examples/macrocyclic_peptides_permeability/macrocyclic_peptides_permeability.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "65403d54-e1bb-438e-8842-40daf4a5f27c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/scratch/eberha0001/slurm-job.666786/ipykernel_2679950/47907170.py:1: DeprecationWarning: Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display\n",
      "  from IPython.core.display import display, HTML\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>.container { width:80% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:80% !important; }</style>\"))\n",
    "\n",
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6c7a64f4-9534-4c83-91f0-fb2836258467",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "from rdkit import Chem\n",
    "from grakel.kernels import WeisfeilerLehman\n",
    "from gpytorch.kernels import RBFKernel\n",
    "from gpytorch.priors import NormalPrior\n",
    "from torch_geometric.nn import global_mean_pool\n",
    "from torch_geometric.nn.models import GIN, GAT, GCN, GraphSAGE\n",
    "\n",
    "from mobius import ExpectedImprovement, Pool, Planner\n",
    "from mobius import GPGKModel, GPModel, GPGNNModel\n",
    "from mobius import Graph, Map4Fingerprint\n",
    "from mobius import TanimotoSimilarityKernel"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88d677f0-f80b-4010-b293-c7b11c8b69a3",
   "metadata": {},
   "source": [
    "## Permeability dataset\n",
    "\n",
    "Citation: CycPeptMPDB: A Comprehensive Database of Membrane Permeability of Cyclic Peptides; Jianan Li, et al.; 2023; https://doi.org/10.1021/acs.jcim.2c01573"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "454fd6c6-d605-4d7f-9a08-bb9aace732da",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/scratch/eberha0001/slurm-job.666786/ipykernel_2679950/2828601079.py:1: DtypeWarning: Columns (24) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  dataset = pd.read_csv('../../data/CycPeptMPDB_Peptide_All.csv')\n"
     ]
    }
   ],
   "source": [
    "dataset = pd.read_csv('../../data/CycPeptMPDB_Peptide_All.csv')\n",
    "subset = dataset[['SMILES', 'Permeability']].copy().dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "409c8ff1-baeb-4d08-9ca2-e2ae3071f013",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Anything below -10\n",
    "subset = subset[(subset['Permeability'] > -10)].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f5590469-29f0-4c2e-8457-cca18fa53a23",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[13:49:32] WARNING: Charges were rearranged\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# For removing duplicates\n",
    "inchikeys = [Chem.MolToInchi(Chem.MolFromSmiles(smiles)) for smiles in subset['SMILES'].values]\n",
    "subset['InChiKey'] = inchikeys\n",
    "\n",
    "subset = subset.drop_duplicates('InChiKey')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "82143af0-69ac-41f5-969b-4ed528dd0e0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sort by permeability values, from worst (-10) to best (-4)\n",
    "subset = subset.sort_values(by='Permeability', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "34a36b36-561b-48cf-9363-92ec7076f7aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axarr = plt.subplots(1, figsize=(10, 4))\n",
    "\n",
    "subset.hist(ax=axarr, bins=7)\n",
    "\n",
    "sns.despine()\n",
    "\n",
    "plt.savefig('figure_distributions_permeability_values.png', dpi=300, bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d630da7-52ce-49ae-8641-21a8944fbcb6",
   "metadata": {},
   "source": [
    "## Random search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "12f2a180-033e-4c3c-b0ae-f537aef228d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best peptide: CC(C)C[C@@H]1C(=O)N(C)[C@H](CC(C)C)C(=O)N2CCC[C@H]2C(=O)N(C)[C@H](CC(C)C)C(=O)N[C@@H](Cc2ccccc2)C(=O)N(C)[C@@H](C)C(=O)N1C / -4.000\n",
      "\n",
      "Best peptide: CC(C)C[C@@H]1C(=O)N(C)[C@H](CC(C)C)C(=O)N2CCC[C@H]2C(=O)N(C)[C@H](CC(C)C)C(=O)N[C@@H](Cc2ccccc2)C(=O)N(C)[C@@H](C)C(=O)N1C / -4.000\n",
      "\n",
      "Best peptide: CC(C)C[C@@H]1C(=O)N(C)[C@H](CC(C)C)C(=O)N2CCC[C@H]2C(=O)N(C)[C@H](CC(C)C)C(=O)N[C@@H](Cc2ccccc2)C(=O)N(C)[C@@H](C)C(=O)N1C / -4.000\n",
      "\n",
      "Best peptide: CC(C)C[C@@H]1C(=O)N(C)[C@H](CC(C)C)C(=O)N2CCC[C@H]2C(=O)N(C)[C@H](CC(C)C)C(=O)N[C@@H](Cc2ccccc2)C(=O)N(C)[C@@H](C)C(=O)N1C / -4.000\n",
      "\n",
      "Best peptide: CC(C)C[C@@H]1C(=O)N(C)[C@H](CC(C)C)C(=O)N2CCC[C@H]2C(=O)N(C)[C@H](CC(C)C)C(=O)N[C@@H](Cc2ccccc2)C(=O)N(C)[C@@H](C)C(=O)N1C / -4.000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pool = subset.copy()\n",
    "\n",
    "peptides = pool['SMILES'][:96].values\n",
    "scores = pool['Permeability'][:96].values\n",
    "\n",
    "data = [('random', 0, p, s) for p, s in zip(peptides, scores)]\n",
    "\n",
    "pool = pool[~pool['SMILES'].isin(peptides)]\n",
    "\n",
    "for i in range(5):\n",
    "    suggested_peptides = np.random.choice(subset['SMILES'], size=96, replace=False)\n",
    "    scores_suggested_peptides = [subset[subset['SMILES'] == p]['Permeability'].values[0] for p in suggested_peptides]\n",
    "\n",
    "    pool = pool[~pool['SMILES'].isin(suggested_peptides)]\n",
    "\n",
    "    peptides = np.concatenate([peptides, suggested_peptides])\n",
    "    scores = np.concatenate((scores, scores_suggested_peptides), axis=0)\n",
    "    data.extend([('random', i + 1, p, s) for p, s in zip(suggested_peptides, scores_suggested_peptides)])\n",
    "\n",
    "    best_seq = peptides[np.argmax(scores)]\n",
    "    best_scores = np.max(scores)\n",
    "    print('Best peptide: %s / %.3f' % (best_seq, best_scores))\n",
    "    print('')\n",
    "\n",
    "df = pd.DataFrame(data=data, columns=('model', 'iter', 'polymer', 'exp_value'))\n",
    "df.to_csv('results_optimization_random.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cf031c4-fa4e-4f25-9bfc-804eb801495b",
   "metadata": {},
   "source": [
    "## Graph kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5f2fef37-b8ac-4019-813a-53fe7f01fa89",
   "metadata": {},
   "outputs": [],
   "source": [
    "graph_convertor = Graph(input_type='smiles', output_type='grakel')\n",
    "kernel = WeisfeilerLehman(normalize=True, n_iter=5)\n",
    "gpmodel = GPGKModel(kernel=kernel, transform=graph_convertor, noise_prior=NormalPrior(loc=0, scale=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0a43f408-d2d5-4010-8286-3f44682242f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "acq = ExpectedImprovement(gpmodel, maximize=True)\n",
    "optimizer = Pool(subset['SMILES'].values)\n",
    "ps = Planner(acq, optimizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5bcd26bf-62f7-4681-aaec-e2b06078286e",
   "metadata": {},
   "outputs": [],
   "source": [
    "seed_library = subset['SMILES'][:96].values\n",
    "scores_seed_library = subset['Permeability'][:96].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7394eca2-be27-4cd0-ab39-f8802885c426",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting GPGK model (cpu): 21step [00:07,  2.74step/s, loss=0.488]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best peptide: CCCC[C@@H]1NC(=O)[C@H](Cc2ccccc2)NC(=O)CSC[C@@H](C(=O)NC)NC(=O)[C@H](CCCC)N(C)C(=O)[C@H](CCCC)NC(=O)[C@H](CCCC)N(C)C1=O / -4.300\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting GPGK model (cpu): 17step [00:09,  1.79step/s, loss=1.16]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best peptide: CCCC[C@@H]1NC(=O)[C@H](CCCC)N(C)C(=O)[C@@H]2CCCN2C(=O)[C@H](CCCC)N(C)C(=O)[C@H](Cc2ccccc2)NC(=O)CSC[C@@H](C(=O)NC)NC(=O)[C@H](CCCC)N(C)C1=O / -4.000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting GPGK model (cpu): 20step [00:18,  1.10step/s, loss=1.19]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best peptide: CCCC[C@@H]1NC(=O)[C@H](CCCC)N(C)C(=O)[C@@H]2CCCN2C(=O)[C@H](CCCC)N(C)C(=O)[C@H](Cc2ccccc2)NC(=O)CSC[C@@H](C(=O)NC)NC(=O)[C@H](CCCC)N(C)C1=O / -4.000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting GPGK model (cpu): 18step [00:24,  1.38s/step, loss=1.12]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best peptide: CCCC[C@@H]1NC(=O)[C@H](CCCC)N(C)C(=O)[C@@H]2CCCN2C(=O)[C@H](CCCC)N(C)C(=O)[C@H](Cc2ccccc2)NC(=O)CSC[C@@H](C(=O)NC)NC(=O)[C@H](CCCC)N(C)C1=O / -4.000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting GPGK model (cpu): 16step [00:29,  1.85s/step, loss=1.09]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best peptide: CCCC[C@@H]1NC(=O)[C@H](CCCC)N(C)C(=O)[C@@H]2CCCN2C(=O)[C@H](CCCC)N(C)C(=O)[C@H](Cc2ccccc2)NC(=O)CSC[C@@H](C(=O)NC)NC(=O)[C@H](CCCC)N(C)C1=O / -4.000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "peptides = seed_library.copy()\n",
    "scores = scores_seed_library.copy()\n",
    "\n",
    "# Store data for later analysis\n",
    "data = [('WL', 0, p, s) for p, s in zip(peptides, scores)]\n",
    "\n",
    "for i in range(5):\n",
    "    suggested_peptides, _ = ps.recommend(peptides, scores.reshape(-1, 1), batch_size=96)\n",
    "\n",
    "    # Get real permeability scores\n",
    "    scores_suggested_peptides = [subset[subset['SMILES'] == p]['Permeability'].values[0] for p in suggested_peptides]\n",
    "\n",
    "    peptides = np.concatenate([peptides, suggested_peptides])\n",
    "    scores = np.concatenate((scores, scores_suggested_peptides), axis=0)\n",
    "    data.extend([('WL', i + 1, p, s) for p, s in zip(suggested_peptides, scores_suggested_peptides)])\n",
    "\n",
    "    best_seq = peptides[np.argmax(scores)]\n",
    "    best_scores = np.max(scores)\n",
    "    print('Best peptide: %s / %.3f' % (best_seq, best_scores))\n",
    "    print('')\n",
    "\n",
    "df = pd.DataFrame(data=data, columns=('model', 'iter', 'polymer', 'exp_value'))\n",
    "df.to_csv('results_optimization_WL.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "ebef350e-64b1-4f90-a9b4-ad40e9cbf09e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_random = pd.read_csv('results_optimization_random.csv')\n",
    "df = pd.read_csv('results_optimization_WL.csv')\n",
    "df = pd.concat([df_random, df])\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(20, 5))\n",
    "\n",
    "# pandas dataframe (df) columns: iter, peptide, exp_value\n",
    "df['iter'] = df['iter'].replace({0: 'Init.', 1: '1', 2: '2', 3: '3', 4: '4', 5: '5'})\n",
    "\n",
    "sns.lineplot(x='iter', y='exp_value', hue='model', data=df, ax=ax[0], errorbar=('ci', 95), err_style='bars', linewidth=3.5, err_kws={'lw': 3.5})\n",
    "\n",
    "ax[0].set_xlim([-0.9, 5.5])\n",
    "ax[0].set_ylim([-9.5, -3])\n",
    "ax[0].set_ylabel('Permeability', fontsize=25)\n",
    "ax[0].set_xlabel('Generations', fontsize=25)\n",
    "ax[0].xaxis.set_tick_params(labelsize=25)\n",
    "ax[0].yaxis.set_tick_params(labelsize=25)\n",
    "\n",
    "df[df['model'] == 'random']['exp_value'].hist(ax=ax[1], density=False, alpha=0.65, bins=20)\n",
    "df[df['model'] == 'WL']['exp_value'].hist(ax=ax[1], density=False, alpha=0.65, bins=20)\n",
    "ax[1].set_xlim([-9.5, -3.5])\n",
    "ax[1].set_ylim([0, 130])\n",
    "ax[1].set_xlabel('Permeability', fontsize=25)\n",
    "ax[1].set_ylabel('#Peptides', fontsize=25)\n",
    "ax[1].xaxis.set_tick_params(labelsize=25)\n",
    "ax[1].yaxis.set_tick_params(labelsize=25)\n",
    "ax[1].set_title('')\n",
    "\n",
    "sns.despine()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de8eeb14-09e7-41e3-bc1d-4b32aa7f5556",
   "metadata": {},
   "source": [
    "## MAP4 fingerprint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "db2857a5-e721-4e13-a851-3f99ad3e250e",
   "metadata": {},
   "outputs": [],
   "source": [
    "map4 = Map4Fingerprint(input_type='smiles', chiral=False)\n",
    "gp = GPModel(kernel=TanimotoSimilarityKernel(), transform=map4, noise_prior=NormalPrior(loc=0, scale=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "896e6c88-e3db-4a3e-86a8-c4ce6256ee98",
   "metadata": {},
   "outputs": [],
   "source": [
    "acq = ExpectedImprovement(gp, maximize=True)\n",
    "optimizer = Pool(subset['SMILES'].values)\n",
    "ps = Planner(acq, optimizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f785277d-691c-4bf5-9e3e-33d42fd05f78",
   "metadata": {},
   "outputs": [],
   "source": [
    "seed_library = subset['SMILES'][:96].values\n",
    "scores_seed_library = subset['Permeability'][:96].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b50b58fe-cc13-4b8d-8c15-f76952ac40c4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting GP model (cpu): 16step [00:00, 136.60step/s, loss=0.448]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best peptide: CC(C)C[C@H]1C(=O)N(C)[C@H](CC(C)C)C(=O)N[C@H](CC(C)C)C(=O)N2CCC[C@H]2C(=O)N(C)[C@H](Cc2ccc(O)cc2)C(=O)N[C@H](CC(C)C)C(=O)N1C / -4.300\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting GP model (cpu): 16step [00:00, 67.82step/s, loss=1.01]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best peptide: CC(C)C[C@@H]1NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](C)N(C)C(=O)[C@H](Cc2ccccc2)NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@@H]2CCCN2C1=O / -4.000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting GP model (cpu): 14step [00:00, 59.07step/s, loss=0.835]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best peptide: CC(C)C[C@@H]1NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](C)N(C)C(=O)[C@H](Cc2ccccc2)NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@@H]2CCCN2C1=O / -4.000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting GP model (cpu): 15step [00:00, 25.11step/s, loss=0.729]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best peptide: CC(C)C[C@@H]1NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](C)N(C)C(=O)[C@H](Cc2ccccc2)NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@@H]2CCCN2C1=O / -4.000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting GP model (cpu): 15step [00:01, 10.57step/s, loss=0.737]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best peptide: CC(C)C[C@@H]1NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](C)N(C)C(=O)[C@H](Cc2ccccc2)NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@@H]2CCCN2C1=O / -4.000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "peptides = seed_library.copy()\n",
    "scores = scores_seed_library.copy()\n",
    "\n",
    "# Store data for later analysis\n",
    "data = [('MAP4', 0, p, s) for p, s in zip(peptides, scores)]\n",
    "\n",
    "for i in range(5):\n",
    "    suggested_peptides, _ = ps.recommend(peptides, scores.reshape(-1, 1), batch_size=96)\n",
    "\n",
    "    # Get real permeability scores\n",
    "    scores_suggested_peptides = [subset[subset['SMILES'] == p]['Permeability'].values[0] for p in suggested_peptides]\n",
    "\n",
    "    peptides = np.concatenate([peptides, suggested_peptides])\n",
    "    scores = np.concatenate((scores, scores_suggested_peptides), axis=0)\n",
    "    data.extend([('MAP4', i + 1, p, s) for p, s in zip(suggested_peptides, scores_suggested_peptides)])\n",
    "\n",
    "    best_seq = peptides[np.argmax(scores)]\n",
    "    best_scores = np.max(scores)\n",
    "    print('Best peptide: %s / %.3f' % (best_seq, best_scores))\n",
    "    print('')\n",
    "\n",
    "df = pd.DataFrame(data=data, columns=('model', 'iter', 'polymer', 'exp_value'))\n",
    "df.to_csv('results_optimization_MAP4.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "66299d44-aecd-47cc-ad0d-0afccea533aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_random = pd.read_csv('results_optimization_random.csv')\n",
    "df = pd.read_csv('results_optimization_MAP4.csv')\n",
    "df = pd.concat([df_random, df])\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(20, 5))\n",
    "\n",
    "# pandas dataframe (df) columns: iter, peptide, exp_value\n",
    "df['iter'] = df['iter'].replace({0: 'Init.', 1: '1', 2: '2', 3: '3', 4: '4', 5: '5'})\n",
    "\n",
    "sns.lineplot(x='iter', y='exp_value', hue='model', data=df, ax=ax[0], errorbar=('ci', 95), err_style='bars', linewidth=3.5, err_kws={'lw': 3.5})\n",
    "\n",
    "ax[0].set_xlim([-0.9, 5.5])\n",
    "ax[0].set_ylim([-9.5, -3])\n",
    "ax[0].set_ylabel('Permeability', fontsize=25)\n",
    "ax[0].set_xlabel('Generations', fontsize=25)\n",
    "ax[0].xaxis.set_tick_params(labelsize=25)\n",
    "ax[0].yaxis.set_tick_params(labelsize=25)\n",
    "\n",
    "df[df['model'] == 'random']['exp_value'].hist(ax=ax[1], density=False, alpha=0.65, bins=20)\n",
    "df[df['model'] == 'MAP4']['exp_value'].hist(ax=ax[1], density=False, alpha=0.65, bins=20)\n",
    "ax[1].set_xlim([-9.5, -3.5])\n",
    "ax[1].set_ylim([0, 130])\n",
    "ax[1].set_xlabel('Permeability', fontsize=25)\n",
    "ax[1].set_ylabel('#Peptides', fontsize=25)\n",
    "ax[1].xaxis.set_tick_params(labelsize=25)\n",
    "ax[1].yaxis.set_tick_params(labelsize=25)\n",
    "ax[1].set_title('')\n",
    "\n",
    "sns.despine()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1eddc700-7820-47c7-b2c5-a16a531bd122",
   "metadata": {},
   "source": [
    "## MAP4 chiral fingerprint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1516ff6f-fca0-4a3c-a852-175c42670d8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "map4 = Map4Fingerprint(input_type='smiles', chiral=True)\n",
    "gp = GPModel(kernel=TanimotoSimilarityKernel(), transform=map4, noise_prior=NormalPrior(loc=0, scale=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e134c65e-ba5f-4c91-baf4-cd0edaa616eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "acq = ExpectedImprovement(gp, maximize=True)\n",
    "optimizer = Pool(subset['SMILES'].values)\n",
    "ps = Planner(acq, optimizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bea4a645-da4e-49a2-95bd-33e9e261e42d",
   "metadata": {},
   "outputs": [],
   "source": [
    "seed_library = subset['SMILES'][:96].values\n",
    "scores_seed_library = subset['Permeability'][:96].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "cd759444-8bf6-46ad-ab19-58161d6a3edf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting GP model (cpu): 20step [00:00, 83.29step/s, loss=0.446] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best peptide: CC(C)C[C@H]1C(=O)N(C)[C@H](CC(C)C)C(=O)N2CCC[C@@H]2C(=O)N(C)[C@@H](Cc2ccc(O)cc2)C(=O)N(C)[C@H](CC(C)C)C(=O)N(C)[C@@H](C)C(=O)N(C)[C@H](CC(C)C)C(=O)N(C)[C@H](C)C(=O)N1C / -4.620\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting GP model (cpu): 14step [00:00, 98.50step/s, loss=1.05]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best peptide: CC(C)C[C@H]1C(=O)N2CCC[C@H]2C(=O)N(C)[C@H](CC(C)C)C(=O)N[C@@H](Cc2ccccc2)C(=O)N(C)[C@@H](C)C(=O)N(C)[C@H](CC(C)C)C(=O)N1C / -4.000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting GP model (cpu): 14step [00:00, 59.72step/s, loss=0.903]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best peptide: CC(C)C[C@H]1C(=O)N2CCC[C@H]2C(=O)N(C)[C@H](CC(C)C)C(=O)N[C@@H](Cc2ccccc2)C(=O)N(C)[C@@H](C)C(=O)N(C)[C@H](CC(C)C)C(=O)N1C / -4.000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting GP model (cpu): 12step [00:00, 30.73step/s, loss=0.805]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best peptide: CC(C)C[C@H]1C(=O)N2CCC[C@H]2C(=O)N(C)[C@H](CC(C)C)C(=O)N[C@@H](Cc2ccccc2)C(=O)N(C)[C@@H](C)C(=O)N(C)[C@H](CC(C)C)C(=O)N1C / -4.000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting GP model (cpu): 11step [00:00, 25.85step/s, loss=0.758]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best peptide: CC(C)C[C@H]1C(=O)N2CCC[C@H]2C(=O)N(C)[C@H](CC(C)C)C(=O)N[C@@H](Cc2ccccc2)C(=O)N(C)[C@@H](C)C(=O)N(C)[C@H](CC(C)C)C(=O)N1C / -4.000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "peptides = seed_library.copy()\n",
    "scores = scores_seed_library.copy()\n",
    "\n",
    "# Store data for later analysis\n",
    "data = [('MAP4C', 0, p, s) for p, s in zip(peptides, scores)]\n",
    "\n",
    "for i in range(5):\n",
    "    suggested_peptides, _ = ps.recommend(peptides, scores.reshape(-1, 1), batch_size=96)\n",
    "\n",
    "    # Get real permeability scores\n",
    "    scores_suggested_peptides = [subset[subset['SMILES'] == p]['Permeability'].values[0] for p in suggested_peptides]\n",
    "\n",
    "    peptides = np.concatenate([peptides, suggested_peptides])\n",
    "    scores = np.concatenate((scores, scores_suggested_peptides), axis=0)\n",
    "    data.extend([('MAP4C', i + 1, p, s) for p, s in zip(suggested_peptides, scores_suggested_peptides)])\n",
    "\n",
    "    best_seq = peptides[np.argmax(scores)]\n",
    "    best_scores = np.max(scores)\n",
    "    print('Best peptide: %s / %.3f' % (best_seq, best_scores))\n",
    "    print('')\n",
    "\n",
    "df = pd.DataFrame(data=data, columns=('model', 'iter', 'polymer', 'exp_value'))\n",
    "df.to_csv('results_optimization_MAP4C.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "35062308-173f-4b9f-9722-415bcd1eeeef",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_random = pd.read_csv('results_optimization_random.csv')\n",
    "df = pd.read_csv('results_optimization_MAP4C.csv')\n",
    "df = pd.concat([df_random, df])\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(20, 5))\n",
    "\n",
    "# pandas dataframe (df) columns: iter, peptide, exp_value\n",
    "df['iter'] = df['iter'].replace({0: 'Init.', 1: '1', 2: '2', 3: '3', 4: '4', 5: '5'})\n",
    "\n",
    "sns.lineplot(x='iter', y='exp_value', hue='model', data=df, ax=ax[0], errorbar=('ci', 95), err_style='bars', linewidth=3.5, err_kws={'lw': 3.5})\n",
    "\n",
    "ax[0].set_xlim([-0.9, 5.5])\n",
    "ax[0].set_ylim([-9.5, -3])\n",
    "ax[0].set_ylabel('Permeability', fontsize=25)\n",
    "ax[0].set_xlabel('Generations', fontsize=25)\n",
    "ax[0].xaxis.set_tick_params(labelsize=25)\n",
    "ax[0].yaxis.set_tick_params(labelsize=25)\n",
    "\n",
    "df[df['model'] == 'random']['exp_value'].hist(ax=ax[1], density=False, alpha=0.65, bins=20)\n",
    "df[df['model'] == 'MAP4C']['exp_value'].hist(ax=ax[1], density=False, alpha=0.65, bins=20)\n",
    "ax[1].set_xlim([-9.5, -3.5])\n",
    "ax[1].set_ylim([0, 130])\n",
    "ax[1].set_xlabel('Permeability', fontsize=25)\n",
    "ax[1].set_ylabel('#Peptides', fontsize=25)\n",
    "ax[1].xaxis.set_tick_params(labelsize=25)\n",
    "ax[1].yaxis.set_tick_params(labelsize=25)\n",
    "ax[1].set_title('')\n",
    "\n",
    "sns.despine()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d6bb8fe-a5d6-4b85-9614-bf627c3029a2",
   "metadata": {},
   "source": [
    "## GNN models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "68f79209-9237-4000-b1db-96d5ce1f5808",
   "metadata": {},
   "outputs": [],
   "source": [
    "graph_convertor = Graph(input_type='smiles', output_type='pyg')\n",
    "graphs = graph_convertor.transform(subset['SMILES'].values[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fabaaf87-0504-46ba-b634-39291446dd1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "class GNNModel(torch.nn.Module):\n",
    "    def __init__(self, model_name, in_channels, hidden_channels, out_channels, num_layers, dropout):\n",
    "        super(GNNModel, self).__init__()\n",
    "\n",
    "        models = {'GIN': GIN, 'GAT': GAT, 'GCN': GCN, 'GraphSAGE': GraphSAGE}\n",
    "        gnn_model = models[model_name]\n",
    "\n",
    "        self.model = gnn_model(in_channels=in_channels, hidden_channels=hidden_channels, num_layers=num_layers, \n",
    "                               out_channels=out_channels, dropout=dropout)\n",
    "        \n",
    "        self._reset_parameters()\n",
    "\n",
    "    def _reset_parameters(self):\n",
    "        r\"\"\"Initiate parameters in the transformer model.\"\"\"\n",
    "\n",
    "        for p in self.parameters():\n",
    "            if p.dim() > 1:\n",
    "                torch.nn.init.xavier_uniform_(p)\n",
    "\n",
    "    def forward(self, x):\n",
    "        node_attr, edge_index, edge_attr, batch = x.node_attr, x.edge_index, x.edge_attr, x.batch\n",
    "        x = self.model(x=node_attr, edge_index=edge_index, edge_attr=edge_attr, batch=batch)\n",
    "        x = global_mean_pool(x, batch)\n",
    "\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f8918ca5-db0c-4eeb-b06c-a7bcd16caf18",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GAT\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting GPGNN model (cpu): 8step [00:03,  2.39step/s, loss=0.701]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best peptide: CC[C@H](C)[C@@H]1NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](C)NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](Cc2ccccc2)NC(=O)[C@H]([C@@H](C)O)NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](C)N(C)C(=O)C[C@H](C)NC1=O / -4.150\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting GPGNN model (cpu): 7step [00:07,  1.02s/step, loss=1.8] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best peptide: CC(C)C[C@@H]1NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](C)N(C)C(=O)[C@H](Cc2ccccc2)NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@@H]2CCCN2C1=O / -4.000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting GPGNN model (cpu): 1step [00:04,  4.97s/step, loss=1.91]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best peptide: CC(C)C[C@@H]1NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](C)N(C)C(=O)[C@H](Cc2ccccc2)NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@@H]2CCCN2C1=O / -4.000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting GPGNN model (cpu): 4step [00:10,  2.59s/step, loss=1.66]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best peptide: CC(C)C[C@@H]1NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](C)N(C)C(=O)[C@H](Cc2ccccc2)NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@@H]2CCCN2C1=O / -4.000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting GPGNN model (cpu): 6step [00:19,  3.30s/step, loss=1.58]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best peptide: CC(C)C[C@@H]1NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](C)N(C)C(=O)[C@H](Cc2ccccc2)NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@@H]2CCCN2C1=O / -4.000\n",
      "\n",
      "GraphSAGE\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting GPGNN model (cpu): 10step [00:03,  2.54step/s, loss=0.667]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best peptide: CC(C)C[C@@H]1NC(=O)[C@H](C(C)C)N(C)C(=O)[C@@H](C)N(C)C(=O)[C@H](CC(C)C)NC(=O)[C@H](C)N(C)C(=O)[C@H](CC(C)C)NC(=O)[C@H](C(C)C)N(C)C(=O)[C@H]2CCCN2C(=O)[C@H](CC(C)C)NC(=O)[C@H](C)N(C)C1=O / -4.300\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting GPGNN model (cpu): 6step [00:04,  1.24step/s, loss=1.63]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best peptide: CC[C@H](C)[C@@H]1NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](C)NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](Cc2ccccc2)NC(=O)[C@H]([C@@H](C)O)NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](C)N(C)C(=O)C[C@H](C)NC1=O / -4.150\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting GPGNN model (cpu): 9step [00:10,  1.15s/step, loss=1.4] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best peptide: CC(C)C[C@@H]1NC(=O)[C@@H](CC(C)C)N(C)C(=O)[C@H](C)N(C)C(=O)[C@H](Cc2ccccc2)NC(=O)[C@@H](CC(C)C)N(C)C(=O)[C@@H]2CCCN2C1=O / -4.000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting GPGNN model (cpu): 8step [00:15,  1.98s/step, loss=1.34]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best peptide: CC(C)C[C@@H]1NC(=O)[C@@H](CC(C)C)N(C)C(=O)[C@H](C)N(C)C(=O)[C@H](Cc2ccccc2)NC(=O)[C@@H](CC(C)C)N(C)C(=O)[C@@H]2CCCN2C1=O / -4.000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting GPGNN model (cpu): 8step [00:16,  2.10s/step, loss=1.18]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best peptide: CC(C)C[C@@H]1NC(=O)[C@@H](CC(C)C)N(C)C(=O)[C@H](C)N(C)C(=O)[C@H](Cc2ccccc2)NC(=O)[C@@H](CC(C)C)N(C)C(=O)[C@@H]2CCCN2C1=O / -4.000\n",
      "\n",
      "GCN\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting GPGNN model (cpu): 8step [00:02,  3.80step/s, loss=0.694]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best peptide: CC[C@H](C)[C@@H]1NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](C)NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](Cc2ccccc2)NC(=O)[C@H]([C@@H](C)O)NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](C)N(C)C(=O)C[C@H](C)NC1=O / -4.150\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting GPGNN model (cpu): 15step [00:07,  1.96step/s, loss=1.43]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best peptide: CC(C)C[C@@H]1NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](C)N(C)C(=O)[C@H](Cc2ccccc2)NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@@H]2CCCN2C1=O / -4.000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting GPGNN model (cpu): 4step [00:05,  1.41s/step, loss=1.49]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best peptide: CC(C)C[C@@H]1NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](C)N(C)C(=O)[C@H](Cc2ccccc2)NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@@H]2CCCN2C1=O / -4.000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting GPGNN model (cpu): 1step [00:06,  6.60s/step, loss=1.53]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best peptide: CC(C)C[C@@H]1NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](C)N(C)C(=O)[C@H](Cc2ccccc2)NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@@H]2CCCN2C1=O / -4.000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting GPGNN model (cpu): 6step [00:14,  2.42s/step, loss=1.44]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best peptide: CC(C)C[C@@H]1NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](C)N(C)C(=O)[C@H](Cc2ccccc2)NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@@H]2CCCN2C1=O / -4.000\n",
      "\n",
      "GIN\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting GPGNN model (cpu): 9step [00:03,  2.44step/s, loss=0.66] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best peptide: CC[C@H](C)[C@@H]1NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](C)NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](Cc2ccccc2)NC(=O)[C@H]([C@@H](C)O)NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](C)N(C)C(=O)C[C@H](C)NC1=O / -4.150\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting GPGNN model (cpu): 4step [00:04,  1.17s/step, loss=1.92]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best peptide: CC(C)C[C@@H]1NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](C)N(C)C(=O)[C@H](Cc2ccccc2)NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@@H]2CCCN2C1=O / -4.000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting GPGNN model (cpu): 9step [00:13,  1.45s/step, loss=1.63]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best peptide: CC(C)C[C@@H]1NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](C)N(C)C(=O)[C@H](Cc2ccccc2)NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@@H]2CCCN2C1=O / -4.000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting GPGNN model (cpu): 5step [00:15,  3.11s/step, loss=1.6] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best peptide: CC(C)C[C@@H]1NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](C)N(C)C(=O)[C@H](Cc2ccccc2)NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@@H]2CCCN2C1=O / -4.000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Fitting GPGNN model (cpu): 3step [00:17,  5.78s/step, loss=1.55]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best peptide: CC(C)C[C@@H]1NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](C)N(C)C(=O)[C@H](Cc2ccccc2)NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@@H]2CCCN2C1=O / -4.000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "models = ['GAT', 'GraphSAGE', 'GCN', 'GIN']\n",
    "\n",
    "for model in models:\n",
    "    print(model)\n",
    "\n",
    "    # define model\n",
    "    gnn = GNNModel(model, in_channels=graphs.node_attr.shape[1], hidden_channels=256, out_channels=256, num_layers=4, dropout=0.25)\n",
    "    gpmodel = GPGNNModel(kernel=RBFKernel(ard_num_dims=256, eps=1e-2), model=gnn, transform=graph_convertor, noise_prior=NormalPrior(loc=0, scale=1))\n",
    "\n",
    "    # define acquisition function, optimizer and planner\n",
    "    acq = ExpectedImprovement(gpmodel , maximize=True)\n",
    "    optimizer = Pool(subset['SMILES'].values)\n",
    "    ps = Planner(acq, optimizer)\n",
    "\n",
    "    # define seed library\n",
    "    seed_library = subset['SMILES'][:96].values\n",
    "    scores_seed_library = subset['Permeability'][:96].values\n",
    "\n",
    "    peptides = seed_library.copy()\n",
    "    scores = scores_seed_library.copy()\n",
    "    \n",
    "    # Store data for later analysis\n",
    "    data = [(model, 0, p, s) for p, s in zip(peptides, scores)]\n",
    "    \n",
    "    for i in range(5):\n",
    "        suggested_peptides, _ = ps.recommend(peptides, scores.reshape(-1, 1), batch_size=96)\n",
    "    \n",
    "        # Get real permeability scores\n",
    "        scores_suggested_peptides = [subset[subset['SMILES'] == p]['Permeability'].values[0] for p in suggested_peptides]\n",
    "    \n",
    "        peptides = np.concatenate([peptides, suggested_peptides])\n",
    "        scores = np.concatenate((scores, scores_suggested_peptides), axis=0)\n",
    "        data.extend([(model, i + 1, p, s) for p, s in zip(suggested_peptides, scores_suggested_peptides)])\n",
    "    \n",
    "        best_seq = peptides[np.argmax(scores)]\n",
    "        best_scores = np.max(scores)\n",
    "        print('Best peptide: %s / %.3f' % (best_seq, best_scores))\n",
    "        print('')\n",
    "    \n",
    "    df = pd.DataFrame(data=data, columns=('model', 'iter', 'polymer', 'exp_value'))\n",
    "    df.to_csv(f'results_optimization_{model}.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "09a65d1f-d44f-4af3-b60c-cfa2fa593b0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2djZefvll7NixAwMGDFCbZ5WysjIMGDAAsbGxAAAnJye0bt0aBQUFOHfunPj8Y2JiEBQUhKNHj9Z4BC99eumllwAAJ0+eFL9PvL294ezsXC3W0dFRLIeHh2Pt2rUAgN27dyMrK0vj9zE/Px/btm0T63Xxe4+IiKihYm3I2hBgbcjakP4V6rWrn4j+dcLCwsSdbAYGBhrvQHxcaWlpUtOmTcUxvby8pH379knl5eUKcX/99ZfCk++urq5STk6O0jbl7zSsevLbxcVF2rFjh0K7paWl0sKFCyWZTCbiP/zwQ7X5Dh48WMSamJhI8+fPl+7fv68Qk5OTI73zzjsKdxn+8ssvStt79Ml2S0tLCYA0Y8YM6e7duwqxKSkpCuubNm2SfHx8pKVLl0pXr15V2n5iYqIUEhIi2jc1NZWuXbum8v1pe5fmo7R9sj04OFjEyWQyadasWQqfY0VFhbRnzx6pefPmCjlfvHhRZZvy56/q8x4/frx069YthbiLFy9K7du3F7EWFhbSgwcPlLYZGhoq4tq2bSv9+eefSuPu3LkjrVu3TvLz85NOnDih+gQRERGR3un69FJWVpZC3Td+/Hix7ddff1WoKUJDQ6uNelReXi5t375dcnBwEHH9+/dXeTz5+qiqxuvSpUu10XAKCgoUnqSRz6OqTp4xY0a12veHH35QGJHqv//9r9SvXz8JgBQUFCRdvnxZIf6vv/6SXFxcRPzzzz+v9nytWLFCIZcxY8ZUqyNLSkqkNWvWSBYWFiJu6tSpKtv8/PPPpR49ekiRkZFSenq60piYmBjJz89PtGdnZyfl5uaqbFP+e2BnZycBkBwdHaUdO3YojGSUnZ0tTZkypdp7UqUun16qostoUVWee+45sc/ixYs1xq9Zs0bEu7i4VPvdRURE9KRibcjaUJJYG7I2JFKOne1EVKc6d+4s/sC2a9eu1o8n36np5+entnO/oKBAoWD45JNPlMbJFz8AJAcHByk1NVVlu/JDzLu6uqqM+/nnnxU6gGNiYtS+twULFoh4T09PpcXKo53tAKQFCxaobbeKtjdClJeXS6+88opof+bMmSpja7OzfceOHVq/z+TkZIWbMIKCglTGPnr+5syZozL2xo0bCkP0r1u3rlpMaWmp+HEik8mq/RhRpaysTKs4IiIi0g9dL6iuXLlSoWaommKnoKBA4SLplClT1LaTnJwsNWnSRMRHR0crjXt0mp7OnTtrVb89WtvMmzdPZeynn34q4qrql+DgYKm0tFRpvPyFPQDVbuaskpaWJpmamoq4RYsWqc05JiZGTLFkYGCg8uZObevXoqIiqVu3buL4y5cvVxkr/z0AIDVu3Fg6d+6cyvhZs2YpxMfHxyuNe1IuqH7zzTdin7Zt22qMlz+vH330kVbHICIiehKwNmRtKEmsDVkbEilnACKiOpSVlSWWlQ1No08XLlzA3r17AVQO1fTDDz/AwsJCZby5uTkiIiLEekREhMJQTqosXrwYLVq0ULl9+vTpYjktLU0M4fSoRYsWieUPP/wQPXv2VHvc9957D15eXgAqh7//448/NObaoUMHzJ49W2McALXnSp6BgYFC7rt379ZqP31buXKlWO7UqZPa99m6dWt8/vnnYv3AgQO4fPmyxmO0bt0a8+bNU7nd1dUVr776qlg/duxYtZisrCwxjJSDgwNatWql8bhA5bD/RERE1DBdvnwZH330kVg3MTFBaGgoAGDjxo3IzMwEUDm05ldffaW2rdatW2Pu3LlifdWqVVrlEBkZqXX9VsXLywsffPCByu1hYWGQyWQAKodBNTY2xpo1a2BkpHxGuoCAALRp00asHz9+XGnc8uXL8fDhQwBAnz59MGvWLLV59uzZE2FhYQAqp9ZZvXq10jht37+pqSm++OILsa5L/fr+++/D29tb5fbPP/8czzzzjFhXleuTYsSIEeK8Xrx4UeVnCgBJSUliGFWZTIZx48bVSY5EREQNDWvDSqwNWRuyNqR/C3a2E1Gdys7OFstWVlZa7RMWFobg4GC1/+7cuVNtv82bN4vO8v79+8PDw0Pjsbp27YqWLVsCqJz/W908RgDQpEkTDBs2TG2Mh4cHmjVrJtaVtXnlyhXExcUBAIyNjfHWW29pzFUmk2H48OFiPSoqSuM+48ePh4GB/v/r9/DwgJ2dHQAgJSUF9+/f1/sx1MnPz8fhw4fF+tSpUzW+z7Fjx4rvoCRJWhXS48aNU/kDoor8TRLKPmv5eaQyMzPFHPJERET0ZCkuLkZycjIWLlwIPz8/hTp36tSpov77/vvvxeuTJ0/WWEsAlRexqmhT43Xq1AmdOnXSJX0AlfWQupqpWbNmcHFxEetBQUEK68p07dpVLKuqpeXPybRp07TKVddzoomfn59YPnXqlFb7GBkZITw8XG2MiYmJwlyUO3furFF+DUWTJk0wdOhQsb5u3TqVsfLb+vTpA3d399pMjYiIqEFhbagca0PWhgBrQ3r6af6fnIhIj4qLi8Vyo0aNtNonJiYGycnJamOKioqU7lclMDBQywwBb29vpKSkAABOnz6Ntm3bqozt3LkzjI2NNbbp4uIiOlWVdUTL59qhQwfY2NhonWuV06dPa4zX9LS8Knfu3MH+/ftx5swZZGRkIC8vD6WlpQox+fn5ACo7rm/dugVra+saHasmTp06hYqKCrEeEhKicR9TU1P07t0b27dvBwBxs4M63bt31xgj/yND2WdtY2MDDw8PXLt2DZIkYcCAAVi7di06dOigsW0iIiKqH0eOHBFP8WgSEhKCBQsWAAAePnyI+Ph4sU3bmtTZ2RnW1ta4f/8+MjMzkZ6ernZUqJrWeN26ddMY4+TkJEZm0ja+Sk5OTrXt165dw61bt8R6QECAFpkq1r0JCQmQJEntZ3L9+nUcOnQIZ8+eRVZWFvLy8lBWVqY09v79+ygsLIS5ubnaHDp37ixuMFUnJCREPIGWlZWF1NRUuLm5adyvoQoPD8f69esBAFu3bsXSpUvRuHFjhZiSkhJs2rRJrMtfVCYiInrasDZkbQiwNgRYGxJVYWc7EdUpa2trMZR8bm5urR4rKSlJLK9btw579uzRar9z586J5bt376qNlS8Y1ZEvzgoLC6ttl8/1xo0bCA4O1qpd+btkNeUKQKun++Wlp6fjnXfewS+//ILy8nKt93vw4IFOx3lcV69eFcv29vZwdHTUaj8fHx/R2S7fhirafN6aPmsAmDFjBqZOnQoAiI+PR8eOHdGxY0cEBwfD398f3bt31/qGCyIiImoY7Ozs8O6772LWrFniiaArV66I6WOAyid1tHl6CYAYShOorPPUXVDVtcar4uDgoDFGvrbRNV7ZDbHyda+RkRFee+01jW0+qrS0FLm5uUpHyrp06RKmTZuGAwcOaDUlVJUHDx5ovKCqbohQeV5eXpDJZOL4KSkpT/QFVT8/P3To0AFnzpxBQUEBtmzZUu2C6a5du8TvETs7O7z88sv1kCkREVHDwdqwejxrQ9aGRE8rdrYTUZ1q2rSp6GxXdjejMsqGGIqOjlZ792dFRYXCU8UJCQm6Jfr/NHUaa/t0vjxlhd29e/fEcmZmJvbv369zu9p0cFtaWmrd3oULFxAQECA+L13Ij2BQF+Q/a23uKFUWq833UdfPW1URP3nyZFy+fBnLly8XryUmJiIxMRELFy6EgYEBunTpgmHDhmHcuHFaT7lAREREtcPGxkZhCEygckhIKysruLu7o1u3bujduzdMTEwUYuRrPAA4dOhQjY6vqc7TpcaTp2tto49aSP6clJWV1ajuBSrPyaM10tGjRxESEqLyhkd1tKlfbW1ttWrLxMQEFhYWYuQnbX/3NGRhYWGYMmUKgMobmR+9oCo/TOioUaNq9DuJiIjoScHasGbxrA1ZGxI9rdjZTkR1yt3dXQwJf+HChVo7TlFRkcKw4jWljza0UVBQ8NhtaJOrtvO1l5eXY8iQIaKj3cTEBCNHjkS/fv3g5eWFZs2awczMTGEIfXd3d6SmptYs+cdUk+kJACj86KnLGwRkMhmWLVuGwYMH43//+x/27duncGdzRUUFTp48iZMnT2LevHlYsWIFhg8fXmf5ERERkSIfHx/8/vvvOu+njxoP0FznaVvjNQS1dU5yc3MxePBgcTHV0tIS48aNQ1BQEFq3bg0nJyeYmZnB0NBQ7KPt8K9VdK0zqy6o1vWNqLVh5MiRmD17NgoLCxEbG4ukpCS0a9cOQOXIXAcOHBCxHCaUiIiedqwN9Ye14ZOJtSGRIna2E1Gd6tmzp7hDMTs7G1evXoWnp6fej2NhYQFjY2Mxr3h0dDR69eql9+Poi/z85mPGjMGGDRvqLxkAe/bsEcM4GRsb4/Dhw3j++efV7pOXl1cXqSklf+eqLnnIT2VQl3PMV/H394e/vz8KCwtx7NgxxMTEICoqCidOnBDD9ufk5GDEiBEwNTXFK6+8Uuc5EhERUc3J1xdGRkYoKirSeqjQp5X8OXF3d8f169f10u769euRmZkJoPJps7i4OLRq1UplfE1qV132kY99GkYpsrKywpAhQ/Dtt98CqHxaacmSJQCADRs2iAvc3bt3h5eXV32lSURE1KCxNqyOteGTibUhkaIn5xYnInoqPNrh/dNPP9Xasezt7cXylStXau04+iA/x1FDyFX+7sPhw4dr7GgvLCxUGMq9rsl/1jdv3hQ3WWjy6Fzv9cXc3BwvvvgiPvvsM8TExODOnTtYuHChwtxQ7777br3lR0RERDUjX+OVlZXh77//rr9kGgj5c5KWlqYw/+jjkK9f3377bbUXUwHg1q1bOh9D28/v1q1bCqMWaTOf6ZMgPDxcLG/atAklJSWQJEnhRmE+uURERKQaa8PqWBs+uVgbEv2Dne1EVKd69OiBNm3aiPXIyEitO0Z11a1bN7Fc0zmQ6op8rqdOnarXp8SByuF+qvj6+mqMj4uL03kYe1XzmddEp06dxHJJSQkSExO12i82NlZpG/XN1tYW7733HlauXCleu3btmsLNAURERNTwtWzZEnZ2dmK9odekdcHX11fUhOXl5Thy5Ihe2tW1fj1+/LjOx4iPj9eqhj116pRYNjIyQvv27XU+Vm143Fq8e/fu4r3cvXsXu3btwoEDB8RUUpaWlhg6dKh+kiUiInoKsTasjrVh/WFtSKQ/7GwnojplYGCAmTNnivXr16/js88+q5VjBQUFieWdO3fi9u3btXIcffjPf/4j5g8vKSnB+vXr6zUf+RsgtJmvaOPGjVq1a2FhIZaLiop0T0yFVq1awcnJSaz/+OOPGve5ePEiTp8+Ldb9/f31lo++DBw4UGH9zp079ZQJERER1YRMJkOfPn3E+urVq+sxm4bB2toaXbt2FesRERF6abe26ld56enpOHbsmMa4LVu2iOXOnTvDzMxM52PVBn3U4mFhYWJ53bp1WLdunVgfNmyYwjGIiIhIEWvD6lgb1h/WhkT6w852IqpzY8aMUSiiFixYUCtzlI8cOVLcLfrw4UO89dZben2aWp+srKwwduxYsf7ZZ5/pbY6immjWrJlY1lQ0xsXF4fvvv9eqXfkOcX0/pS1//iIiIjQO5fTee++JZQcHB4SGhuo1H1V0+Q4WFBQorDdt2lTf6RAREVEtmz59ulhOSEjA0qVL6y+ZBkL+nOzatQs7d+587DZ1qV9//vnnGj819dFHH6mt586cOaMwVdaYMWNqdJzaoI9afNSoUeIC8YEDBxQ+u/Hjxz9WfkRERP8GrA2rY21YP1gbEukPO9uJqM4ZGxtj27ZtouOwvLwc48aNw5tvvqnV/Dh5eXnYvn27xjgLCwuFp+Z37NiBESNGaByi/cGDB1ixYgWGDRum8Rj6NHfuXHFOsrOzERgYiPj4eLX7VFRU4NChQwgODkZycrLecunVq5dY3rZtm8phrU6fPo0BAwagvLxcq3blh2pPSEhAdHT0Y+Upb8qUKbCysgJQeTdm//79kZGRUS1OkiS8//772LNnj3ht9uzZaNSokd5yUefo0aMIDQ1FdHS02mK8vLwcH3zwgVh3cnJC69at6yJFIiIi0iM/Pz8MGTJErM+cOROLFy/WWD+lpqZi9uzZmD9/fm2nWOcGDx6M7t27A6iszYYPH45NmzZp3O/ChQuYOHEivv3222rb5OvXFStW4Pz580rb+OOPPx7rImd0dDRmzJihdAql1NRUDBo0SHy2Li4uGDlyZI2PpW/ytfjWrVtrNDeptbU1Bg8eDKDyt0jV/KPe3t7w8/PTT6JERERPMdaG1bE2rB+sDYn0x6i+EyCifyc3NzccOnQIAwYMQFpaGoDKoZM2btyIwMBABAYGwsPDA7a2tjAwMEBubi6uXr2KuLg47N27F7m5uaItExMTMQT7oyZNmoTY2Fh89913ACqHF//9998xfPhw9OzZU9zBl52djQsXLuDEiRM4ePAgSkpK6rwgcHFxwY8//oh+/fqhrKwMqamp6Nq1K0JCQtCvXz94enrCwsICDx48wI0bNxAfH499+/aJDmV9PrU/dOhQvP/++7h9+zbKysoQEhKCCRMmIDg4GDY2NsjIyMBvv/2GzZs3o6ysDEFBQbh48aL4LFVp27YtOnbsiMTEREiShMDAQPj4+MDV1RVGRv/8SYqMjISDg4NOOTdv3hzLli3D6NGjAQDnz59Hu3btMHHiRPj7+8Pc3ByXL1/G+vXrERcXJ/br2bMnZsyYodOxHockSdi7dy/27t0LV1dXhISEoEuXLnB1dYWlpSXy8vJw/vx5bNq0CWfPnhX7zZkzR2EuJSIiInpyrF27FklJSUhKSkJ5eTneffddrF69Gq+//jo6d+4MW1tbPHz4EJmZmThz5gyOHDki6hX50XieFgYGBvjpp5/g6+uLjIwMFBUV4Y033sBXX32F1157DR06dICVlRUKCwtx+/ZtJCQk4ODBg+Ii6XPPPVetzfDwcHz55ZcoKipCbm4uunfvjkmTJiEwMBAWFha4ceMGduzYIW7anTBhAtauXatT3gMHDsTvv/+OpUuX4tixY5gwYQLatGmDgoICHD16FBEREeJ3ikwmw8qVK9G4cePHPFv688orr2DGjBkoLi5Geno6PDw80KlTJ9jZ2Yk608HBAZGRkWrbCQ8PF7+vqkyYMKHW8iYiInrasDZUxNqwfrA2JNIfdrYTUb3p2LEjTp06hbCwMPGU8cOHD7Fv3z7s27dPqzYGDRqERYsWKQwN9KgNGzbAwcEBixcvBgDk5ORg5cqVWLly5eO/CT0LCgrCgQMH8Nprr+HevXuQJAm//fYbfvvttzrNw8zMDJs3b0ZISAhKSkpQWlqKVatWYdWqVdVivby88P3338PX11erttesWYMXX3wR9+/fBwCcPXtWoVMZAL7++usa5f3GG2/g/v37mD59OiRJQk5ODhYuXIiFCxcqje/Rowd+/fXXeuvETktLQ2RkpMaideLEiZg6dWodZUVERET6ZmlpiZiYGAwZMgQHDx4EAKSkpGDevHn1nFn9cXZ2RmxsLAYOHIjExEQAlSMfJSQk1Ki95s2bY/Xq1Rg9ejQkSUJ+fj4WLVqERYsWVYv19/fH8uXLdb6g2rFjR/Tr1w9vvvkm4uPj1Y5C9dVXX2HAgAE6v4/a5OjoiGXLlmHSpEmoqKhAcXExTpw4oRDj5uamsZ0ePXqgXbt2SEpKAlB58/OoUaNqJWciIqKnEWvD6lgb1j3WhkT6w0fkiKheOTo6Yvfu3Th58iSGDh2q1ZzUnp6emDNnDpKTk7F9+3Z4enqqjTcwMMCiRYsQGxuLvn37KjxB/SiZTIaOHTti3rx5CvPp1KWAgAAkJydjzpw5sLW1VRvr5OSEsWPHIioqCm3atNFrHi+88AKio6PRvn17pdvNzc3x5ptv4uTJk7C3t9e63S5duuD8+fOYO3cuunXrhqZNm6r9THT19ttv4/jx4/D391cZ4+joiCVLliAqKkoMPV9X2rdvj08++QRdunSBoaGh2tjnnnsO27dvR0REBJ9qJyIiesLZ2Nhg//792LJlCzp27Kg21sTEBL1798aaNWswd+7cukmwHrRo0QInT55EREQEWrVqpTa2cePG6N+/P3788UeVQ32OGjUKu3btwjPPPKN0u42NDebOnYvDhw/D1NS0RjmHhYVh7969KvNt2bIl9u3bh2nTptWo/doWHh6OkydPIjw8HN7e3mjSpEmN6kz53wiDBg3S6nccERER/YO1YXWsDesea0Mi/ZBJ+hx3mIjoMVVUVCAxMREpKSm4e/cucnJyYGZmBhsbGzg7O4uhlB5HXl4e/vzzT9y4cQPZ2dkwNDSEtbU1WrZsCR8fH9jZ2enp3Ty+iooKJCQk4Ny5c7h79y6Ki4thaWkJV1dXeHl56b2DXRlJksTdmTk5ObCxsYGrqysCAgIa1NBHyqSnpyMmJgYZGRkoLi6Gvb09vL294evr2yA6r/Pz88X3PSsrC8XFxWjcuDGcnZ3RpUsXlT8GiIiI6Ml369YtHD9+HLdv38aDBw9gamoKOzs7tGnTBh06dICZmVl9p1jnrl27hri4OGRmZiIvLw8WFhZwdHTEs88+i/bt28PY2FirdsrKynDixAmcOXMGubm5sLOzg7u7OwICAtCoUSO95RsbG4sLFy7gzp07aNq0KXx8fNCtWzfIZDK9HaMhysvLg5OTEwoLCwEABw8eRO/eves5KyIioicba8PqWBs+GVgbErGznYiIiIiIiIiItBQREYFJkyYBqBx17MqVK0/9RWQiIiIiUo61IRGHkSciIiIiIiIiIi2UlZVhyZIlYn3ixIm8mEpERET0L8XakKgSO9uJiIiIiIiIiEit8vJyzJkzB1euXAEAWFtbIzw8vJ6zIiIiIqL6wNqQ6B9G9Z0AERERERERERE1PFu2bMGWLVtQXFyM8+fP4+bNm2LbBx98ACsrq3rMjoiIiIjqEmtDIuXY2U5ERERERERERNVcunQJu3btqvb6Sy+9hHfeeaceMiIiIiKi+sLakEg5DiNPRERERERERERqmZqawsfHB19++SX27NkDQ0PD+k6JiIiIiOoJa0Oif8gkSZLqOwkiIiIiIiIiIiIiIiIiIqInCZ9sJyIiIiIiIiIiIiIiIiIi0hE724mIiIiIiIiIiIiIiIiIiHTEznYiIiIiIiIiIiIiIiIiIiIdsbOdiIiIiIiIiIiIiIiIiIhIR+xsJyIiIiIiIiIiIiIiIiIi0hE724mIiIiIiIiIiIiIiIiIiHTEznYiIiIiIiIiIiIiIiIiIiIdsbOdiIiIiIiIiIiIiIiIiIhIR/8HKk+Jzt7hvLMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 2000x500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "models = ['random', 'GAT', 'GraphSAGE', 'GCN', 'GIN']\n",
    "\n",
    "df = pd.concat([pd.read_csv(f'results_optimization_{model}.csv') for model in models])\n",
    "\n",
    "axarr = plt.figure(figsize=(20, 5), layout=\"constrained\").subplot_mosaic(\n",
    "    \"\"\"\n",
    "    ABC\n",
    "    ADE\n",
    "    \"\"\"\n",
    ")\n",
    "\n",
    "# pandas dataframe (df) columns: iter, peptide, exp_value\n",
    "df['iter'] = df['iter'].replace({0: 'Init.', 1: '1', 2: '2', 3: '3', 4: '4', 5: '5'})\n",
    "\n",
    "sns.lineplot(x='iter', y='exp_value', hue='model', data=df, ax=axarr['A'], errorbar=('ci', 95), err_style='bars', linewidth=3.5, err_kws={'lw': 3.5})\n",
    "\n",
    "axarr['A'].set_xlim([-0.9, 5.5])\n",
    "axarr['A'].set_ylim([-9, -3])\n",
    "axarr['A'].set_ylabel('Permeability', fontsize=25)\n",
    "axarr['A'].set_xlabel('Generations', fontsize=25)\n",
    "axarr['A'].xaxis.set_tick_params(labelsize=25)\n",
    "axarr['A'].yaxis.set_tick_params(labelsize=25)\n",
    "\n",
    "models = ['GAT', 'GraphSAGE', 'GCN', 'GIN']\n",
    "\n",
    "for ax, model in zip(['B', 'C', 'D', 'E'], models):\n",
    "    df[df['model'] == 'random']['exp_value'].hist(ax=axarr[ax], density=False, alpha=0.65, bins=20)\n",
    "    df[df['model'] == model]['exp_value'].hist(ax=axarr[ax], density=False, alpha=0.65, bins=20)\n",
    "    axarr[ax].set_xlim([-9.5, -3.5])\n",
    "    axarr[ax].set_ylim([0, 130])\n",
    "    axarr[ax].xaxis.set_tick_params(labelsize=25)\n",
    "    axarr[ax].yaxis.set_tick_params(labelsize=25)\n",
    "    axarr[ax].set_title(model)\n",
    "\n",
    "axarr['D'].set_xlabel('Permeability', fontsize=25)\n",
    "axarr['E'].set_xlabel('Permeability', fontsize=25)\n",
    "axarr['B'].set_ylabel('#Peptides', fontsize=25)\n",
    "axarr['D'].set_ylabel('#Peptides', fontsize=25)\n",
    "\n",
    "sns.despine()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4c98ab5-124e-456f-acc5-08e3744dc046",
   "metadata": {},
   "source": [
    "# Results comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "abbcecc8-5dff-4f06-9a2c-317c48c7b1a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "models = ['random', 'WL', 'MAP4', 'MAP4C', 'GAT', 'GraphSAGE', 'GCN', 'GIN']\n",
    "\n",
    "df = pd.concat([pd.read_csv(f'results_optimization_{model}.csv') for model in models])\n",
    "\n",
    "axarr = plt.figure(figsize=(20, 10), layout=\"constrained\").subplot_mosaic(\n",
    "    \"\"\"\n",
    "    ABC\n",
    "    ADE\n",
    "    AFG\n",
    "    AHI\n",
    "    \"\"\"\n",
    ")\n",
    "\n",
    "# pandas dataframe (df) columns: iter, peptide, exp_value\n",
    "df['iter'] = df['iter'].replace({0: 'Init.', 1: '1', 2: '2', 3: '3', 4: '4', 5: '5'})\n",
    "\n",
    "sns.lineplot(x='iter', y='exp_value', hue='model', data=df, ax=axarr['A'], errorbar=('ci', 95), err_style='bars', linewidth=3.5, err_kws={'lw': 3.5})\n",
    "\n",
    "axarr['A'].set_xlim([-0.9, 5.5])\n",
    "axarr['A'].set_ylim([-9, -3])\n",
    "axarr['A'].set_ylabel('Permeability', fontsize=25)\n",
    "axarr['A'].set_xlabel('Generations', fontsize=25)\n",
    "axarr['A'].xaxis.set_tick_params(labelsize=25)\n",
    "axarr['A'].yaxis.set_tick_params(labelsize=25)\n",
    "\n",
    "for ax, model in zip(['B', 'C', 'D', 'E', 'F', 'G', 'H', 'I'], models):\n",
    "    df[df['model'] == 'random']['exp_value'].hist(ax=axarr[ax], density=False, alpha=0.65, bins=20)\n",
    "    if model != 'random':\n",
    "        df[df['model'] == model]['exp_value'].hist(ax=axarr[ax], density=False, alpha=0.65, bins=20)\n",
    "    axarr[ax].set_xlim([-9.5, -3.5])\n",
    "    axarr[ax].set_ylim([0, 130])\n",
    "    axarr[ax].xaxis.set_tick_params(labelsize=25)\n",
    "    axarr[ax].yaxis.set_tick_params(labelsize=25)\n",
    "    axarr[ax].set_title(model)\n",
    "\n",
    "axarr['H'].set_xlabel('Permeability', fontsize=25)\n",
    "axarr['I'].set_xlabel('Permeability', fontsize=25)\n",
    "axarr['B'].set_ylabel('#Peptides', fontsize=25)\n",
    "axarr['D'].set_ylabel('#Peptides', fontsize=25)\n",
    "axarr['F'].set_ylabel('#Peptides', fontsize=25)\n",
    "axarr['H'].set_ylabel('#Peptides', fontsize=25)\n",
    "\n",
    "sns.despine()\n",
    "\n",
    "plt.savefig('figure_results_comparison_all_models.png', dpi=300, bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a7bada5-ebc4-45c1-82e3-cf11d479bbe6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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