{ "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" ] }, "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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", 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" ] }, "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 }