Phase 1: Graph Construction
  - 分子 SMILES → 分子グラフ変換
  - タンパク質 PDB → 接触マップグラフ
  - ノード/エッジ特徴量設計
    ↓
Phase 2: Model Selection
  - GCN / GAT / GIN / GraphSAGE / SchNet
  - Message Passing 方式選択
  - プーリング戦略 (mean, sum, attention)
    ↓
Phase 3: Training
  - データ分割 (scaffold split 推奨)
  - ミニバッチローダー設定
  - 学習率スケジューラ
    ↓
Phase 4: Evaluation
  - ROC-AUC / RMSE / MAE
  - 5-fold CV or scaffold split
  - MoleculeNet ベンチマーク比較
    ↓
Phase 5: Interpretation
  - GNNExplainer / Attention 可視化
  - 分子部分構造の寄与分析
  - SHAP for graphs
    ↓
Phase 6: Deployment
  - モデルエクスポート (ONNX)
  - バッチ推論パイプライン
  - 予測レポート生成

import torch
import torch.nn.functional as F
from torch_geometric.nn import GCNConv, GATConv, GINConv, global_mean_pool
from torch_geometric.data import Data, DataLoader
from torch_geometric.datasets import MoleculeNet
from rdkit import Chem
import numpy as np

# === 分子 → グラフ変換 ===
def smiles_to_graph(smiles):
    """SMILES → PyG Data オブジェクト"""
    mol = Chem.MolFromSmiles(smiles)
    if mol is None:
        return None

    # ノード特徴量 (原子特徴)
    atom_features = []
    for atom in mol.GetAtoms():
        features = [
            atom.GetAtomicNum(),
            atom.GetDegree(),
            atom.GetFormalCharge(),
            int(atom.GetHybridization()),
            int(atom.GetIsAromatic()),
            atom.GetTotalNumHs(),
            atom.GetNumRadicalElectrons(),
        ]
        atom_features.append(features)

    x = torch.tensor(atom_features, dtype=torch.float)

    # エッジ (結合)
    edge_index = []
    for bond in mol.GetBonds():
        i, j = bond.GetBeginAtomIdx(), bond.GetEndAtomIdx()
        edge_index.extend([[i, j], [j, i]])  # 双方向

    edge_index = torch.tensor(edge_index, dtype=torch.long).t().contiguous()

    return Data(x=x, edge_index=edge_index)


# === GCN モデル ===
class MolGCN(torch.nn.Module):
    def __init__(self, in_channels, hidden_channels, out_channels, n_layers=3):
        super().__init__()
        self.convs = torch.nn.ModuleList()
        self.convs.append(GCNConv(in_channels, hidden_channels))
        for _ in range(n_layers - 2):
            self.convs.append(GCNConv(hidden_channels, hidden_channels))
        self.convs.append(GCNConv(hidden_channels, hidden_channels))
        self.lin = torch.nn.Linear(hidden_channels, out_channels)

    def forward(self, x, edge_index, batch):
        for conv in self.convs:
            x = conv(x, edge_index)
            x = F.relu(x)
            x = F.dropout(x, p=0.2, training=self.training)

        # グラフレベル表現 (Readout)
        x = global_mean_pool(x, batch)
        x = self.lin(x)
        return x


# === GAT (Graph Attention Network) ===
class MolGAT(torch.nn.Module):
    def __init__(self, in_channels, hidden_channels, out_channels, heads=4):
        super().__init__()
        self.conv1 = GATConv(in_channels, hidden_channels, heads=heads, dropout=0.3)
        self.conv2 = GATConv(hidden_channels * heads, hidden_channels, heads=1, dropout=0.3)
        self.lin = torch.nn.Linear(hidden_channels, out_channels)

    def forward(self, x, edge_index, batch):
        x = F.elu(self.conv1(x, edge_index))
        x = F.elu(self.conv2(x, edge_index))
        x = global_mean_pool(x, batch)
        x = self.lin(x)
        return x

from torch_geometric.nn import GINConv, global_add_pool

class MolGIN(torch.nn.Module):
    """GIN: 最大表現力を持つ GNN (WL test 等価)"""
    def __init__(self, in_channels, hidden_channels, out_channels, n_layers=5):
        super().__init__()
        self.convs = torch.nn.ModuleList()
        self.batch_norms = torch.nn.ModuleList()

        for i in range(n_layers):
            in_ch = in_channels if i == 0 else hidden_channels
            mlp = torch.nn.Sequential(
                torch.nn.Linear(in_ch, hidden_channels),
                torch.nn.BatchNorm1d(hidden_channels),
                torch.nn.ReLU(),
                torch.nn.Linear(hidden_channels, hidden_channels),
            )
            self.convs.append(GINConv(mlp, train_eps=True))
            self.batch_norms.append(torch.nn.BatchNorm1d(hidden_channels))

        self.lin1 = torch.nn.Linear(hidden_channels, hidden_channels)
        self.lin2 = torch.nn.Linear(hidden_channels, out_channels)

    def forward(self, x, edge_index, batch):
        for conv, bn in zip(self.convs, self.batch_norms):
            x = conv(x, edge_index)
            x = bn(x)
            x = F.relu(x)

        x = global_add_pool(x, batch)
        x = F.relu(self.lin1(x))
        x = F.dropout(x, p=0.5, training=self.training)
        x = self.lin2(x)
        return x

import torchdrug
from torchdrug import datasets, models, tasks, core

# === TorchDrug: MoleculeNet ベンチマーク ===
# BBBP (Blood-Brain Barrier Penetration) 分類
dataset = datasets.BBBP("data/bbbp/", atom_feature="default", bond_feature="default")

# Scaffold split (推奨: 化学的多様性を考慮)
train_set, valid_set, test_set = dataset.split(ratios=[0.8, 0.1, 0.1])

# GIN モデル
model = models.GIN(
    input_dim=dataset.node_feature_dim,
    hidden_dims=[256, 256, 256, 256],
    batch_norm=True,
    short_cut=True,
    readout="mean",
)

# タスク定義
task = tasks.PropertyPrediction(
    model,
    task=dataset.tasks,
    criterion="bce",
    metric=("auprc", "auroc"),
)

# トレーニング
optimizer = torch.optim.Adam(task.parameters(), lr=1e-3)
solver = core.Engine(
    task,
    train_set, valid_set, test_set,
    optimizer,
    batch_size=128,
    gpus=[0],
)
solver.train(num_epoch=100)
solver.evaluate("valid")

from torchdrug import datasets as td_datasets, models as td_models, tasks as td_tasks

# === 知識グラフ埋め込み ===
# 例: Hetionet (疾患-遺伝子-薬物 KG)
# dataset = td_datasets.Hetionet("data/hetionet/")

# TransE / RotatE / ComplEx モデル
class KGEModel:
    """Knowledge Graph Embedding wrapper"""

    MODELS = {
        "transe": {"embedding_dim": 256, "margin": 6.0},
        "rotate": {"embedding_dim": 256},
        "complex": {"embedding_dim": 256},
    }

    def __init__(self, model_name, num_entities, num_relations):
        self.model_name = model_name
        self.num_entities = num_entities
        self.num_relations = num_relations

    def train_and_evaluate(self, train_triples, valid_triples, test_triples):
        """KGE トレーニングと評価"""
        # 評価メトリクス
        # MRR (Mean Reciprocal Rank)
        # Hits@1, Hits@3, Hits@10
        pass

    def predict_links(self, head, relation, top_k=10):
        """リンク予測: (h, r, ?) → top-k tail entities"""
        pass

    def drug_repurposing_candidates(self, disease_entity, treats_relation):
        """薬物リポジショニング候補のスコアリング"""
        pass

from sklearn.metrics import roc_auc_score, mean_squared_error
from torch_geometric.loader import DataLoader

def train_gnn(model, train_loader, optimizer, criterion, device="cpu"):
    """GNN トレーニング 1 エポック"""
    model.train()
    total_loss = 0
    for batch in train_loader:
        batch = batch.to(device)
        optimizer.zero_grad()
        out = model(batch.x, batch.edge_index, batch.batch)
        loss = criterion(out.squeeze(), batch.y.float())
        loss.backward()
        optimizer.step()
        total_loss += loss.item() * batch.num_graphs
    return total_loss / len(train_loader.dataset)


@torch.no_grad()
def evaluate_gnn(model, loader, device="cpu"):
    """GNN 評価"""
    model.eval()
    y_true, y_pred = [], []
    for batch in loader:
        batch = batch.to(device)
        out = model(batch.x, batch.edge_index, batch.batch)
        y_true.extend(batch.y.cpu().numpy())
        y_pred.extend(out.squeeze().cpu().numpy())

    y_true = np.array(y_true)
    y_pred = np.array(y_pred)

    if len(np.unique(y_true)) == 2:  # 分類
        auc = roc_auc_score(y_true, y_pred)
        return {"auroc": round(auc, 4)}
    else:  # 回帰
        rmse = np.sqrt(mean_squared_error(y_true, y_pred))
        return {"rmse": round(rmse, 4)}


def scaffold_split(dataset, train_ratio=0.8, val_ratio=0.1):
    """
    Scaffold Split: 化学構造の Murcko scaffold ベースでデータ分割。
    train/val/test が異なる scaffold を持つことを保証。
    """
    from rdkit.Chem.Scaffolds.MurckoScaffold import MurckoScaffoldSmiles
    scaffolds = {}
    for idx, data in enumerate(dataset):
        smiles = data.smiles if hasattr(data, "smiles") else ""
        scaffold = MurckoScaffoldSmiles(smiles=smiles) if smiles else str(idx)
        scaffolds.setdefault(scaffold, []).append(idx)

    scaffold_sets = sorted(scaffolds.values(), key=len, reverse=True)

    train_idx, val_idx, test_idx = [], [], []
    n = len(dataset)
    for s in scaffold_sets:
        if len(train_idx) / n < train_ratio:
            train_idx.extend(s)
        elif len(val_idx) / n < val_ratio:
            val_idx.extend(s)
        else:
            test_idx.extend(s)

    return train_idx, val_idx, test_idx