,0 +1,17 @@
+# AI Drug Designer (Hugging Face Space)
+
+This is a Docker-based Streamlit app for de novo drug screening using RDKit.
+
+## Features
+
+- Upload SMILES molecules
+- Compute QED, MW, logP, H-bond counts
+- Validate with Lipinski Rule-of-5
+- Simulate SA Score
+- Visualize molecules with legends
+
+## How to Use on Hugging Face
+
+1. Create a new Space → Select **Docker** → Template: **Streamlit**
+2. Upload these files
+3. App will run on https://your-username.hf.space

app.py

@@ -0,0 +1,80 @@
+import streamlit as st
+from rdkit import Chem
+from rdkit.Chem import Descriptors, QED
+from rdkit.Chem import Draw
+import pandas as pd
+import base64
+from io import BytesIO
+
+st.set_page_config(page_title="AI Drug Generator Demo", layout="wide")
+st.title("💊 AI-Driven De Novo Drug Design - Molecule Screening Demo")
+
+st.markdown("""
+This app demonstrates a simplified AI-driven drug design platform.
+- Upload a CSV of SMILES
+- Compute QED, MW, logP, and Lipinski Rule
+- Visualize molecules and export results
+""")
+
+def validate_smiles(smiles_list):
+    valid = []
+    mols = []
+    for smi in smiles_list:
+        mol = Chem.MolFromSmiles(smi)
+        if mol:
+            valid.append(smi)
+            mols.append(mol)
+    return valid, mols
+
+def simulated_sa_score(mol):
+    return Descriptors.NumRotatableBonds(mol) + 2
+
+def draw_molecule_grid(mols, legends):
+    return Draw.MolsToGridImage(mols, molsPerRow=4, legends=legends, subImgSize=(200,200))
+
+uploaded_file = st.file_uploader("📁 Upload CSV with SMILES column", type=["csv"])
+
+if uploaded_file:
+    df = pd.read_csv(uploaded_file)
+    smiles_column = st.selectbox("Select SMILES column", df.columns.tolist())
+    smiles_list = df[smiles_column].dropna().astype(str).tolist()
+
+    valid_smiles, mols = validate_smiles(smiles_list)
+    st.success(f"✅ Found {len(valid_smiles)} valid molecules.")
+
+    results = []
+    for smi, mol in zip(valid_smiles, mols):
+        mw = Descriptors.MolWt(mol)
+        logp = Descriptors.MolLogP(mol)
+        hbd = Descriptors.NumHDonors(mol)
+        hba = Descriptors.NumHAcceptors(mol)
+        qed = QED.qed(mol)
+        sa_score = simulated_sa_score(mol)
+        lipinski = all([mw <= 500, logp <= 5, hbd <= 5, hba <= 10])
+        results.append({
+            "SMILES": smi,
+            "MW": round(mw, 2),
+            "logP": round(logp, 2),
+            "HBD": hbd,
+            "HBA": hba,
+            "QED": round(qed, 2),
+            "SA Score (simulated)": sa_score,
+            "Lipinski Pass": lipinski
+        })
+
+    result_df = pd.DataFrame(results)
+    st.subheader("📊 Molecule Properties Table")
+    st.dataframe(result_df)
+
+    st.subheader("🧪 Molecule Visualization")
+    legends = [f"QED={row['QED']}" for row in results[:8]]
+    grid_img = draw_molecule_grid(mols[:8], legends)
+    st.image(grid_img)
+
+    csv_buffer = BytesIO()
+    result_df.to_csv(csv_buffer, index=False)
+    b64 = base64.b64encode(csv_buffer.getvalue()).decode()
+    st.markdown(f"📥 [Download Results CSV](data:file/csv;base64,{b64})", unsafe_allow_html=True)
+
+else:
+    st.info("👆 Upload a CSV with SMILES to begin.")

requirements.txt

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+streamlit
+pandas

sample_molecules.csv

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+smiles
+CCO
+CC(=O)OC1=CC=CC=C1C(=O)O
+CC(C)CC1=CC=C(C=C1)C(C)C(=O)O
+C1=CC=C(C=C1)C=O
+C1CCC(CC1)CO
+CC(C)C(C(=O)O)N