app.py

"""

AntioxFP — GNN-Based Antioxidant Activity Predictor

====================================================

Predicts DPPH radical scavenging activity (pIC50) from SMILES strings

using a 30-model AttentiveFP ensemble with GNNExplainer atom importance maps.



Based on: "Graph Neural Network Models for Predicting the Antioxidant Activity

of Chemical Compounds" (2025)

"""

import os
import sys
import warnings
import io
import math

os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
warnings.filterwarnings("ignore")

import numpy as np
import torch
from torch_geometric.data import Data
from torch_geometric.explain import Explainer, GNNExplainer
from rdkit import Chem
from rdkit.Chem import Descriptors
from rdkit.Chem.Draw import rdMolDraw2D
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from matplotlib.colors import Normalize
from PIL import Image
import gradio as gr

from models_arch import AttentiveFPModel

# ── Constants ─────────────────────────────────────────────────────────────────
MODEL_DIR = os.path.join(os.path.dirname(__file__), "models")
DEVICE = torch.device("cpu")  # HF free CPU tier

SEEDS = [42, 1, 100]
N_FOLDS = 10
HIDDEN = 200

ATOM_TYPES  = ["C", "N", "O", "S", "F", "Cl", "Br", "I", "P", "other"]
DEGREE_VALS = [0, 1, 2, 3, 4, 5]
CHARGE_VALS = [-2, -1, 0, 1, 2]
from rdkit.Chem import rdchem
HYBRID_VALS = [
    rdchem.HybridizationType.SP, rdchem.HybridizationType.SP2,
    rdchem.HybridizationType.SP3, rdchem.HybridizationType.SP3D,
    rdchem.HybridizationType.SP3D2,
]
BOND_TYPES = [
    rdchem.BondType.SINGLE, rdchem.BondType.DOUBLE,
    rdchem.BondType.TRIPLE, rdchem.BondType.AROMATIC,
]

EXAMPLE_SMILES = [
    ["O=c1c(O)c(-c2ccc(O)c(O)c2)oc2cc(O)cc(O)c12", "Quercetin — high activity flavonol"],
    ["OC(=O)/C=C/c1ccc(O)c(O)c1",                  "Caffeic acid — phenolic acid"],
    ["Oc1ccc(/C=C/c2cc(O)cc(O)c2)cc1",             "Resveratrol — stilbene antioxidant"],
    ["O=c1cc(-c2ccccc2)oc2cc(O)cc(O)c12",          "Chrysin — low activity (no B-ring OH)"],
    ["O=c1c(O)c(-c2ccc(O)cc2)oc2cc(O)cc(O)c12",   "Kaempferol — moderate activity"],
    ["CC(C)(C)c1cc(C(C)(C)C)cc(CC(=O)Nc2ccccc2)c1","BHA analogue — synthetic antioxidant"],
]


# ── Molecular graph builder ────────────────────────────────────────────────────
def one_hot(val, choices):
    vec = [0] * len(choices)
    idx = choices.index(val) if val in choices else len(choices) - 1
    vec[idx] = 1
    return vec


def atom_feat(atom):
    sym = atom.GetSymbol()
    return (
        one_hot(sym if sym in ATOM_TYPES[:-1] else "other", ATOM_TYPES)
        + one_hot(atom.GetDegree(), DEGREE_VALS)
        + one_hot(atom.GetFormalCharge(), CHARGE_VALS)
        + one_hot(atom.GetHybridization(), HYBRID_VALS)
        + [int(atom.GetIsAromatic())]
        + one_hot(atom.GetTotalNumHs(), [0, 1, 2, 3, 4])
        + [int(atom.IsInRing())]
        + one_hot(atom.GetTotalValence(), [0, 1, 2, 3, 4, 5, 6])
    )


def bond_feat(bond):
    return (
        one_hot(bond.GetBondType(), BOND_TYPES)
        + [int(bond.GetIsConjugated()), int(bond.IsInRing())]
    )


def smiles_to_graph(smiles):
    mol = Chem.MolFromSmiles(smiles)
    if mol is None:
        return None, None
    x = torch.tensor([atom_feat(a) for a in mol.GetAtoms()], dtype=torch.float)
    ei, ea = [], []
    for bond in mol.GetBonds():
        i, j = bond.GetBeginAtomIdx(), bond.GetEndAtomIdx()
        f = bond_feat(bond)
        ei += [[i, j], [j, i]]
        ea += [f, f]
    if not ei:
        return None, None
    graph = Data(
        x=x,
        edge_index=torch.tensor(ei, dtype=torch.long).t().contiguous(),
        edge_attr=torch.tensor(ea, dtype=torch.float),
        batch=torch.zeros(x.size(0), dtype=torch.long),
    )
    return graph, mol


# ── Model loader ──────────────────────────────────────────────────────────────
_MODELS = None  # lazy load


def load_models():
    global _MODELS
    if _MODELS is not None:
        return _MODELS

    models = []
    for seed in SEEDS:
        seed_tag = "" if seed == 42 else f"_seed{seed}"
        for fold in range(1, N_FOLDS + 1):
            name = f"random_attentivefp{seed_tag}_fold{fold}.pt"
            path = os.path.join(MODEL_DIR, name)
            if not os.path.exists(path):
                continue
            m = AttentiveFPModel(hidden=HIDDEN, num_layers=2,
                                 num_timesteps=2, dropout=0.2).to(DEVICE)
            m.load_state_dict(torch.load(path, map_location=DEVICE, weights_only=False))
            m.eval()
            models.append(m)

    if not models:
        # fallback: single canonical model
        path = os.path.join(MODEL_DIR, "random_attentivefp.pt")
        m = AttentiveFPModel(hidden=HIDDEN).to(DEVICE)
        m.load_state_dict(torch.load(path, map_location=DEVICE, weights_only=False))
        m.eval()
        models = [m]

    _MODELS = models
    return models


# ── Rendering ─────────────────────────────────────────────────────────────────
def render_atom_importance(mol, atom_weights, size=(600, 450)):
    """Render molecule with per-atom importance heatmap via RDKit Cairo."""
    w = np.array(atom_weights, dtype=float)
    if w.max() > w.min():
        w = (w - w.min()) / (w.max() - w.min())
    else:
        w = np.ones_like(w) * 0.5

    cmap = cm.get_cmap("RdYlBu_r")
    atom_colors = {i: cmap(float(w[i]))[:3] for i in range(mol.GetNumAtoms())}
    atom_radii  = {i: 0.20 + 0.55 * float(w[i]) for i in range(mol.GetNumAtoms())}
    highlight   = list(range(mol.GetNumAtoms()))

    try:
        drawer = rdMolDraw2D.MolDraw2DCairo(*size)
        opts = drawer.drawOptions()
        opts.addAtomIndices = False
        opts.bondLineWidth = 2.0
        rdMolDraw2D.PrepareAndDrawMolecule(
            drawer, mol,
            highlightAtoms=highlight,
            highlightAtomColors=atom_colors,
            highlightAtomRadii=atom_radii,
            highlightBonds=[],
            highlightBondColors={},
        )
        drawer.FinishDrawing()
        img = Image.open(io.BytesIO(drawer.GetDrawingText()))

        # Add colorbar
        fig, ax = plt.subplots(figsize=(img.width / 100, img.height / 100 + 0.5))
        ax.imshow(img)
        ax.axis("off")
        sm = plt.cm.ScalarMappable(cmap="RdYlBu_r", norm=Normalize(0, 1))
        sm.set_array([])
        cbar = fig.colorbar(sm, ax=ax, orientation="vertical",
                            fraction=0.03, pad=0.02, aspect=20)
        cbar.set_label("Atom Importance", fontsize=10)
        cbar.set_ticks([0, 0.5, 1])
        cbar.set_ticklabels(["Low", "Medium", "High"], fontsize=8)
        plt.tight_layout(pad=0.3)
        buf = io.BytesIO()
        plt.savefig(buf, format="png", dpi=120, bbox_inches="tight")
        plt.close()
        buf.seek(0)
        return Image.open(buf).copy()
    except Exception as e:
        print(f"Rendering error: {e}")
        return None


def make_importance_bargraph(mol, atom_weights, pred_pic50):
    """Horizontal bar chart of top-15 atom importances."""
    w = np.array(atom_weights)
    atoms  = [mol.GetAtomWithIdx(i).GetSymbol() for i in range(mol.GetNumAtoms())]
    labels = [f"{sym}{i}" for i, sym in enumerate(atoms)]

    top_k = min(15, mol.GetNumAtoms())
    sort_idx = np.argsort(w)[::-1][:top_k]
    sw = w[sort_idx]
    sl = [labels[i] for i in sort_idx]

    cmap = cm.get_cmap("RdYlBu_r")
    colors = [cmap(float(v)) for v in sw]

    fig, ax = plt.subplots(figsize=(6, max(3, top_k * 0.4)))
    ax.barh(range(top_k), sw[::-1], color=colors[::-1])
    ax.set_yticks(range(top_k))
    ax.set_yticklabels(sl[::-1], fontsize=9)
    ax.set_xlabel("Atom Importance Score", fontsize=10)
    ax.set_title(f"Top-{top_k} Atom Importances  (pred pIC₅₀ = {pred_pic50:.3f})",
                 fontsize=11, fontweight="bold")
    ax.set_xlim(0, 1.05)
    ax.spines["top"].set_visible(False)
    ax.spines["right"].set_visible(False)
    plt.tight_layout()
    buf = io.BytesIO()
    plt.savefig(buf, format="png", dpi=110, bbox_inches="tight")
    plt.close()
    buf.seek(0)
    return Image.open(buf).copy()


# ── Activity interpretation ────────────────────────────────────────────────────
def interpret_activity(pic50):
    ic50_uM = 10 ** (-pic50) * 1e6
    if pic50 >= 5.0:
        level = "🟢 High"
        desc  = "Strong DPPH radical scavenger (IC₅₀ ≤ 10 µM). Comparable to quercetin."
    elif pic50 >= 4.5:
        level = "🟡 Moderate–High"
        desc  = "Moderate-to-high radical scavenging activity."
    elif pic50 >= 4.0:
        level = "🟠 Moderate"
        desc  = "Moderate DPPH scavenging activity."
    else:
        level = "🔴 Low"
        desc  = "Weak DPPH radical scavenger."
    return level, ic50_uM, desc


def pharmacophore_hint(mol, atom_weights):
    """Generate a brief pharmacophore text based on atom weights."""
    w = np.array(atom_weights)
    top_idx = np.argsort(w)[::-1][:5]
    top_atoms = [mol.GetAtomWithIdx(int(i)).GetSymbol() for i in top_idx]

    # Simple heuristic annotations
    hints = []
    if top_atoms.count("O") >= 2:
        hints.append("**Phenolic hydroxyl / catechol motif** — primary HAT pharmacophore detected")
    if any(mol.GetAtomWithIdx(int(i)).GetIsAromatic() for i in top_idx):
        hints.append("**Aromatic conjugation** — supports radical delocalization (HAT/SET)")
    if any(mol.GetAtomWithIdx(int(i)).GetSymbol() == "C"
           and not mol.GetAtomWithIdx(int(i)).GetIsAromatic() for i in top_idx):
        hints.append("**sp² vinyl/carbonyl carbons** — extended π-system (SET pathway)")
    if not hints:
        hints.append("Importance distributed across scaffold — no single dominant pharmacophore")

    return "\n".join(f"• {h}" for h in hints)


# ── Core prediction function ───────────────────────────────────────────────────
def predict(smiles_input, run_explainer):
    smiles = smiles_input.strip()
    if not smiles:
        return (None, None, "⚠️ Please enter a SMILES string.", "", "")

    graph, mol = smiles_to_graph(smiles)
    if graph is None or mol is None:
        return (None, None,
                "❌ Invalid SMILES string. Please check the input.",
                "", "")

    models = load_models()
    graph = graph.to(DEVICE)

    # Ensemble prediction
    preds = []
    with torch.no_grad():
        for m in models:
            out = m(graph)  # pass Data object → uses hasattr(x,'edge_index') branch
            preds.append(out.item())

    pred_mean = float(np.mean(preds))
    pred_std  = float(np.std(preds))
    level, ic50_uM, desc = interpret_activity(pred_mean)

    result_md = (
        f"## Predicted pIC₅₀: **{pred_mean:.3f} ± {pred_std:.3f}**\n\n"
        f"| Property | Value |\n|---|---|\n"
        f"| Activity level | {level} |\n"
        f"| Estimated IC₅₀ | **{ic50_uM:.1f} µM** |\n"
        f"| Ensemble size | {len(models)} models |\n\n"
        f"*{desc}*\n\n"
        f"> **Note**: pIC₅₀ = −log₁₀(IC₅₀/M). Higher = more active."
    )

    # Atom importance map
    atom_img = None
    bar_img  = None
    pharma_text = ""

    if run_explainer:
        try:
            explainer = Explainer(
                model=models[0],
                algorithm=GNNExplainer(epochs=150),
                explanation_type="model",
                node_mask_type="attributes",
                edge_mask_type="object",
                model_config=dict(mode="regression", task_level="graph",
                                  return_type="raw"),
            )
            exp = explainer(
                x=graph.x,
                edge_index=graph.edge_index,
                edge_attr=graph.edge_attr,
                batch=graph.batch,
            )
            raw_w = exp.node_mask.sum(dim=-1).cpu().numpy()
            raw_w = np.abs(raw_w)
            if raw_w.max() > 0:
                raw_w /= raw_w.max()

            atom_img = render_atom_importance(mol, raw_w)
            bar_img  = make_importance_bargraph(mol, raw_w, pred_mean)
            pharma_text = "### Pharmacophore Analysis\n\n" + pharmacophore_hint(mol, raw_w)
        except Exception as e:
            pharma_text = f"⚠️ Explainer error: {e}"
    else:
        pharma_text = (
            "💡 *Enable 'Run GNNExplainer' to generate atom importance maps.*\n\n"
            "The explainer adds ~10–20 s on CPU but reveals which atoms drive the prediction."
        )

    return atom_img, bar_img, result_md, pharma_text, ""


# ── Gradio UI ─────────────────────────────────────────────────────────────────
CSS = """

.main-header {

    background: linear-gradient(135deg, #e8f4f8 0%, #d0e8f0 50%, #b8dcea 100%);

    padding: 24px;

    border-radius: 12px;

    margin-bottom: 16px;

    text-align: center;

    color: #1a3a4a;

    border: 1px solid #a0c8dc;

    box-shadow: 0 2px 8px rgba(0,0,0,0.08);

}

.main-header h1 { font-size: 2.2em; margin: 0; font-weight: 800; color: #0d2b3e; }

.main-header p  { font-size: 1.05em; opacity: 0.9; margin-top: 8px; color: #1a3a4a; }

.result-box { border: 1px solid #e0e0e0; border-radius: 8px; padding: 16px; }

.example-btn { font-size: 0.85em !important; }

footer { display: none !important; }

"""

HEADER_HTML = """

<div class="main-header">

  <h1>🧪 AntioxFP</h1>

  <p>GNN-Based Antioxidant Activity Predictor | AttentiveFP Ensemble × 30 Models</p>

  <p style="font-size:0.85em; opacity:0.65;">

    Predicts DPPH• radical scavenging pIC₅₀ from SMILES · Atom-level interpretability via GNNExplainer

  </p>

</div>

"""

ABOUT_MD = """

### About This Tool



**AntioxFP** uses a 30-model AttentiveFP ensemble to predict

DPPH radical scavenging activity (pIC₅₀) for any small molecule

provided as a SMILES string.



**Key features:**

- 30-model ensemble (3 random seeds × 10-fold CV) → prediction ± uncertainty

- GNNExplainer atom importance maps → identify key pharmacophores

- Exceeds descriptor-based benchmark (R² = 0.785 vs 0.78)

- Applicable domain: ~98% of drug-like antioxidant space



**Dataset**: 1,911 DPPH antioxidants (AODB, curated by Ghironi et al. 2025)



**Reference**: *Graph Neural Network Models for Predicting the Antioxidant

Activity of Chemical Compounds*, 2025.



**Tips:**

- pIC₅₀ > 5.0 → strong antioxidant (IC₅₀ ≤ 10 µM)

- pIC₅₀ 4.0–5.0 → moderate activity

- pIC₅₀ < 4.0 → weak scavenger

- Enable GNNExplainer to see which atoms drive the prediction

"""


def predict_wrapper(smiles, run_explainer):
    atom_img, bar_img, result_md, pharma_text, _ = predict(smiles, run_explainer)
    return atom_img, bar_img, result_md, pharma_text


with gr.Blocks(css=CSS, title="AntioxFP — Antioxidant Activity Predictor") as demo:
    gr.HTML(HEADER_HTML)

    with gr.Row():
        # ── Left panel ────────────────────────────────────────────────────────
        with gr.Column(scale=2):
            smiles_input = gr.Textbox(
                label="SMILES Input",
                placeholder="Enter SMILES string, e.g. O=c1c(O)c(-c2ccc(O)c(O)c2)oc2cc(O)cc(O)c12",
                lines=3,
                max_lines=5,
            )
            with gr.Row():
                run_btn      = gr.Button("🔬 Predict Activity", variant="primary", scale=3)
                explainer_cb = gr.Checkbox(
                    label="Run GNNExplainer (atom maps, +10–20s)",
                    value=True, scale=2,
                )
            clear_btn = gr.Button("🗑️ Clear", variant="secondary")

            gr.Markdown("**Quick Examples** — click to load:")
            example_rows = []
            for i in range(0, len(EXAMPLE_SMILES), 2):
                with gr.Row():
                    for smi, label in EXAMPLE_SMILES[i:i+2]:
                        btn = gr.Button(label, elem_classes=["example-btn"])
                        btn.click(fn=lambda s=smi: s, outputs=smiles_input)

            gr.Markdown(ABOUT_MD)

        # ── Right panel ───────────────────────────────────────────────────────
        with gr.Column(scale=3):
            result_md    = gr.Markdown("*Results will appear here after prediction.*",
                                       elem_classes=["result-box"])
            pharma_text  = gr.Markdown("")

            with gr.Tabs():
                with gr.TabItem("🗺️ Atom Importance Map"):
                    atom_img = gr.Image(
                        label="2D Atom-Level Importance (blue=low → red=high)",
                        type="pil", height=420,
                    )
                with gr.TabItem("📊 Top-15 Atom Scores"):
                    bar_img = gr.Image(
                        label="Atom Importance Bar Chart",
                        type="pil", height=420,
                    )

    run_btn.click(
        fn=predict_wrapper,
        inputs=[smiles_input, explainer_cb],
        outputs=[atom_img, bar_img, result_md, pharma_text],
        show_progress="full",
    )

    clear_btn.click(
        fn=lambda: ("", None, None,
                    "*Results will appear here after prediction.*", ""),
        outputs=[smiles_input, atom_img, bar_img, result_md, pharma_text],
    )

    gr.HTML("""

    <div style="text-align:center;margin-top:16px;opacity:0.5;font-size:0.82em;">

      AntioxFP · AttentiveFP (PyTorch Geometric) · GNNExplainer ·

      RDKit · Built with Gradio · 2025

    </div>

    """)

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860)