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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2e2da9e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cuda\n",
      "✅ Special tokens bound: 0 1 2 3 4\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fbfdf8cb9ac5437197a7cc86f6087456",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Processing Inputs:   0%|          | 0/4 [00:00<?, ?input/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8b4f50a359d143b98f33c2a8cb7fb114",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating for O=C(C[C@@H]1N([...:   0%|          | 0/50 [00:00<?, ?mol/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Generating for O=C(C[C@@H]1N([...:  43%|████▎     | 43/100 [01:08<01:31,  1.60s/mol]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7cf96fa7ab95442cb6c360490c133f4a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating for O=C2N(C)[C@H](c...:   0%|          | 0/50 [00:00<?, ?mol/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "013cc00c44304d8a8f1fc32df7616d0e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating for O=C1/C=C\\C=C2/N...:   0%|          | 0/50 [00:00<?, ?mol/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c4acb3cdadff4458ba95201308fead60",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating for n1c2cc3c(cc2ncc...:   0%|          | 0/50 [00:00<?, ?mol/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Summary ===\n",
      "Total generated: 200\n",
      "Overall validity rate: 100.00%\n",
      "\n",
      "Input: O=C(C[C@@H]1N([C@@H](CCC1)C[C@@H](C2=CC=CC=C2)O)C)C3=CC=CC=C3\n",
      "  Validity: 100.00%\n",
      "  Avg Similarity: 0.626\n",
      "  Lipinski Pass Rate: 38.00%\n",
      "\n",
      "Input: O=C2N(C)[C@H](c1cnccc1)CC2\n",
      "  Validity: 100.00%\n",
      "  Avg Similarity: 0.418\n",
      "  Lipinski Pass Rate: 94.00%\n",
      "\n",
      "Input: O=C1/C=C\\C=C2/N1C[C@@H]3CNC[C@H]2C3\n",
      "  Validity: 100.00%\n",
      "  Avg Similarity: 0.598\n",
      "  Lipinski Pass Rate: 98.00%\n",
      "\n",
      "Input: n1c2cc3c(cc2ncc1)[C@@H]4CNC[C@H]3C4\n",
      "  Validity: 100.00%\n",
      "  Avg Similarity: 0.475\n",
      "  Lipinski Pass Rate: 96.00%\n",
      "\n",
      "Running t-SNE...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Results saved to 'generation_evaluation_results.csv'\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import selfies as sf\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import AllChem, Descriptors, Lipinski\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.manifold import TSNE\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import sys\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "# ----------------------------\n",
    "# Setup (same as before)\n",
    "# ----------------------------\n",
    "\n",
    "notebook_dir = os.getcwd()\n",
    "chemq3mtp_path = os.path.join(notebook_dir, \"ChemQ3MTP\")\n",
    "if chemq3mtp_path not in sys.path:\n",
    "    sys.path.insert(0, chemq3mtp_path)\n",
    "\n",
    "existing_paths = [p for p in sys.path if p.endswith(\"ChemQ3MTP\")]\n",
    "for path in existing_paths[:-1]:\n",
    "    sys.path.remove(path)\n",
    "\n",
    "from FastChemTokenizerHF import FastChemTokenizerSelfies\n",
    "from ChemQ3MTP import ChemQ3MTPForCausalLM\n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(f\"Using device: {device}\")\n",
    "\n",
    "tokenizer = FastChemTokenizerSelfies.from_pretrained('./selftok_core/')\n",
    "model = ChemQ3MTPForCausalLM.from_pretrained(\"./ChemQ\").to(device).eval()\n",
    "if hasattr(model, 'set_mtp_training'):\n",
    "    model.set_mtp_training(False)\n",
    "\n",
    "# ----------------------------\n",
    "# Utility Functions (same)\n",
    "# ----------------------------\n",
    "\n",
    "def smiles_to_selfies_safe(smiles):\n",
    "    try:\n",
    "        return sf.encoder(smiles)\n",
    "    except:\n",
    "        return None\n",
    "\n",
    "def selfies_to_smiles_safe(selfies):\n",
    "    try:\n",
    "        return sf.decoder(selfies)\n",
    "    except:\n",
    "        return None\n",
    "\n",
    "def process_selfies_sentence(selfies_str):\n",
    "    try:\n",
    "        tokens = list(sf.split_selfies(selfies_str))\n",
    "        joined = []\n",
    "        i = 0\n",
    "        while i < len(tokens):\n",
    "            if tokens[i] == '.' and i + 1 < len(tokens):\n",
    "                joined.append(f\".{tokens[i+1]}\")\n",
    "                i += 2\n",
    "            else:\n",
    "                joined.append(tokens[i])\n",
    "                i += 1\n",
    "        return ' '.join(joined)\n",
    "    except:\n",
    "        return None\n",
    "\n",
    "def generate_selfies(input_selfies, max_new_tokens=64):\n",
    "    processed = process_selfies_sentence(input_selfies)\n",
    "    if processed is None:\n",
    "        return None\n",
    "    enc = tokenizer(\n",
    "        processed,\n",
    "        return_tensors=\"pt\",\n",
    "        padding=True,\n",
    "        truncation=True,\n",
    "        max_length=128\n",
    "    ).to(device)\n",
    "    with torch.no_grad():\n",
    "        gen = model.generate(\n",
    "            input_ids=enc.input_ids,\n",
    "            attention_mask=enc.attention_mask,\n",
    "            max_new_tokens=max_new_tokens,\n",
    "            top_k=50,\n",
    "            temperature=1.0,\n",
    "            do_sample=True,\n",
    "            pad_token_id=tokenizer.pad_token_id,\n",
    "            eos_token_id=tokenizer.eos_token_id,\n",
    "        )\n",
    "    decoded = tokenizer.decode(gen[0], skip_special_tokens=True).replace(\" \", \"\")\n",
    "    return decoded\n",
    "\n",
    "def is_valid_smiles(smiles):\n",
    "    return Chem.MolFromSmiles(smiles) is not None\n",
    "\n",
    "def compute_morgan_fp(smiles, radius=2, nBits=2048):\n",
    "    mol = Chem.MolFromSmiles(smiles)\n",
    "    if mol is None:\n",
    "        return None\n",
    "    return AllChem.GetMorganFingerprintAsBitVect(mol, radius, nBits=nBits)\n",
    "\n",
    "def tanimoto_sim(fp1, fp2):\n",
    "    if fp1 is None or fp2 is None:\n",
    "        return 0.0\n",
    "    return Chem.DataStructs.TanimotoSimilarity(fp1, fp2)\n",
    "\n",
    "def lipinski_pass(smiles):\n",
    "    mol = Chem.MolFromSmiles(smiles)\n",
    "    if mol is None:\n",
    "        return False\n",
    "    mw = Descriptors.MolWt(mol)\n",
    "    logp = Descriptors.MolLogP(mol)\n",
    "    hbd = Lipinski.NumHDonors(mol)\n",
    "    hba = Lipinski.NumHAcceptors(mol)\n",
    "    return all([\n",
    "        mw <= 500,\n",
    "        logp <= 5,\n",
    "        hbd <= 5,\n",
    "        hba <= 10\n",
    "    ])\n",
    "\n",
    "# ----------------------------\n",
    "# Main Evaluation with selective tqdm\n",
    "# ----------------------------\n",
    "\n",
    "test_smiles = [\n",
    "    \"O=C(C[C@@H]1N([C@@H](CCC1)C[C@@H](C2=CC=CC=C2)O)C)C3=CC=CC=C3\",           # Lobeline\n",
    "    \"O=C2N(C)[C@H](c1cnccc1)CC2\",       # Cotinine\n",
    "    \"O=C1/C=C\\\\C=C2/N1C[C@@H]3CNC[C@H]2C3\",      # Cytisine\n",
    "    \"n1c2cc3c(cc2ncc1)[C@@H]4CNC[C@H]3C4\",        # Varenicline\n",
    "]\n",
    "\n",
    "results = []\n",
    "all_fps = []\n",
    "all_labels = []\n",
    "\n",
    "N_PER_INPUT = 50\n",
    "BATCHES = 5\n",
    "BATCH_SIZE = N_PER_INPUT // BATCHES\n",
    "\n",
    "# Outer progress bar: over input molecules\n",
    "for input_idx, input_smi in enumerate(tqdm(test_smiles, desc=\"Processing Inputs\", unit=\"input\")):\n",
    "    input_selfies = smiles_to_selfies_safe(input_smi)\n",
    "    if input_selfies is None:\n",
    "        print(f\"⚠️ Skipping invalid input: {input_smi}\")\n",
    "        continue\n",
    "\n",
    "    input_fp = compute_morgan_fp(input_smi)\n",
    "    generated_smiles_list = []\n",
    "\n",
    "    # Progress bar for generating 100 molecules for this input\n",
    "    pbar = tqdm(\n",
    "        total=N_PER_INPUT,\n",
    "        desc=f\"Generating for {input_smi[:15]}...\",\n",
    "        unit=\"mol\"\n",
    "    )\n",
    "\n",
    "    for _ in range(BATCHES):\n",
    "        for _ in range(BATCH_SIZE):\n",
    "            try:\n",
    "                out_selfies = generate_selfies(input_selfies, max_new_tokens=64)\n",
    "                if out_selfies:\n",
    "                    out_smi = selfies_to_smiles_safe(out_selfies)\n",
    "                    if out_smi:\n",
    "                        generated_smiles_list.append(out_smi)\n",
    "            except Exception:\n",
    "                pass  # Silent fail; pbar still increments\n",
    "            pbar.update(1)\n",
    "    pbar.close()\n",
    "\n",
    "    # Evaluate each generated molecule (no progress bar - fast operation)\n",
    "    for gen_smi in generated_smiles_list:\n",
    "        valid = is_valid_smiles(gen_smi)\n",
    "        gen_fp = compute_morgan_fp(gen_smi) if valid else None\n",
    "        sim = tanimoto_sim(input_fp, gen_fp) if valid and input_fp else 0.0\n",
    "        lipinski_ok = lipinski_pass(gen_smi) if valid else False\n",
    "\n",
    "        results.append({\n",
    "            \"input_smiles\": input_smi,\n",
    "            \"generated_smiles\": gen_smi,\n",
    "            \"valid\": valid,\n",
    "            \"similarity\": sim,\n",
    "            \"lipinski_pass\": lipinski_ok,\n",
    "            \"input_index\": input_idx\n",
    "        })\n",
    "\n",
    "        if valid and gen_fp is not None:\n",
    "            all_fps.append(np.array(gen_fp))\n",
    "            all_labels.append(input_idx)\n",
    "\n",
    "# ----------------------------\n",
    "# Summary & t-SNE (updated legend)\n",
    "# ----------------------------\n",
    "\n",
    "df = pd.DataFrame(results)\n",
    "print(\"\\n=== Summary ===\")\n",
    "print(f\"Total generated: {len(df)}\")\n",
    "print(f\"Overall validity rate: {df['valid'].mean():.2%}\")\n",
    "\n",
    "for idx, smi in enumerate(test_smiles):\n",
    "    subset = df[df['input_index'] == idx]\n",
    "    if len(subset) > 0:\n",
    "        avg_sim = subset['similarity'].mean()\n",
    "        lip_rate = subset['lipinski_pass'].mean()\n",
    "        val_rate = subset['valid'].mean()\n",
    "        print(f\"\\nInput: {smi}\")\n",
    "        print(f\"  Validity: {val_rate:.2%}\")\n",
    "        print(f\"  Avg Similarity: {avg_sim:.3f}\")\n",
    "        print(f\"  Lipinski Pass Rate: {lip_rate:.2%}\")\n",
    "\n",
    "# t-SNE\n",
    "if len(all_fps) > 0:\n",
    "    print(\"\\nRunning t-SNE...\")\n",
    "    X = np.array(all_fps)\n",
    "    tsne = TSNE(n_components=2, random_state=42, perplexity=min(30, len(X)-1))\n",
    "    X_tsne = tsne.fit_transform(X)\n",
    "\n",
    "    plt.figure(figsize=(10, 8))\n",
    "    scatter = plt.scatter(X_tsne[:, 0], X_tsne[:, 1], c=all_labels, cmap='tab10', alpha=0.7)\n",
    "    plt.colorbar(scatter, ticks=range(len(test_smiles)))\n",
    "    plt.title(\"t-SNE of Generated Molecules (colored by input molecule)\")\n",
    "    plt.xlabel(\"t-SNE 1\")\n",
    "    plt.ylabel(\"t-SNE 2\")\n",
    "\n",
    "    # Create legend with shortened labels\n",
    "    handles = [plt.Line2D([0], [0], marker='o', color='w', markerfacecolor=plt.cm.tab10(i), markersize=8)\n",
    "               for i in range(len(test_smiles))]\n",
    "    legend_labels = [f\"Mol {i+1}\" for i in range(len(test_smiles))]\n",
    "    plt.legend(handles, legend_labels, title=\"Input Molecules\", bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(\"tsne_generated_molecules.png\", dpi=150)\n",
    "    plt.show()\n",
    "else:\n",
    "    print(\"No valid fingerprints for t-SNE.\")\n",
    "\n",
    "df.to_csv(\"generation_evaluation_results.csv\", index=False)\n",
    "print(\"\\nResults saved to 'generation_evaluation_results.csv'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "214898cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Running t-SNE...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# t-SNE\n",
    "if len(all_fps) > 0:\n",
    "    print(\"\\nRunning t-SNE...\")\n",
    "    X = np.array(all_fps)\n",
    "    tsne = TSNE(n_components=2, random_state=42, perplexity=min(30, len(X)-1))\n",
    "    X_tsne = tsne.fit_transform(X)\n",
    "\n",
    "    plt.figure(figsize=(10, 8))\n",
    "    scatter = plt.scatter(\n",
    "        X_tsne[:, 0], X_tsne[:, 1],\n",
    "        c=all_labels, cmap='tab10', alpha=0.7\n",
    "    )\n",
    "    plt.title(\"t-SNE of Generated Molecules (colored by input molecule)\")\n",
    "    plt.xlabel(\"t-SNE 1\")\n",
    "    plt.ylabel(\"t-SNE 2\")\n",
    "\n",
    "    # Extract actual mapping from scatter\n",
    "    unique_labels = np.unique(all_labels)\n",
    "    cmap = scatter.cmap\n",
    "    norm = scatter.norm\n",
    "\n",
    "    handles = []\n",
    "    legend_labels = []\n",
    "    for lbl in unique_labels:\n",
    "        color = cmap(norm(lbl))   # <--- use same mapping as scatter\n",
    "        handles.append(plt.Line2D([0], [0], marker='o', color='w',\n",
    "                                  markerfacecolor=color, markersize=8))\n",
    "        legend_labels.append(f\"Mol {lbl+1}\")\n",
    "\n",
    "    plt.legend(handles, legend_labels, title=\"Input Molecules\",\n",
    "               bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(\"tsne_generated_molecules.png\", dpi=150)\n",
    "    plt.show()\n",
    "else:\n",
    "    print(\"No valid fingerprints for t-SNE.\")\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}