{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4e44363e-436d-4680-a6f0-c8d791410d79",
   "metadata": {},
   "source": [
    "# Sampling Chemical Space for Drug Discovery using the MolMIM NIM\n",
    "\n",
    "```{note}\n",
    "This notebook assumes that you have the MolMIM NIM available at the following address: `http://localhost:8000`. If your container is available at different address, you will need to modify the URL below.\n",
    "```\n",
    "\n",
    "In the field of drug discovery, identifying novel and effective compounds is a crucial step in the development of new medicines. Chemical space, the vast expanse of all possible chemical compounds, is a complex landscape that can be difficult to navigate. Molecular generative AI models, such as MolMIM, can be used to sample this space and identify promising compounds.\n",
    "\n",
    "## Unguided Sampling: Exploring Chemical Space around a Starting Seed Molecule\n",
    "\n",
    "In this notebook, we will use MolMIM to perform unguided sampling around a starting seed molecule, upadacitinib. Unguided sampling involves generating new molecules without any specific goal or objective in mind. This can be useful for exploring new areas of chemical space and identifying novel compounds that may not have been considered before.\n",
    "\n",
    "## Guided Optimization: Improving a Seed Molecule's QED Score\n",
    "\n",
    "In addition to unguided sampling, we will also use MolMIM to perform guided optimization of the same seed molecule, upadacitinib. Guided optimization involves using a specific objective function, such as the Quantitative Estimate of Drug-likeness (QED) score, to guide the generation of new molecules. In this case, we will use the CMA-ES algorithm to optimize the QED score of upadacitinib.\n",
    "\n",
    "## CMA-ES: A Global Optimization Algorithm\n",
    "\n",
    "CMA-ES (Covariance Matrix Adaptation Evolution Strategy) is a global optimization algorithm that is well-suited for optimizing complex objective functions such as the QED score. It uses a population of candidate solutions to search for the optimal solution, and adapts the covariance matrix of the search distribution to improve the search process.\n",
    "\n",
    "## Objectives\n",
    "\n",
    "In this notebook, we will use MolMIM to:\n",
    "\n",
    "1. Perform unguided sampling around the seed molecule upadacitinib to explore chemical space.\n",
    "2. Perform guided optimization of upadacitinib using CMA-ES to improve its QED score.\n",
    "\n",
    "## Random Sampling Around a Seed Molecule\n",
    "\n",
    "In the code blocks below, we will step through how to randomly sample molecules in a similar chemical space to a seed molecule, in this case upadacitinib. We begin by importing the necessary libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ad0fdcb6-d1ad-4825-af28-ffb3c6e2680d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem.QED import qed\n",
    "from rdkit.Chem.AllChem import GetMorganFingerprintAsBitVect\n",
    "from rdkit.DataStructs import TanimotoSimilarity\n",
    "from rdkit import RDLogger                                                                                                                                                               \n",
    "RDLogger.DisableLog('rdApp.*')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75cc4392-148a-48fc-821a-aea7af418e75",
   "metadata": {},
   "source": [
    "This code block defines a function called `tanimoto_similarity` that calculates the Tanimoto similarity between two molecules. The function takes two parameters: `smiles`, the SMILES string of the molecule to be compared, and `reference`, the SMILES string of the reference molecule. The function first gets the fingerprint parameters, then creates the fingerprint for the reference molecule. It then validates the input molecule by converting its SMILES string to a molecule object and checks if the object is None. If the object is None, it returns 0. Otherwise, it creates the fingerprint for the input molecule and calculates the Tanimoto similarity between the two fingerprints. The function returns the calculated Tanimoto similarity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ea403fca-4f36-4c7d-9426-466c0d198676",
   "metadata": {},
   "outputs": [],
   "source": [
    "def tanimoto_similarity(smiles, reference: str):\n",
    "    # Get fingerprint params\n",
    "    fingerprint_radius_param = 2\n",
    "    fingerprint_nbits = 2048\n",
    "\n",
    "    # Handle the reference molecule\n",
    "    reference_mol = Chem.MolFromSmiles(reference)\n",
    "    reference_fingerprint = GetMorganFingerprintAsBitVect(\n",
    "        reference_mol, radius=fingerprint_radius_param, nBits=fingerprint_nbits\n",
    "    )\n",
    "\n",
    "    # Validate the other molecule\n",
    "    mol = Chem.MolFromSmiles(smiles)\n",
    "    if mol is None:\n",
    "        return 0\n",
    "\n",
    "    fingerprint = GetMorganFingerprintAsBitVect(mol, radius=fingerprint_radius_param, nBits=fingerprint_nbits)\n",
    "\n",
    "    # Calculate and return the Tanimoto similarity\n",
    "    return TanimotoSimilarity(fingerprint, reference_fingerprint)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67ed7ab6-1b6d-452c-b189-4668863f289e",
   "metadata": {},
   "source": [
    "This next block defines the molecule of interest, which is the SMILES string representation of the molecule upadacitinib. This molecule will be used as a reference for calculating Tanimoto similarity and QED scores for other molecules in the script."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c921dd1a-10e0-4463-807e-5974482bfe63",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the molecule of interest\n",
    "upadacitinib = 'CCC1CN(CC1C2=CN=C3N2C4=C(NC=C4)N=C3)C(=O)NCC(F)(F)F'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "498b2774-08cf-4e0a-bcc2-b02747047732",
   "metadata": {},
   "source": [
    "This code block defines the parameters for sampling radii and initializes lists and a dictionary to store the results of the sampling process. The `radii` variable is set to a numpy array of 10 evenly spaced values between 0.3 and 1.0, which will be used to sample different radii for the molecular fingerprint calculation. The `valid_smiles_counts`, `avg_tanimoto_similarities`, and `avg_qed_scores` lists will be used to store the number of valid SMILES strings, the average Tanimoto similarity, and the average QED score for each radius, respectively. The `results` dictionary will be used to store the results for each radius, with the radius as the key and a dictionary containing the results as the value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "72c2b6af-3f22-4ea1-801e-c06c72a45db5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the radii to sample\n",
    "radii = np.linspace(0.3, 1.0, num=10)\n",
    "\n",
    "# Initialize lists to store the results\n",
    "valid_smiles_counts = []\n",
    "avg_tanimoto_similarities = []\n",
    "avg_qed_scores = []\n",
    "\n",
    "# Create a dictionary to store the results\n",
    "results = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ae1a7cb-9b9b-4eb3-aff6-71b16d907453",
   "metadata": {},
   "source": [
    "This code block is responsible for processing the results of the molecule sampling at different radii. It iterates over each radius, sends a request to the MolMIM sampling API, extracts the generated SMILES strings, filters out invalid ones, calculates the Tanimoto similarity and QED score for each valid SMILES string, and stores the results. After processing all radii, it creates a Pandas dataframe from the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ecdf41fb-a7ae-4af9-aeef-19910ef315c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loop through each radius\n",
    "for radius in radii:\n",
    "    # Create a dictionary to store the results for this radius\n",
    "    radius_results = {'tanimoto_similarity': [], 'qed_score': []}\n",
    "    \n",
    "    # Create the request data\n",
    "    data = {'sequences': [upadacitinib], 'num_molecules': 10, 'scaled_radius': radius}\n",
    "\n",
    "    # Send the request and get the response\n",
    "    headers = {'Content-Type': 'application/json'}\n",
    "    response = requests.post('http://localhost:8000/sampling', headers=headers, json=data)\n",
    "\n",
    "    # Parse the response as JSON\n",
    "    response_json = json.loads(response.text)\n",
    "\n",
    "    # Extract the generated SMILES strings\n",
    "    generated_molecules = response_json['generated'][0]\n",
    "\n",
    "    # Filter out invalid SMILES strings\n",
    "    valid_smiles = [m for m in generated_molecules if Chem.MolFromSmiles(m) is not None]\n",
    "    radius_results['valid_smiles'] = len(valid_smiles)\n",
    "    \n",
    "    # Calculate Tanimoto similarity and QED score for each valid SMILES string\n",
    "    for smile in valid_smiles:\n",
    "        mol = Chem.MolFromSmiles(smile)\n",
    "        if mol:\n",
    "            tanimoto = tanimoto_similarity(smile, upadacitinib)\n",
    "            qed_score = qed(mol)\n",
    "            radius_results['tanimoto_similarity'].append(tanimoto)\n",
    "            radius_results['qed_score'].append(qed_score)\n",
    "\n",
    "    # Get the average of Tanimoto and QED scores\n",
    "    radius_results['tanimoto_similarity'] = np.mean(radius_results['tanimoto_similarity'])\n",
    "    radius_results['qed_score'] = np.mean(radius_results['qed_score'])\n",
    "    \n",
    "    # Store the results for this radius\n",
    "    results[radius] = radius_results\n",
    "\n",
    "# Create a Pandas dataframe from the results\n",
    "df = pd.DataFrame(results).T.reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83dc583f-8e98-4d00-9d39-d9566523d818",
   "metadata": {},
   "source": [
    "The following block creates three plots to visualize the results of the sampling process. The plots show the number of valid SMILES strings, the average Tanimoto similarity, and the average QED score at each radius. The plots are created using the matplotlib library and are displayed using the `plt.show()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0c342445-0873-4d73-907f-a731a93a20e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the plots\n",
    "plt.figure(figsize=(20, 4))\n",
    "\n",
    "# Plot the number of valid SMILES strings at each radius\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.plot(df['index'], df['valid_smiles'])\n",
    "plt.xlabel('Radius')\n",
    "plt.ylabel('Number of valid SMILES strings')\n",
    "plt.title('Number of valid SMILES strings at each radius')\n",
    "\n",
    "# Plot the average Tanimoto similarity at each radius\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.plot(df['index'], df['tanimoto_similarity'])\n",
    "plt.xlabel('Radius')\n",
    "plt.ylabel('Average Tanimoto similarity')\n",
    "plt.title('Average Tanimoto similarity at each radius')\n",
    "\n",
    "# Plot the average QED score at each radius\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.plot(df['index'], df['qed_score'])\n",
    "plt.xlabel('Radius')\n",
    "plt.ylabel('Average QED score')\n",
    "plt.title('Average QED score at each radius')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "992b9d30-d0ec-4654-8b69-f986934ed207",
   "metadata": {},
   "source": [
    "## Guided Molecular Generation with CMA-ES\n",
    "\n",
    "In contrast to the random sampling of the latent space described above, we can use a black box optimizer, called CMA-ES, to perform guided optimization of the a molecule's property through sampling of the latent space. In the blocks below, we use CMA-ES to optimize the QED score of the generated molecules while preseving a level of similary to the seed molecule, upadacitinib.\n",
    "\n",
    "This first block initializes variables to store the results of the script and sets up the parameters for the experiment. It defines three lists to store the counts of valid SMILES strings, average Tanimoto similarities, and average QED scores. It also creates an empty dictionary to store the results and a list of minimum similarities to be used in the experiment, ranging from 0.1 to 0.7 with 10 evenly spaced values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "625dfdea-b453-4e05-92aa-4aa4aea3544b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize lists to store the results\n",
    "valid_smiles_counts = []\n",
    "avg_tanimoto_similarities = []\n",
    "avg_qed_scores = []\n",
    "\n",
    "# Create a dictionary to store the results\n",
    "results = {}\n",
    "\n",
    "# Create a list of minimum similarities\n",
    "min_sims = np.linspace(0.1, 0.7, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fb8b268-4da7-4854-9cc9-8a8e7708f3bb",
   "metadata": {},
   "source": [
    "The following block is the main loop of the script, where it iterates over each minimum similarity value in the `min_sims` list. For each minimum similarity, it generates molecules using the CMA-ES algorithm, filters out invalid SMILES strings, calculates the Tanimoto similarity and QED score for each valid SMILES string, and stores the results. The results are stored in a dictionary called `results`, where the keys are the minimum similarity values and the values are dictionaries containing the number of valid SMILES strings, average Tanimoto similarity, and average QED score. After the loop, the results are converted into a Pandas dataframe for further analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5dac62d8-a15d-4d3d-a692-861ec4ad298e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loop through each radius\n",
    "for min_sim in min_sims:\n",
    "    # Create a dictionary to store the results for this min_sim\n",
    "    min_sim_results = {'tanimoto_similarity': [], 'qed_score': []}\n",
    "    \n",
    "    # Create the request data\n",
    "    data = {\n",
    "        'smi': upadacitinib,\n",
    "        'algorithm': 'CMA-ES',\n",
    "        'num_molecules': 10,\n",
    "        'property_name': 'QED',\n",
    "        'minimize': False,\n",
    "        'min_similarity': min_sim,\n",
    "        'particles': 20,\n",
    "        'iterations': 3\n",
    "    }\n",
    "\n",
    "    # Send the request and get the response\n",
    "    headers = {'Content-Type': 'application/json'}\n",
    "    response = requests.post('http://localhost:8000/generate', headers=headers, json=data)\n",
    "\n",
    "    # Parse the response as JSON\n",
    "    response_json = json.loads(response.text)\n",
    "\n",
    "    # Extract the generated SMILES strings\n",
    "    generated_molecules = [i['smiles'] for i in response_json['generated']]\n",
    "\n",
    "    # Filter out invalid SMILES strings\n",
    "    valid_smiles = [m for m in generated_molecules if Chem.MolFromSmiles(m) is not None]\n",
    "    min_sim_results['valid_smiles'] = len(valid_smiles)\n",
    "    \n",
    "    # Calculate Tanimoto similarity and QED score for each valid SMILES string\n",
    "    for smile in valid_smiles:\n",
    "        mol = Chem.MolFromSmiles(smile)\n",
    "        if mol:\n",
    "            tanimoto = tanimoto_similarity(smile, upadacitinib)\n",
    "            qed_score = qed(mol)\n",
    "            min_sim_results['tanimoto_similarity'].append(tanimoto)\n",
    "            min_sim_results['qed_score'].append(qed_score)\n",
    "\n",
    "    # Get the average of Tanimoto and QED scores\n",
    "    min_sim_results['tanimoto_similarity'] = np.mean(min_sim_results['tanimoto_similarity'])\n",
    "    min_sim_results['qed_score'] = np.mean(min_sim_results['qed_score'])\n",
    "    \n",
    "    # Store the results for this min_sim\n",
    "    results[min_sim] = min_sim_results\n",
    "\n",
    "# Create a Pandas dataframe from the results\n",
    "df = pd.DataFrame(results).T.reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba8b124f-0f42-49de-a914-a07d460a5e03",
   "metadata": {},
   "source": [
    "The following block creates three plots to visualize the results of the script. The first plot shows the number of valid SMILES strings generated at each minimum similarity threshold. The second plot shows the average Tanimoto similarity of the generated molecules at each minimum similarity threshold. The third plot shows the average QED score of the generated molecules at each minimum similarity threshold. The plots are arranged in a single figure with three subplots, and the figure is displayed using the `plt.show()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "159b0b27-9a27-47fb-b815-c28284f8bcd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the plots\n",
    "plt.figure(figsize=(20, 4))\n",
    "\n",
    "# Plot the number of valid SMILES strings at each min_sim\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.plot(df['index'], df['valid_smiles'])\n",
    "plt.xlabel('Minimum similarity')\n",
    "plt.ylabel('Number of valid SMILES strings')\n",
    "plt.title('Number of valid SMILES strings at each minimum similarity')\n",
    "\n",
    "# Plot the average Tanimoto similarity at each radius\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.plot(df['index'], df['tanimoto_similarity'])\n",
    "plt.xlabel('Minimum similarity')\n",
    "plt.ylabel('Average Tanimoto similarity')\n",
    "plt.title('Average Tanimoto similarity at each minimum similarity')\n",
    "\n",
    "# Plot the average QED score at each radius\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.plot(df['index'], df['qed_score'])\n",
    "plt.xlabel('Minimum similarity')\n",
    "plt.ylabel('Average QED score')\n",
    "plt.title('Average QED score at each minimum similarity')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d13a6f4-f1d5-436a-8998-0d9ae8db5657",
   "metadata": {},
   "source": [
    "Note that the `min_similarity` parameter is not a hard cutoff, but rather the minimum similarity necessary to receive a full score for the Tanimoto similarity component of the scoring function. Generally the average similarity will increase with increasing `minimum_similarity` thresholds, but the similarity of the generated molecules to the seed is not guaranted to exceed that threshold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b3ae767-0fb7-4c5a-944e-2c4edbb18030",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
