{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This is a reformatted and lightly revised version of a [blog post from 2019](https://rdkit.blogspot.com/2019/11/additive-fingerprints.html).\n", "\n", "The goal here is to evaluate one strategy for enabling similarity search of virtual libraries defined via molecular fragments that can be combined using simple rules. One obvious approach is to just enumerate the molecules in the library, generate their fingerprints, and the calculate similarity. This is clearly impractical if the libraries are really big, so I want to see if it's possible to make things faster by skipping the enumeration step and approximating the fingerprint of a molecule with the sum of the fingerprints that make it up plus constant correction terms.\n", "\n", "For example, suppose I form a molecule by combining the two fragments `c1ccccc1-[16*]` and `CCCCO-[3*]` to make `c1ccccc1-OCCCC`, I want to see how closely I can approximate the fingerprint of the final molecule by adding the fingerprints of the two fragments together with a correction term that is characteristic of the bond type being formed (in this case the `16-3` combination rule). This obviously won't work well with fingerprints like Atom Pairs that include terms encompassing the entire molecule, but may be useful for more \"local\" descriptors like the Morgan fingerprint, topological torsions, or the RDKit fingerprint.\n", "\n", "This isn't a really new idea - it's inspired by the idea behind [Truchon and Bayly's GLARE algorithm](http://pubs.acs.org/doi/abs/10.1021/ci0504871) and I've explored it a bit in the past - but I haven't seen it published and never really dug into it. That's what this post (likely a series of posts) is for.\n", "\n", "Demonstrating that this really works requires a fair amount of effort, but we can get a good initial readout by comparing the calculated similarities between a bunch of real molecules calculated with their \"real\" fingerprints and then with the \"additive\" fingerprints. I'll start here with the Morgan fingerprint, since that's one of the more popular ones in the RDKit (and one where the approach seems likely to work). If that goes well, I'll try another fingerprint type or two and maybe do some more comprehensive validation.\n", "\n", "> Since the original post was published, a [paper from Matthias Rarey's group](https://pubs.acs.org/doi/full/10.1021/acs.jcim.0c00850) explored an efficient strategy for searching combinatorial library spaces using topological fingerprints.\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2024-09-20T05:20:59.978449Z", "start_time": "2024-09-20T05:20:59.965723Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2024.03.5\n" ] } ], "source": [ "import numpy as np\n", "from rdkit import DataStructs\n", "from rdkit import Chem\n", "from rdkit.Chem import rdFingerprintGenerator\n", "from rdkit.Chem.Draw import IPythonConsole\n", "from rdkit.Chem.MolStandardize import rdMolStandardize\n", "from rdkit.Chem import Draw\n", "from rdkit.Chem import BRICS\n", "import rdkit\n", "print(rdkit.__version__)\n", "from matplotlib import pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Start by reading in the set of molecules we'll work with. This dataset was described in a recent [RDKit blog post](https://rdkit.blogspot.com/2019/10/a-new-lessel-and-briem-like-dataset.html).\n", "\n", "Bring in the molecules and do salt stripping:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2024-09-20T05:20:14.197264Z", "start_time": "2024-09-20T05:20:08.117933Z" } }, "outputs": [ { "data": { "text/plain": [ "6359" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from rdkit import rdBase\n", "rdBase.DisableLog('rdApp.info')\n", "ms = [rdMolStandardize.FragmentParent(x) for x in Chem.SmilesMolSupplier('../data/BLSets_selected_actives.txt')]\n", "_ = [Chem.GetSymmSSSR(m) for m in ms]\n", "len(ms)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to fragment the molecules we're going to be using the [BRICS fragmentation rules](http://doi.wiley.com/10.1002/cmdc.200800178) as implemented in the RDKit. Note that the RDKit implementation has differs from the published BRICS rules in that atom types 2 and 5 (both Ns) have been combined with each other. This is, unfortunately not (yet) documented outside of the code itself, but there's an explanation here: https://github.com/rdkit/rdkit/blob/master/Code/GraphMol/ChemTransforms/MolFragmenter.cpp#L101\n", "\n", "For the purposes of this evaluation as to whether or not additive fingerprints work, we will only be using the ten most common BRICS bonds in the dataset. Let's start by finding those.\n", "\n", "This is what the list of BRICS bonds for each molecule looks like:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2024-09-20T05:20:14.202874Z", "start_time": "2024-09-20T05:20:14.198923Z" } }, "outputs": [ { "data": { "text/plain": [ "[((2, 1), ('1', '5')),\n", " ((8, 7), ('1', '5')),\n", " ((6, 7), ('4', '5')),\n", " ((15, 20), ('5', '12')),\n", " ((8, 10), ('6', '13')),\n", " ((6, 30), ('8', '15')),\n", " ((23, 24), ('8', '16'))]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(BRICS.FindBRICSBonds(ms[0]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first element of each tuple contains the indices of the atoms defining the bond, the second element has the BRICS atom types of those atoms. For example, the first BRICS bond in the molecule above is between atom 2 (BRICS type 1) and atom 1 (BRICS type 5).\n", "\n", "Loop over all the molecules and count the number of times each bond type occurs:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2024-09-20T05:20:16.710986Z", "start_time": "2024-09-20T05:20:14.203972Z" } }, "outputs": [ { "data": { "text/plain": [ "[(7337, ('4', '5')),\n", " (4619, ('1', '5')),\n", " (3488, ('8', '16')),\n", " (2599, ('3', '16')),\n", " (1817, ('5', '16')),\n", " (1507, ('3', '4')),\n", " (997, ('6', '16')),\n", " (853, ('5', '15')),\n", " (789, ('8', '14')),\n", " (714, ('8', '15'))]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from collections import defaultdict, Counter\n", "cntr = Counter()\n", "for m in ms:\n", " bbnds = BRICS.FindBRICSBonds(m)\n", " for aids,lbls in bbnds:\n", " cntr[lbls] += 1\n", "freqs = sorted([(y,x) for x,y in cntr.items()],reverse=True)\n", "freqs[:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The top two bond types are `4-5`(alkyl C - amine) and `1-5` (the amide bond).\n", "\n", "Keep those top 10:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2024-09-20T05:20:16.714314Z", "start_time": "2024-09-20T05:20:16.712437Z" } }, "outputs": [], "source": [ "bondsToKeep = [y for x,y in freqs[:10]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to clarify what the more complex code below does, here's a worked example demonstrating fragmenting a single molecule along two bonds, both between BRICS atom types 1 and 5 (these bonds and the types come from the example above where we found the molecule's BRICS bonds):" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2024-09-20T05:20:18.088028Z", "start_time": "2024-09-20T05:20:16.715288Z" } }, "outputs": [ { "data": { "text/plain": [ "['[1*]C(=O)C(=O)C(NC(=O)[C@@H]1CC[C@H]2CN(S(=O)(=O)Cc3ccccc3)CC(=O)N21)[C@H]1CC[C@H](N)CC1.[5*]NC',\n", " '[1*]C(=O)[C@@H]1CC[C@H]2CN(S(=O)(=O)Cc3ccccc3)CC(=O)N21.[5*]NC(C(=O)C(=O)NC)[C@H]1CC[C@H](N)CC1']" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tm=Chem.FragmentOnSomeBonds(ms[0],[ms[0].GetBondBetweenAtoms(2,1).GetIdx(),ms[0].GetBondBetweenAtoms(8,7).GetIdx()],\n", " dummyLabels=[[1,5],[1,5]])\n", "[Chem.MolToSmiles(x) for x in tm]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To make it visual, here's a drawing of the two fragmentations:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2024-09-20T05:20:18.262798Z", "start_time": "2024-09-20T05:20:18.092745Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fms = Chem.FragmentOnSomeBonds(ms[0],[ms[0].GetBondBetweenAtoms(2,1).GetIdx(),ms[0].GetBondBetweenAtoms(8,7).GetIdx()],\n", " dummyLabels=[[1,5],[1,5]])\n", "Draw.MolsToGridImage(fms,subImgSize=(300,250))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's define some functions that we'll use for the rest of this:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2024-09-20T05:20:18.348232Z", "start_time": "2024-09-20T05:20:18.264046Z" } }, "outputs": [], "source": [ "def splitMol(mol,bondsToKeep):\n", " ''' fragments a molecule on a particular set of BRICS bonds. \n", " Partially sanitizes the results \n", " '''\n", " bbnds = BRICS.FindBRICSBonds(mol)\n", " bndsToTry = []\n", " lbls = []\n", " for aids,lbl in bbnds:\n", " if lbl in bondsToKeep:\n", " bndsToTry.append(mol.GetBondBetweenAtoms(aids[0],aids[1]).GetIdx())\n", " lbls.append([int(x) for x in lbl])\n", " if not bndsToTry:\n", " return []\n", " res = Chem.FragmentOnSomeBonds(mol,bndsToTry,dummyLabels=lbls)\n", " # We need at least a partial sanitization for the rest of what we will be doing:\n", " for entry in res:\n", " entry.UpdatePropertyCache(False)\n", " Chem.FastFindRings(entry)\n", " return res\n", "\n", "def getDifferenceFPs(mol,bondsToKeep,fpgen):\n", " ''' generates the difference fingerprint between the molecule\n", " and each of its fragmentations based on the BRICS bond types passed in.\n", " \n", " This calculates the sum of the fragment fingerprints by just generating the\n", " fingerprint of the fragmented molecule, so whole-molecule fingerprints like\n", " atom pairs will not work here. That's fine since they are unlikely to be useful\n", " with this approach anyway\n", " '''\n", " frags = splitMol(mol,bondsToKeep)\n", " if not frags:\n", " return []\n", " res = []\n", " # get the fingerprint for the molecule:\n", " molfp = fpgen.GetCountFingerprint(mol)\n", " # now loop over each fragmentation\n", " for frag in frags:\n", " # generate the fingerprint of the fragmented molecule\n", " # (equivalent to the sum of the fingerprints of the fragments)\n", " ffp = fpgen.GetCountFingerprint(frag)\n", " # and keep the difference\n", " res.append(molfp-ffp)\n", " return res\n", "\n", "def getSumFP(fragMol,fpgen,delta):\n", " ''' Constructs the sum fingerprint for a fragmented molecule using\n", " delta as a constant offset\n", " \n", " note that any elements of the fingerprint that are negative after adding the\n", " constant offset will be set to zero\n", " \n", " '''\n", " ffp = fpgen.GetCountFingerprint(fragMol)\n", " for idx,v in delta.GetNonzeroElements().items():\n", " tv = ffp[idx] + v\n", " if tv<0:\n", " tv = 0;\n", " ffp[idx] = tv\n", " return ffp\n", " " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2024-09-20T05:20:18.450797Z", "start_time": "2024-09-20T05:20:18.379029Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# demonstrate what splitMol returns:\n", "frags = splitMol(ms[0],bondsToKeep[2:3])\n", "Draw.MolsToGridImage(frags)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now walk through the analysis with a single bond type: `1-5`\n", "\n", "The first thing we need to do is determine the constant offset term for this bond type. We will do that by generating difference fingerprints for all the fragmentations it generates and then averaging those difference fingerprints.\n", "\n", "Start by generating the difference fingerprints for all the fragmentations using that bond type:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2024-09-20T05:20:22.585306Z", "start_time": "2024-09-20T05:20:19.109765Z" } }, "outputs": [], "source": [ "# for this analysis we will use a 4096 \"bit\" count-based Morgan fingerprints with a radius of 2\n", "fpgen = rdFingerprintGenerator.GetMorganGenerator(radius=2,fpSize=4096)\n", "dfps= []\n", "for m in ms:\n", " dfps.extend(getDifferenceFPs(m,bondsToKeep[1:2],fpgen))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now find the average value of the difference fingerprint, being careful to keep only integer values:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2024-09-20T05:20:22.628417Z", "start_time": "2024-09-20T05:20:22.586574Z" } }, "outputs": [ { "data": { "text/plain": [ "{226: -2, 264: -1, 1831: -1}" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "delta1 = dfps[0]\n", "for dfp in dfps[1:]:\n", " delta1 += dfp\n", "nfps = len(dfps)\n", "for k,v in delta1.GetNonzeroElements().items():\n", " delta1[k] = round(v/nfps)\n", "delta1.GetNonzeroElements()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To quickly summarize that this is telling us: We can simulate the MFP2 fingerprint for a molecule by adding the MFP2 fingerprints of its bond type `1-5` fragments and then substracting one each from the values for bits 264 and 1831 and two from the value for bit 226.\n", "\n", "Let's look at how well that works across all the molecules. We start by generating the full FP and the `1-5` fragment FP for every molecule that has a `1-5` fragment:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2024-09-20T05:20:26.476872Z", "start_time": "2024-09-20T05:20:22.629421Z" } }, "outputs": [], "source": [ "allfps = []\n", "for m in ms:\n", " frags = splitMol(m,bondsToKeep[1:2])\n", " if not frags:\n", " continue\n", " fp1 = fpgen.GetCountFingerprint(m)\n", " ffps = [getSumFP(x,fpgen,delta1) for x in frags]\n", " allfps.append((fp1,ffps))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2024-09-20T05:20:26.481011Z", "start_time": "2024-09-20T05:20:26.478207Z" } }, "outputs": [ { "data": { "text/plain": [ "2801" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(allfps)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And calculate the similarity of each molecule to itself by calculating the Tanimoto similarity between the full fingerprint and the fragment fingerprint:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2024-09-20T05:20:26.588111Z", "start_time": "2024-09-20T05:20:26.481896Z" } }, "outputs": [], "source": [ "self_tanis = []\n", "for fp1,ffps in allfps:\n", " self_tanis.extend([DataStructs.TanimotoSimilarity(fp1,x) for x in ffps])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Visualize those:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2024-09-20T05:20:34.867463Z", "start_time": "2024-09-20T05:20:34.751222Z" } }, "outputs": [ { "data": { "image/png": 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53NczMzOVlZWlmTNnKi8vTy6XS7169dL+/fs9NampqcrJydGSJUu0fv16HThwQP369fP6WfshQ4Zo8+bNWr58uZYvX67Nmzdr6NChp3GKAADgnGTOgCSTk5PjeV5VVWVcLpd58sknPW1//PGHcTqd5sUXXzTGGLNv3z4TFBRklixZ4qn56aefTKNGjczy5cuNMcZs3brVSDIbN2701GzYsMFIMt99991Jjc3tdhtJxu12n8kpAgCAs+hU/n779JqYHTt2qKioSL179/a0hYSEKCkpSbm5uZKk/Px8HT582KsmJiZGCQkJnpoNGzbI6XQqMTHRU3PVVVfJ6XR6ao5VXl6u0tJSrwcAADh3+TTEFBUVSZKio6O92qOjoz2vFRUVKTg4WE2bNq21pkWLFtX6b9GihafmWBkZGZ7rZ5xOJ98RAwDAOc4vdycde1uUMeaEt0odW3O8+tr6mTRpktxut+dRUFBwGiMHAAC28GmIcblcklRtt6S4uNizO+NyuVRRUaGSkpJaa3755Zdq/e/du7faLs9RISEhnu+E4bthAAA49/k0xMTFxcnlcmnlypWetoqKCq1du1bdu3eXJHXp0kVBQUFeNYWFhdqyZYunplu3bnK73fr88889NZ999pncbrenBgAANGyn/I29Bw4c0Pfff+95vmPHDm3evFlRUVFq06aNUlNTlZ6ervj4eMXHxys9PV1hYWEaMmSIJMnpdGrEiBEaN26cmjVrpqioKI0fP14dO3ZUz549JUkXX3yxrrvuOv3973/Xf//3f0uS/vGPf6hfv3666KKLfHHeAADAcqccYr744gtdc801nucPPvigJGnYsGHKzs7WhAkTVFZWplGjRqmkpESJiYlasWKF128gPPvsswoMDNSgQYNUVlamlJQUZWdnKyAgwFPz+uuva+zYsZ67mAYMGFDjd9MAAICGx2GMMXU9CH8oLS2V0+mU2+3m+hgAACxxKn+/+e0kAABgJUIMAACw0ilfEwMAqH9iJy7zS787n+zrl34BX2AnBgAAWIkQAwAArESIAQAAViLEAAAAKxFiAACAlQgxAADASoQYAABgJUIMAACwEiEGAABYiRADAACsRIgBAABWIsQAAAArEWIAAICVCDEAAMBKhBgAAGAlQgwAALASIQYAAFiJEAMAAKxEiAEAAFYixAAAACsRYgAAgJUIMQAAwEqEGAAAYCVCDAAAsBIhBgAAWIkQAwAArESIAQAAViLEAAAAKxFiAACAlQgxAADASoQYAABgJUIMAACwEiEGAABYiRADAACsRIgBAABWIsQAAAArEWIAAICVCDEAAMBKhBgAAGAlQgwAALASIQYAAFiJEAMAAKxEiAEAAFYixAAAACsRYgAAgJUIMQAAwEqEGAAAYCVCDAAAsBIhBgAAWIkQAwAArESIAQAAViLEAAAAKxFiAACAlQgxAADASoQYAABgJUIMAACwEiEGAABYiRADAACs5PMQc+TIET366KOKi4tTaGio2rVrp8cee0xVVVWeGmOM0tLSFBMTo9DQUCUnJ+ubb77x6qe8vFxjxoxR8+bNFR4ergEDBmjPnj2+Hi4AALCUz0PMU089pRdffFEzZ87Ut99+q8zMTE2fPl0vvPCCpyYzM1NZWVmaOXOm8vLy5HK51KtXL+3fv99Tk5qaqpycHC1ZskTr16/XgQMH1K9fP1VWVvp6yAAAwEKBvu5ww4YNGjhwoPr27StJio2N1eLFi/XFF19I+nMXZsaMGZo8ebJuvvlmSdL8+fMVHR2tRYsWaeTIkXK73Zo7d64WLFignj17SpIWLlyo1q1ba9WqVerTp4+vhw0AACzj852Yv/zlL1q9erW2b98uSfrqq6+0fv163XDDDZKkHTt2qKioSL179/YcExISoqSkJOXm5kqS8vPzdfjwYa+amJgYJSQkeGqOVV5ertLSUq8HAAA4d/l8J+bhhx+W2+1W+/btFRAQoMrKSj3xxBP6z//8T0lSUVGRJCk6OtrruOjoaO3atctTExwcrKZNm1arOXr8sTIyMjRt2jRfnw4AAKinfL4T88Ybb2jhwoVatGiRvvzyS82fP19PP/205s+f71XncDi8nhtjqrUdq7aaSZMmye12ex4FBQVndiIAAKBe8/lOzEMPPaSJEyfqtttukyR17NhRu3btUkZGhoYNGyaXyyXpz92Wli1beo4rLi727M64XC5VVFSopKTEazemuLhY3bt3P+77hoSEKCQkxNenAwAA6imf78QcOnRIjRp5dxsQEOC5xTouLk4ul0srV670vF5RUaG1a9d6AkqXLl0UFBTkVVNYWKgtW7bUGGIAAEDD4vOdmP79++uJJ55QmzZtdMkll2jTpk3KysrSXXfdJenPj5FSU1OVnp6u+Ph4xcfHKz09XWFhYRoyZIgkyel0asSIERo3bpyaNWumqKgojR8/Xh07dvTcrQQAABo2n4eYF154Qf/85z81atQoFRcXKyYmRiNHjtSUKVM8NRMmTFBZWZlGjRqlkpISJSYmasWKFYqIiPDUPPvsswoMDNSgQYNUVlamlJQUZWdnKyAgwNdDBgAAFnIYY0xdD8IfSktL5XQ65Xa7FRkZWdfDAQC/ip24zC/97nyyr1/6BWpyKn+/fb4TAwA4d/grHEkEJJw5fgASAABYiRADAACsRIgBAABWIsQAAAArEWIAAICVCDEAAMBKhBgAAGAlQgwAALASIQYAAFiJEAMAAKxEiAEAAFYixAAAACsRYgAAgJUIMQAAwEqEGAAAYCVCDAAAsBIhBgAAWIkQAwAArESIAQAAViLEAAAAKxFiAACAlQgxAADASoQYAABgJUIMAACwEiEGAABYiRADAACsRIgBAABWIsQAAAArEWIAAICVCDEAAMBKhBgAAGAlQgwAALASIQYAAFiJEAMAAKxEiAEAAFYixAAAACsRYgAAgJUIMQAAwEqEGAAAYCVCDAAAsBIhBgAAWIkQAwAArESIAQAAViLEAAAAKxFiAACAlQgxAADASoQYAABgJUIMAACwEiEGAABYiRADAACsRIgBAABWIsQAAAArEWIAAICVCDEAAMBKhBgAAGAlQgwAALASIQYAAFiJEAMAAKxEiAEAAFYixAAAACv5JcT89NNPuuOOO9SsWTOFhYXpsssuU35+vud1Y4zS0tIUExOj0NBQJScn65tvvvHqo7y8XGPGjFHz5s0VHh6uAQMGaM+ePf4YLgAAsJDPQ0xJSYl69OihoKAgffjhh9q6daueeeYZNWnSxFOTmZmprKwszZw5U3l5eXK5XOrVq5f279/vqUlNTVVOTo6WLFmi9evX68CBA+rXr58qKyt9PWQAAGAhhzHG+LLDiRMn6tNPP9W6deuO+7oxRjExMUpNTdXDDz8s6c9dl+joaD311FMaOXKk3G63zj//fC1YsECDBw+WJP38889q3bq1PvjgA/Xp0+eE4ygtLZXT6ZTb7VZkZKTvThAA6qHYicvqeginbOeTfet6CKiHTuXvt893YpYuXaquXbvq1ltvVYsWLXT55Zfr5Zdf9ry+Y8cOFRUVqXfv3p62kJAQJSUlKTc3V5KUn5+vw4cPe9XExMQoISHBU3Os8vJylZaWej0AAMC5y+ch5scff9ScOXMUHx+vjz76SPfcc4/Gjh2r1157TZJUVFQkSYqOjvY6Ljo62vNaUVGRgoOD1bRp0xprjpWRkSGn0+l5tG7d2tenBgAA6hGfh5iqqip17txZ6enpuvzyyzVy5Ej9/e9/15w5c7zqHA6H13NjTLW2Y9VWM2nSJLndbs+joKDgzE4EAADUaz4PMS1btlSHDh282i6++GLt3r1bkuRyuSSp2o5KcXGxZ3fG5XKpoqJCJSUlNdYcKyQkRJGRkV4PAABw7vJ5iOnRo4e2bdvm1bZ9+3a1bdtWkhQXFyeXy6WVK1d6Xq+oqNDatWvVvXt3SVKXLl0UFBTkVVNYWKgtW7Z4agAAQMMW6OsOH3jgAXXv3l3p6ekaNGiQPv/8c7300kt66aWXJP35MVJqaqrS09MVHx+v+Ph4paenKywsTEOGDJEkOZ1OjRgxQuPGjVOzZs0UFRWl8ePHq2PHjurZs6evhwwAACzk8xBzxRVXKCcnR5MmTdJjjz2muLg4zZgxQ7fffrunZsKECSorK9OoUaNUUlKixMRErVixQhEREZ6aZ599VoGBgRo0aJDKysqUkpKi7OxsBQQE+HrIAADAQj7/npj6gu+JAdCQ8D0xOFfU6ffEAAAAnA2EGAAAYCVCDAAAsBIhBgAAWIkQAwAArESIAQAAViLEAAAAKxFiAACAlQgxAADASoQYAABgJUIMAACwEiEGAABYiRADAACsRIgBAABWIsQAAAArEWIAAICVCDEAAMBKhBgAAGAlQgwAALASIQYAAFgpsK4HAABomGInLvNLvzuf7OuXflH/sBMDAACsRIgBAABWIsQAAAArEWIAAICVCDEAAMBKhBgAAGAlQgwAALASIQYAAFiJEAMAAKxEiAEAAFbiZwcA4Czx19fsAw0VOzEAAMBKhBgAAGAlQgwAALASIQYAAFiJEAMAAKxEiAEAAFYixAAAACsRYgAAgJUIMQAAwEqEGAAAYCVCDAAAsBIhBgAAWIkQAwAArESIAQAAViLEAAAAKxFiAACAlQgxAADASoQYAABgJUIMAACwEiEGAABYiRADAACsRIgBAABWIsQAAAArEWIAAICVCDEAAMBKhBgAAGAlQgwAALASIQYAAFiJEAMAAKxEiAEAAFbye4jJyMiQw+FQamqqp80Yo7S0NMXExCg0NFTJycn65ptvvI4rLy/XmDFj1Lx5c4WHh2vAgAHas2ePv4cLAAAs4dcQk5eXp5deekmXXnqpV3tmZqaysrI0c+ZM5eXlyeVyqVevXtq/f7+nJjU1VTk5OVqyZInWr1+vAwcOqF+/fqqsrPTnkAEAgCX8FmIOHDig22+/XS+//LKaNm3qaTfGaMaMGZo8ebJuvvlmJSQkaP78+Tp06JAWLVokSXK73Zo7d66eeeYZ9ezZU5dffrkWLlyof/3rX1q1apW/hgwAACzitxBz3333qW/fvurZs6dX+44dO1RUVKTevXt72kJCQpSUlKTc3FxJUn5+vg4fPuxVExMTo4SEBE/NscrLy1VaWur1AAAA565Af3S6ZMkSffnll8rLy6v2WlFRkSQpOjraqz06Olq7du3y1AQHB3vt4BytOXr8sTIyMjRt2jRfDB8AAFjA5zsxBQUFuv/++7Vw4UI1bty4xjqHw+H13BhTre1YtdVMmjRJbrfb8ygoKDj1wQMAAGv4PMTk5+eruLhYXbp0UWBgoAIDA7V27Vo9//zzCgwM9OzAHLujUlxc7HnN5XKpoqJCJSUlNdYcKyQkRJGRkV4PAABw7vJ5iElJSdG//vUvbd682fPo2rWrbr/9dm3evFnt2rWTy+XSypUrPcdUVFRo7dq16t69uySpS5cuCgoK8qopLCzUli1bPDUAAKBh8/k1MREREUpISPBqCw8PV7NmzTztqampSk9PV3x8vOLj45Wenq6wsDANGTJEkuR0OjVixAiNGzdOzZo1U1RUlMaPH6+OHTtWu1AYAAA0TH65sPdEJkyYoLKyMo0aNUolJSVKTEzUihUrFBER4al59tlnFRgYqEGDBqmsrEwpKSnKzs5WQEBAXQwZAADUMw5jjKnrQfhDaWmpnE6n3G4318cAqBdiJy6r6yE0CDuf7FvXQzgt/lofts3Hqfz95reTAACAlQgxAADASoQYAABgJUIMAACwEiEGAABYiRADAACsRIgBAABWIsQAAAArEWIAAICVCDEAAMBKhBgAAGAlQgwAALASIQYAAFiJEAMAAKxEiAEAAFYixAAAACsF1vUAAACA/8ROXOa3vnc+2ddvfZ8MQgwAHMOf/9IH4Dt8nAQAAKxEiAEAAFYixAAAACsRYgAAgJUIMQAAwEqEGAAAYCVCDAAAsBIhBgAAWIkQAwAArESIAQAAViLEAAAAKxFiAACAlQgxAADASvyKNQAr8UvTANiJAQAAViLEAAAAKxFiAACAlQgxAADASoQYAABgJUIMAACwEiEGAABYiRADAACsRIgBAABWIsQAAAArEWIAAICVCDEAAMBKhBgAAGAlQgwAALASIQYAAFiJEAMAAKxEiAEAAFYixAAAACsRYgAAgJUIMQAAwEqEGAAAYCVCDAAAsBIhBgAAWIkQAwAArESIAQAAViLEAAAAKxFiAACAlQgxAADASoQYAABgJZ+HmIyMDF1xxRWKiIhQixYtdOONN2rbtm1eNcYYpaWlKSYmRqGhoUpOTtY333zjVVNeXq4xY8aoefPmCg8P14ABA7Rnzx5fDxcAAFjK5yFm7dq1uu+++7Rx40atXLlSR44cUe/evXXw4EFPTWZmprKysjRz5kzl5eXJ5XKpV69e2r9/v6cmNTVVOTk5WrJkidavX68DBw6oX79+qqys9PWQAQCAhQJ93eHy5cu9ns+bN08tWrRQfn6+rr76ahljNGPGDE2ePFk333yzJGn+/PmKjo7WokWLNHLkSLndbs2dO1cLFixQz549JUkLFy5U69attWrVKvXp08fXwwYAAJbxeYg5ltvtliRFRUVJknbs2KGioiL17t3bUxMSEqKkpCTl5uZq5MiRys/P1+HDh71qYmJilJCQoNzc3OOGmPLycpWXl3uel5aW+uuUAAD1WOzEZX7re+eTff3WN06dXy/sNcbowQcf1F/+8hclJCRIkoqKiiRJ0dHRXrXR0dGe14qKihQcHKymTZvWWHOsjIwMOZ1Oz6N169a+Ph0AAFCP+DXEjB49Wl9//bUWL15c7TWHw+H13BhTre1YtdVMmjRJbrfb8ygoKDj9gQMAgHrPbyFmzJgxWrp0qT7++GO1atXK0+5yuSSp2o5KcXGxZ3fG5XKpoqJCJSUlNdYcKyQkRJGRkV4PAABw7vJ5iDHGaPTo0Xr33Xe1Zs0axcXFeb0eFxcnl8ullStXetoqKiq0du1ade/eXZLUpUsXBQUFedUUFhZqy5YtnhoAANCw+fzC3vvuu0+LFi3S+++/r4iICM+Oi9PpVGhoqBwOh1JTU5Wenq74+HjFx8crPT1dYWFhGjJkiKd2xIgRGjdunJo1a6aoqCiNHz9eHTt29NytBAAAGjafh5g5c+ZIkpKTk73a582bp+HDh0uSJkyYoLKyMo0aNUolJSVKTEzUihUrFBER4al/9tlnFRgYqEGDBqmsrEwpKSnKzs5WQECAr4cMAAAs5DDGmLoehD+UlpbK6XTK7XZzfQxwDvLnbbRATfx5i7WNa9of83Eqf7/57SQAAGAlQgwAALASIQYAAFiJEAMAAKxEiAEAAFYixAAAACsRYgAAgJUIMQAAwEqEGAAAYCVCDAAAsBIhBgAAWIkQAwAArESIAQAAViLEAAAAKxFiAACAlQgxAADASoQYAABgJUIMAACwEiEGAABYKbCuBwAAgC1iJy6r6yHg37ATAwAArESIAQAAViLEAAAAK3FNDAC/4hoCAP7CTgwAALASIQYAAFiJEAMAAKxEiAEAAFYixAAAACsRYgAAgJUIMQAAwEqEGAAAYCVCDAAAsBIhBgAAWIkQAwAArESIAQAAViLEAAAAKxFiAACAlQgxAADASoQYAABgJUIMAACwEiEGAABYiRADAACsRIgBAABWIsQAAAArEWIAAICVCDEAAMBKhBgAAGAlQgwAALBSYF0PAEDdi524rK6HAACnjJ0YAABgJUIMAACwEiEGAABYiRADAACsRIgBAABWIsQAAAArEWIAAICVCDEAAMBKhBgAAGAlQgwAALASIQYAAFip3oeY2bNnKy4uTo0bN1aXLl20bt26uh4SAACoB+p1iHnjjTeUmpqqyZMna9OmTfrrX/+q66+/Xrt3767roQEAgDrmMMaYuh5ETRITE9W5c2fNmTPH03bxxRfrxhtvVEZGRq3HlpaWyul0yu12KzIy0t9DBc4Kfm0aQH2y88m+Pu/zVP5+B/r83X2koqJC+fn5mjhxold77969lZubW62+vLxc5eXlnudut1vSn5MBnE0JUz+q6yEAwFnhj7+xR/s8mT2Wehtifv31V1VWVio6OtqrPTo6WkVFRdXqMzIyNG3atGrtrVu39tsYAQBoyJwz/Nf3/v375XQ6a62ptyHmKIfD4fXcGFOtTZImTZqkBx980PO8qqpKv//+u5o1a3bc+oaqtLRUrVu3VkFBAR+zHQfzUzPmpnbMT82Ym9oxP96MMdq/f79iYmJOWFtvQ0zz5s0VEBBQbdeluLi42u6MJIWEhCgkJMSrrUmTJv4cotUiIyP5P0stmJ+aMTe1Y35qxtzUjvn5PyfagTmq3t6dFBwcrC5dumjlypVe7StXrlT37t3raFQAAKC+qLc7MZL04IMPaujQoeratau6deuml156Sbt379Y999xT10MDAAB1rF6HmMGDB+u3337TY489psLCQiUkJOiDDz5Q27Zt63po1goJCdHUqVOrffSGPzE/NWNuasf81Iy5qR3zc/rq9ffEAAAA1KTeXhMDAABQG0IMAACwEiEGAABYiRADAACsRIgBAABWIsScA2bPnq24uDg1btxYXbp00bp162qsHT58uBwOR7XHJZdc4lX3zjvvqEOHDgoJCVGHDh2Uk5Pj79PwC1/PTXZ29nFr/vjjj7NxOj53KvMjSa+//ro6deqksLAwtWzZUn/729/022+/edU0xLUjnXhuGvramTVrli6++GKFhobqoosu0muvvVatpqGunRPNzbm2dnzKwGpLliwxQUFB5uWXXzZbt241999/vwkPDze7du06bv2+fftMYWGh51FQUGCioqLM1KlTPTW5ubkmICDApKenm2+//dakp6ebwMBAs3HjxrN0Vr7hj7mZN2+eiYyM9KorLCw8S2fkW6c6P+vWrTONGjUyzz33nPnxxx/NunXrzCWXXGJuvPFGT01DXTsnMzcNee3Mnj3bREREmCVLlpgffvjBLF682Jx33nlm6dKlnpqGunZOZm7OpbXja4QYy1155ZXmnnvu8Wpr3769mThx4kkdn5OTYxwOh9m5c6enbdCgQea6667zquvTp4+57bbbznzAZ5E/5mbevHnG6XT6cph15lTnZ/r06aZdu3Zebc8//7xp1aqV53lDXTsnMzcNee1069bNjB8/3qvt/vvvNz169PA8b6hr52Tm5lxaO77Gx0kWq6ioUH5+vnr37u3V3rt3b+Xm5p5UH3PnzlXPnj29vgV5w4YN1frs06fPSfdZH/hrbiTpwIEDatu2rVq1aqV+/fpp06ZNPhv32XI689O9e3ft2bNHH3zwgYwx+uWXX/T222+rb9++npqGunZOZm6khrt2ysvL1bhxY6+20NBQff755zp8+LCkhrt2TmZupHNj7fgDIcZiv/76qyorK6v9qnd0dHS1X/8+nsLCQn344Ye6++67vdqLiopOu8/6wl9z0759e2VnZ2vp0qVavHixGjdurB49euh///d/fTp+fzud+enevbtef/11DR48WMHBwXK5XGrSpIleeOEFT01DXTsnMzcNee306dNHr7zyivLz82WM0RdffKFXX31Vhw8f1q+//iqp4a6dk5mbc2Xt+AMh5hzgcDi8nhtjqrUdT3Z2tpo0aaIbb7zRZ33WN76em6uuukp33HGHOnXqpL/+9a968803deGFF3r9sbLJqczP1q1bNXbsWE2ZMkX5+flavny5duzYUe0HWRvi2jmZuWnIa+ef//ynrr/+el111VUKCgrSwIEDNXz4cElSQEDAafVZn/l6bs61teNLhBiLNW/eXAEBAdUSfnFxcbX/EjiWMUavvvqqhg4dquDgYK/XXC7XafVZn/hrbo7VqFEjXXHFFdb9F9HpzE9GRoZ69Oihhx56SJdeeqn69Omj2bNn69VXX1VhYaGkhrt2TmZujtWQ1k5oaKheffVVHTp0SDt37tTu3bsVGxuriIgINW/eXFLDXTsnMzfHsnXt+AMhxmLBwcHq0qWLVq5c6dW+cuVKde/evdZj165dq++//14jRoyo9lq3bt2q9blixYoT9lmf+GtujmWM0ebNm9WyZcszGu/Zdjrzc+jQITVq5P2vjKP/pWj+/+/INtS1czJzc6yGtHaOCgoKUqtWrRQQEKAlS5aoX79+nnlrqGvnqNrm5li2rh2/OLvXEcPXjt7ON3fuXLN161aTmppqwsPDPXfUTJw40QwdOrTacXfccYdJTEw8bp+ffvqpCQgIME8++aT59ttvzZNPPmn1rY6+nJu0tDSzfPly88MPP5hNmzaZv/3tbyYwMNB89tlnfj0XfzjV+Zk3b54JDAw0s2fPNj/88INZv3696dq1q7nyyis9NQ117ZzM3DTktbNt2zazYMECs337dvPZZ5+ZwYMHm6ioKLNjxw5PTUNdOyczN+fS2vE1Qsw5YNasWaZt27YmODjYdO7c2axdu9bz2rBhw0xSUpJX/b59+0xoaKh56aWXauzzrbfeMhdddJEJCgoy7du3N++8846/hu9Xvp6b1NRU06ZNGxMcHGzOP/9807t3b5Obm+vPU/CrU52f559/3nTo0MGEhoaali1bmttvv93s2bPHq6ahrp0TzU1DXjtbt241l112mQkNDTWRkZFm4MCB5rvvvqvWZ0NcOyczN+fa2vElhzE17HUCAADUY1wTAwAArESIAQAAViLEAAAAKxFiAACAlQgxAADASoQYAABgJUIMAACwEiEGAABYiRADAACsRIgBAABWIsQAAAAr/T/CL2m1u3od4wAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(self_tanis,bins=20);\n", "plt.title(\"self similarity, fusion 1-5\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Though that's interesting, and good to see that the self-similarities aren't all too far from 1.0, we're actually more interested in using the fingerprints for comparing different molecules to each other.\n", "\n", "Look at a bunch of random pairs of molecules that are at least 0.3 similar to each other (the [noise threshold](https://greglandrum.github.io/rdkit-blog/posts/2021-05-18-fingerprint-thresholds1.html) for count-based MFP2 is around 0.3), and then look at how different the similarities between their normal and fragment fingerprints are." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2024-09-20T05:20:40.232712Z", "start_time": "2024-09-20T05:20:39.588025Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "considered 15382 fingerprint pairs from 7845601\n" ] } ], "source": [ "import random\n", "random.seed(0xf00d)\n", "ps1 = list(range(len(allfps)))\n", "random.shuffle(ps1)\n", "ps1 = ps1[:500]\n", "ps2 = list(range(len(allfps)))\n", "random.shuffle(ps2)\n", "ps2 = ps2[:500]\n", "\n", "nCompared = 0\n", "other_tanidiff = []\n", "for i in ps1:\n", " fp1,frags1 = allfps[i]\n", " for j in ps2:\n", " fp2,frags2 = allfps[j]\n", " refTani = DataStructs.TanimotoSimilarity(fp1,fp2)\n", " if refTani < 0.3:\n", " continue\n", " nCompared += 1\n", " ltanis = []\n", " for f1 in frags1:\n", " ltanis.extend(DataStructs.BulkTanimotoSimilarity(f1,frags2))\n", " other_tanidiff.extend([refTani-x for x in ltanis])\n", "print(f\"considered {nCompared} fingerprint pairs from {len(allfps)**2}\")" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2024-09-20T05:22:06.692154Z", "start_time": "2024-09-20T05:22:06.476816Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "median = np.median(np.abs(other_tanidiff))\n", "bins = np.quantile(np.abs(other_tanidiff),[0.25,0.75, 0.9,0.95])\n", "iqr = bins[1]-bins[0]\n", "plt.hist(other_tanidiff,bins=20);\n", "gzero = np.sum(np.array(other_tanidiff)>-0.005) / len(other_tanidiff)\n", "plt.title(f'MAD={median:.2f}, IQR={iqr:.2f}, 90%={bins[2]:.2f}, 95%={bins[3]:.2f}, {gzero:.2f}>-.005');\n", "plt.xlabel('$\\\\Delta$Similarity');\n", "plt.ylabel('count');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The title of the plot shows statistics about the similarity differences: the median absolute deviation (MAD), the inter-quartile range (IQR), the 90% and 95% bins of the absolute deviations, and the fraction of the differences which are greater than -0.005 (I chose 0.005 as an conservative tolerance value for considering the similarities identical).\n", "\n", "That looks pretty good. For at least the `1-5` fragmentation, the similarity calculated with the fragment fingerprint is quite similar to that calculated with the full fingerprint. Even better: most of the deviation happens to the high-similarity side (87% exceed the -0.005 tolerance value), so we should be able to safely use the fragment-fingerprint similarity as a filter to identify interesting molecules. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Validation\n", "\n", "Repeat that analysis for each of the top 10 fragmentations:" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "ExecuteTime": { "end_time": "2024-09-20T03:30:09.764668Z", "start_time": "2024-09-20T03:28:44.748774Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Doing ('4', '5')\n", "considered 66995 fingerprint pairs from 18326961\n", "Doing ('1', '5')\n", "considered 63242 fingerprint pairs from 7845601\n", "Doing ('8', '16')\n", "considered 54070 fingerprint pairs from 6095961\n", "Doing ('3', '16')\n", "considered 95650 fingerprint pairs from 4260096\n", "Doing ('5', '16')\n", "considered 217114 fingerprint pairs from 2725801\n", "Doing ('3', '4')\n", "considered 112805 fingerprint pairs from 1537600\n", "Doing ('6', '16')\n", "considered 98275 fingerprint pairs from 790321\n", "Doing ('5', '15')\n", "considered 53490 fingerprint pairs from 640000\n", "Doing ('8', '14')\n", "considered 43291 fingerprint pairs from 502681\n", "Doing ('8', '15')\n", "considered 35148 fingerprint pairs from 352836\n" ] } ], "source": [ "import random\n", "random.seed(0xf00d)\n", "from collections import defaultdict\n", "results = {}\n", "for btk in bondsToKeep:\n", " print(f\"Doing {btk}\")\n", " fpgen = rdFingerprintGenerator.GetMorganGenerator(radius=2,fpSize=4096)\n", " dfps= []\n", " for m in ms:\n", " dfps.extend(getDifferenceFPs(m,[btk],fpgen))\n", "\n", " delta1 = dfps[0]\n", " for dfp in dfps[1:]:\n", " delta1 += dfp\n", " nfps = len(dfps)\n", " for k,v in delta1.GetNonzeroElements().items():\n", " delta1[k] = round(v/nfps)\n", "\n", " allfps = []\n", " for m in ms:\n", " frags = splitMol(m,[btk])\n", " if not frags:\n", " continue\n", " fp1 = fpgen.GetCountFingerprint(m)\n", " ffps = [getSumFP(x,fpgen,delta1) for x in frags]\n", " allfps.append((fp1,ffps))\n", "\n", " self_tanis = []\n", " for fp1,ffps in allfps:\n", " self_tanis.extend([DataStructs.TanimotoSimilarity(fp1,x) for x in ffps])\n", " \n", " ps1 = list(range(len(allfps)))\n", " random.shuffle(ps1)\n", " ps1 = ps1[:1000]\n", " ps2 = list(range(len(allfps)))\n", " random.shuffle(ps2)\n", " ps2 = ps2[:1000]\n", "\n", " nCompared = 0\n", " other_tanidiff1 = defaultdict(list)\n", " other_tanidiff2 = defaultdict(list)\n", " for i in ps1:\n", " fp1,frags1 = allfps[i]\n", " for j in ps2:\n", " fp2,frags2 = allfps[j]\n", " refTani = DataStructs.TanimotoSimilarity(fp1,fp2)\n", " if refTani < 0.3:\n", " continue\n", " nCompared += 1\n", " ltanis = []\n", " for f1 in frags1:\n", " ltanis.extend(DataStructs.BulkTanimotoSimilarity(f1,frags2))\n", " for thresh in (0.3,0.5,0.7):\n", " if refTani < thresh:\n", " continue\n", " other_tanidiff1[thresh].extend([refTani-x for x in ltanis])\n", " other_tanidiff2[thresh].extend([refTani-x for x in DataStructs.BulkTanimotoSimilarity(fp1,frags2)])\n", " other_tanidiff2[thresh].extend([refTani-x for x in DataStructs.BulkTanimotoSimilarity(fp2,frags1)])\n", "\n", " print(f\"considered {nCompared} fingerprint pairs from {len(allfps)**2}\")\n", " results[btk] = (self_tanis,other_tanidiff1,other_tanidiff2)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Start, again, by looking at self similarities." ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "ExecuteTime": { "end_time": "2024-09-20T03:30:32.384729Z", "start_time": "2024-09-20T03:30:31.317658Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12,18))\n", "for i,btk in enumerate(results.keys()):\n", " plt.subplot(5,2,i+1)\n", " self_tanis,other_tanidiff1,other_tanidiff2 = results[btk]\n", " plt.hist(self_tanis,bins=20);\n", " plt.title(f\"self similarity, {btk}\"); \n", " plt.xlim((0.6,1.0));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The differences here aren't huge, but we definitely aren't getting self-similarities of 1." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Still, we aren't really interested in self-similarity. How does the method do when we compute fragment-fragment similarities? This time we plot the difference between the similarities calculated with the full fingerprint and the fragment fingerprints." ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "ExecuteTime": { "end_time": "2024-09-20T03:30:39.576749Z", "start_time": "2024-09-20T03:30:35.989374Z" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12,18))\n", "for i,btk in enumerate(results.keys()):\n", " plt.subplot(5,2,i+1)\n", " self_tanis,other_tanidiff1,other_tanidiff2 = results[btk]\n", " other_tanidiff1 = other_tanidiff1[0.3]\n", " other_tanidiff2 = other_tanidiff2[0.3]\n", " plt.hist(other_tanidiff1,bins=20,label=f'fragfp-fragfp {btk}');\n", " plt.legend()\n", " median = np.median(np.abs(other_tanidiff1))\n", " std = np.std(np.abs(other_tanidiff1))\n", " bins = np.quantile(np.abs(other_tanidiff1),[0.25,0.75, 0.9,0.95])\n", " iqr = bins[1]-bins[0]\n", " gzero = np.sum(np.array(other_tanidiff1)>-0.005) / len(other_tanidiff1)\n", " plt.title(f'MAD={median:.2f}, IQR={iqr:.2f}, 90%={bins[2]:.2f}, 95%={bins[3]:.2f}, {gzero:.2f}>-.005');\n", " plt.xlim((-0.1,0.25));\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Those look good! In general the difference in similarities is within 0.05." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And, finally, since we could also query the collection of fragment molecules with a full fingerprint, look at the difference between the full FP similarity and the full FP - fragment FP similarity:" ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "ExecuteTime": { "end_time": "2024-09-20T03:30:51.488192Z", "start_time": "2024-09-20T03:30:47.368017Z" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12,18))\n", "for i,btk in enumerate(results.keys()):\n", " plt.subplot(5,2,i+1)\n", " self_tanis,other_tanidiff1,other_tanidiff2 = results[btk]\n", " other_tanidiff1 = other_tanidiff1[0.3]\n", " other_tanidiff2 = other_tanidiff2[0.3]\n", " plt.hist(other_tanidiff2,bins=20,label=f'fp-fragfp {btk}');\n", " plt.legend()\n", " median = np.median(np.abs(other_tanidiff2))\n", " std = np.std(np.abs(other_tanidiff2))\n", " bins = np.quantile(np.abs(other_tanidiff2),[0.25,0.75, 0.9,0.95])\n", " iqr = bins[1]-bins[0]\n", " gzero = np.sum(np.array(other_tanidiff2)>-0.005) / len(other_tanidiff2)\n", " plt.title(f'MAD={median:.2f}, IQR={iqr:.2f}, 90%={bins[2]:.2f}, 95%={bins[3]:.2f}, {gzero:.2f}>-.005');\n", "\n", " plt.xlim((-0.1,0.25));\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here the differences are larger than when we search with the fragment fps, but they still aren't huge. This form of the query also has the characteristic that the similarity calculated against the fragment fps is usually lower than that calculated against the full fp." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tolerances for searching\n", "\n", "Suppose we want to use the fragment fingerprints for similarity searching with a threshold and be sure that we don't miss any results. How would we need to adjust the threshold?" ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "ExecuteTime": { "end_time": "2024-09-20T03:32:37.594990Z", "start_time": "2024-09-20T03:32:37.324895Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Threshold: 0.3\n", " \t frag-frag\t fp-frag\n", "('4', '5'):\t-0.025 -0.015\t-0.006 0.000\n", "('1', '5'):\t-0.022 -0.010\t-0.004 0.000\n", "('8', '16'):\t-0.032 -0.021\t-0.012 -0.007\n", "('3', '16'):\t-0.026 -0.021\t-0.013 -0.010\n", "('5', '16'):\t-0.029 -0.019\t-0.016 -0.002\n", "('3', '4'):\t-0.032 -0.019\t-0.018 -0.009\n", "('6', '16'):\t-0.043 -0.029\t-0.025 -0.015\n", "('5', '15'):\t-0.048 -0.026\t-0.005 -0.002\n", "('8', '14'):\t-0.042 -0.016\t-0.010 -0.002\n", "('8', '15'):\t-0.053 -0.028\t-0.014 -0.003\n", "-----------\n", "Threshold: 0.5\n", " \t frag-frag\t fp-frag\n", "('4', '5'):\t-0.023 -0.008\t 0.000 0.004\n", "('1', '5'):\t-0.022 -0.004\t-0.003 0.000\n", "('8', '16'):\t-0.035 -0.019\t-0.012 -0.004\n", "('3', '16'):\t-0.030 -0.012\t-0.011 0.000\n", "('5', '16'):\t-0.028 -0.016\t 0.008 0.014\n", "('3', '4'):\t-0.028 -0.009\t-0.005 0.004\n", "('6', '16'):\t-0.036 -0.028\t-0.004 0.006\n", "('5', '15'):\t-0.060 -0.020\t-0.006 0.026\n", "('8', '14'):\t-0.050 -0.016\t-0.008 0.000\n", "('8', '15'):\t-0.056 -0.035\t 0.000 0.006\n", "-----------\n", "Threshold: 0.7\n", " \t frag-frag\t fp-frag\n", "('4', '5'):\t-0.016 -0.003\t 0.000 0.008\n", "('1', '5'):\t-0.023 -0.002\t 0.000 0.005\n", "('8', '16'):\t-0.035 -0.012\t-0.009 0.006\n", "('3', '16'):\t-0.034 -0.003\t-0.009 0.029\n", "('5', '16'):\t-0.029 -0.012\t 0.011 0.047\n", "('3', '4'):\t-0.022 -0.006\t-0.001 0.014\n", "('6', '16'):\t-0.034 -0.009\t 0.009 0.035\n", "('5', '15'):\t-0.026 -0.012\t 0.036 0.058\n", "('8', '14'):\t-0.061 -0.021\t 0.000 0.028\n", "('8', '15'):\t-0.061 -0.032\t-0.001 0.017\n", "-----------\n" ] } ], "source": [ "for thresh in (0.3,0.5,0.7):\n", " print(f'Threshold: {thresh}')\n", " print(' \\t frag-frag\\t fp-frag')\n", " for i,btk in enumerate(results.keys()):\n", " self_tanis,other_tanidiff1,other_tanidiff2 = results[btk]\n", " bins1 = np.quantile(other_tanidiff1[thresh],[0.01,0.05])\n", " bins2 = np.quantile(other_tanidiff2[thresh],[0.01,0.05])\n", " print(f'{btk}:\\t{bins1[0]: 6.3f} {bins1[1]: 6.3f}\\t{bins2[0]: 6.3f} {bins2[1]: 6.3f}')\n", " print('-----------')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to be sure of retrieving 99% of the actual results when searching using the full fingerprint of the query molecule against the fragment fingerprint of the database molecules (these are the fp-frag results), we'd need to use a search threshold of 0.275 (true threshold = 0.3), 0.488 (true threshold = 0.5), or 0.691 (true threshold = 0.7). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# What about if we don't use the constant offset?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I went into this convinced that the constant offset was important. What if we just use the simple additive fingerprints without including that offset?" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "ExecuteTime": { "end_time": "2024-09-19T17:07:43.102411Z", "start_time": "2024-09-19T17:06:17.200690Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Doing ('4', '5')\n", "considered 66995 fingerprint pairs from 18326961\n", "Doing ('1', '5')\n", "considered 63242 fingerprint pairs from 7845601\n", "Doing ('8', '16')\n", "considered 54070 fingerprint pairs from 6095961\n", "Doing ('3', '16')\n", "considered 95650 fingerprint pairs from 4260096\n", "Doing ('5', '16')\n", "considered 217114 fingerprint pairs from 2725801\n", "Doing ('3', '4')\n", "considered 112805 fingerprint pairs from 1537600\n", "Doing ('6', '16')\n", "considered 98275 fingerprint pairs from 790321\n", "Doing ('5', '15')\n", "considered 53490 fingerprint pairs from 640000\n", "Doing ('8', '14')\n", "considered 43291 fingerprint pairs from 502681\n", "Doing ('8', '15')\n", "considered 35148 fingerprint pairs from 352836\n" ] } ], "source": [ "import random\n", "random.seed(0xf00d)\n", "noconstant_results = {}\n", "for btk in bondsToKeep:\n", " print(f\"Doing {btk}\")\n", " fpgen = rdFingerprintGenerator.GetMorganGenerator(radius=2,fpSize=4096)\n", " dfps= []\n", " for m in ms:\n", " dfps.extend(getDifferenceFPs(m,[btk],fpgen))\n", "\n", " allfps = []\n", " for m in ms:\n", " frags = splitMol(m,[btk])\n", " if not frags:\n", " continue\n", " fp1 = fpgen.GetCountFingerprint(m)\n", " ffps = [fpgen.GetCountFingerprint(x) for x in frags]\n", " allfps.append((fp1,ffps))\n", "\n", " self_tanis = []\n", " for fp1,ffps in allfps:\n", " self_tanis.extend([DataStructs.TanimotoSimilarity(fp1,x) for x in ffps])\n", " \n", " ps1 = list(range(len(allfps)))\n", " random.shuffle(ps1)\n", " ps1 = ps1[:1000]\n", " ps2 = list(range(len(allfps)))\n", " random.shuffle(ps2)\n", " ps2 = ps2[:1000]\n", "\n", " nCompared = 0\n", " other_tanidiff1 = []\n", " other_tanidiff2 = []\n", " for i in ps1:\n", " fp1,frags1 = allfps[i]\n", " for j in ps2:\n", " fp2,frags2 = allfps[j]\n", " refTani = DataStructs.TanimotoSimilarity(fp1,fp2)\n", " if refTani < 0.3:\n", " continue\n", " nCompared += 1\n", " ltanis = []\n", " for f1 in frags1:\n", " ltanis.extend(DataStructs.BulkTanimotoSimilarity(f1,frags2))\n", " other_tanidiff1.extend([refTani-x for x in ltanis])\n", " other_tanidiff2.extend([refTani-x for x in DataStructs.BulkTanimotoSimilarity(fp1,frags2)])\n", " other_tanidiff2.extend([refTani-x for x in DataStructs.BulkTanimotoSimilarity(fp2,frags1)])\n", "\n", " print(f\"considered {nCompared} fingerprint pairs from {len(allfps)**2}\")\n", " noconstant_results[btk] = (self_tanis,other_tanidiff1,other_tanidiff2)\n" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "ExecuteTime": { "end_time": "2024-09-19T17:22:48.750129Z", "start_time": "2024-09-19T17:22:47.112204Z" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(16,18))\n", "for i,btk in enumerate(noconstant_results.keys()):\n", " subplot(5,2,i+1)\n", " self_tanis,other_tanidiff1,other_tanidiff2 = results[btk]\n", " self_tanis_2,other_tanidiff1_2,other_tanidiff2_2 = noconstant_results[btk]\n", " plt.hist([self_tanis,self_tanis_2],bins=20, label=['with constant','no constant']);\n", " plt.title(f\"self similarity, {btk}\");\n", " plt.legend()\n", " plt.xlim((0.6,1.0))\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here the red histograms are clearly shifted to the left; the constant is helping." ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "ExecuteTime": { "end_time": "2024-09-19T17:22:57.089543Z", "start_time": "2024-09-19T17:22:55.280399Z" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(16,18))\n", "for i,btk in enumerate(noconstant_results.keys()):\n", " plt.subplot(5,2,i+1)\n", " self_tanis,other_tanidiff1,other_tanidiff2 = results[btk]\n", " self_tanis_2,other_tanidiff1_2,other_tanidiff2_2 = noconstant_results[btk]\n", " plt.hist([other_tanidiff1,other_tanidiff1_2],bins=20, label=['with constant','no constant']);\n", " plt.legend();\n", " plt.title(f\"fragfp-fragfp comparison, {btk}\");\n", " #yscale('log')\n", " plt.xlim((-0.1,0.25));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here the differences aren't as pronounced. It looks like the constant term has the consequence of making the similarity a bit lower(the red histograms are shifted a bit to the left). The difference is, in any case, not large" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "ExecuteTime": { "end_time": "2024-09-19T17:23:03.162915Z", "start_time": "2024-09-19T17:23:01.313284Z" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(16,18))\n", "for i,btk in enumerate(noconstant_results.keys()):\n", " plt.subplot(5,2,i+1)\n", " self_tanis,other_tanidiff1,other_tanidiff2 = results[btk]\n", " self_tanis_2,other_tanidiff1_2,other_tanidiff2_2 = noconstant_results[btk]\n", " plt.hist([other_tanidiff2,other_tanidiff2_2],bins=20, label=['with constant','no constant']);\n", " plt.legend();\n", " plt.title(f\"fp-fragfp comparison, {btk}\");\n", " #yscale('log')\n", " plt.xlim((-0.1,0.3));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we again have a clear signal: leaving the constant term out causes a larger similarity difference (the red histograms are shifted to the right)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I'm going to wrap this one up here. I think the approach clearly works, but there's some more to do in a future blog post or two:\n", " - look at how well the constants generated on this set work on a completely different set of molecules.\n", " - look at fragmenting the molecule multiple times (i.e. combining more than 2 fragments to form a molecule)\n", " - look at an asymmetric similarity measure like Tversky. This is what we'd want to use if we were searching a large fragment space and incrementally building up the results molecules (like in the [FTrees-FS paper](https://dx.doi.org/10.1023/A:1011144622059))." ] }, { "cell_type": "code", "execution_count": null, "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": "3.12.1" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }