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author | bgruening |
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date | Thu, 15 Aug 2013 03:34:00 -0400 |
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#!/usr/bin/env python __all__ = ['weights_max', 'weights_mean', 'weights_none', 'default'] # Silicos-it from errors import WrongArgument # RDKit from rdkit.Chem import Descriptors from rdkit import Chem # General from copy import deepcopy from math import exp, log import sys, os, re import argparse def check_filetype(filepath): mol = False possible_inchi = True for line_counter, line in enumerate(open(filepath)): if line_counter > 10000: break if line.find('$$$$') != -1: return 'sdf' elif line.find('@<TRIPOS>MOLECULE') != -1: return 'mol2' elif line.find('ligand id') != -1: return 'drf' elif possible_inchi and re.findall('^InChI=', line): return 'inchi' elif re.findall('^M\s+END', line): mol = True # first line is not an InChI, so it can't be an InChI file possible_inchi = False if mol: # END can occures before $$$$, so and SDF file will # be recognised as mol, if you not using this hack' return 'mol' return 'smi' AliphaticRings = Chem.MolFromSmarts('[$([A;R][!a])]') AcceptorSmarts = [ '[oH0;X2]', '[OH1;X2;v2]', '[OH0;X2;v2]', '[OH0;X1;v2]', '[O-;X1]', '[SH0;X2;v2]', '[SH0;X1;v2]', '[S-;X1]', '[nH0;X2]', '[NH0;X1;v3]', '[$([N;+0;X3;v3]);!$(N[C,S]=O)]' ] Acceptors = [] for hba in AcceptorSmarts: Acceptors.append(Chem.MolFromSmarts(hba)) StructuralAlertSmarts = [ '*1[O,S,N]*1', '[S,C](=[O,S])[F,Br,Cl,I]', '[CX4][Cl,Br,I]', '[C,c]S(=O)(=O)O[C,c]', '[$([CH]),$(CC)]#CC(=O)[C,c]', '[$([CH]),$(CC)]#CC(=O)O[C,c]', 'n[OH]', '[$([CH]),$(CC)]#CS(=O)(=O)[C,c]', 'C=C(C=O)C=O', 'n1c([F,Cl,Br,I])cccc1', '[CH1](=O)', '[O,o][O,o]', '[C;!R]=[N;!R]', '[N!R]=[N!R]', '[#6](=O)[#6](=O)', '[S,s][S,s]', '[N,n][NH2]', 'C(=O)N[NH2]', '[C,c]=S', '[$([CH2]),$([CH][CX4]),$(C([CX4])[CX4])]=[$([CH2]),$([CH][CX4]),$(C([CX4])[CX4])]', 'C1(=[O,N])C=CC(=[O,N])C=C1', 'C1(=[O,N])C(=[O,N])C=CC=C1', 'a21aa3a(aa1aaaa2)aaaa3', 'a31a(a2a(aa1)aaaa2)aaaa3', 'a1aa2a3a(a1)A=AA=A3=AA=A2', 'c1cc([NH2])ccc1', '[Hg,Fe,As,Sb,Zn,Se,se,Te,B,Si,Na,Ca,Ge,Ag,Mg,K,Ba,Sr,Be,Ti,Mo,Mn,Ru,Pd,Ni,Cu,Au,Cd,Al,Ga,Sn,Rh,Tl,Bi,Nb,Li,Pb,Hf,Ho]', 'I', 'OS(=O)(=O)[O-]', '[N+](=O)[O-]', 'C(=O)N[OH]', 'C1NC(=O)NC(=O)1', '[SH]', '[S-]', 'c1ccc([Cl,Br,I,F])c([Cl,Br,I,F])c1[Cl,Br,I,F]', 'c1cc([Cl,Br,I,F])cc([Cl,Br,I,F])c1[Cl,Br,I,F]', '[CR1]1[CR1][CR1][CR1][CR1][CR1][CR1]1', '[CR1]1[CR1][CR1]cc[CR1][CR1]1', '[CR2]1[CR2][CR2][CR2][CR2][CR2][CR2][CR2]1', '[CR2]1[CR2][CR2]cc[CR2][CR2][CR2]1', '[CH2R2]1N[CH2R2][CH2R2][CH2R2][CH2R2][CH2R2]1', '[CH2R2]1N[CH2R2][CH2R2][CH2R2][CH2R2][CH2R2][CH2R2]1', 'C#C', '[OR2,NR2]@[CR2]@[CR2]@[OR2,NR2]@[CR2]@[CR2]@[OR2,NR2]', '[$([N+R]),$([n+R]),$([N+]=C)][O-]', '[C,c]=N[OH]', '[C,c]=NOC=O', '[C,c](=O)[CX4,CR0X3,O][C,c](=O)', 'c1ccc2c(c1)ccc(=O)o2', '[O+,o+,S+,s+]', 'N=C=O', '[NX3,NX4][F,Cl,Br,I]', 'c1ccccc1OC(=O)[#6]', '[CR0]=[CR0][CR0]=[CR0]', '[C+,c+,C-,c-]', 'N=[N+]=[N-]', 'C12C(NC(N1)=O)CSC2', 'c1c([OH])c([OH,NH2,NH])ccc1', 'P', '[N,O,S]C#N', 'C=C=O', '[Si][F,Cl,Br,I]', '[SX2]O', '[SiR0,CR0](c1ccccc1)(c2ccccc2)(c3ccccc3)', 'O1CCCCC1OC2CCC3CCCCC3C2', 'N=[CR0][N,n,O,S]', '[cR2]1[cR2][cR2]([Nv3X3,Nv4X4])[cR2][cR2][cR2]1[cR2]2[cR2][cR2][cR2]([Nv3X3,Nv4X4])[cR2][cR2]2', 'C=[C!r]C#N', '[cR2]1[cR2]c([N+0X3R0,nX3R0])c([N+0X3R0,nX3R0])[cR2][cR2]1', '[cR2]1[cR2]c([N+0X3R0,nX3R0])[cR2]c([N+0X3R0,nX3R0])[cR2]1', '[cR2]1[cR2]c([N+0X3R0,nX3R0])[cR2][cR2]c1([N+0X3R0,nX3R0])', '[OH]c1ccc([OH,NH2,NH])cc1', 'c1ccccc1OC(=O)O', '[SX2H0][N]', 'c12ccccc1(SC(S)=N2)', 'c12ccccc1(SC(=S)N2)', 'c1nnnn1C=O', 's1c(S)nnc1NC=O', 'S1C=CSC1=S', 'C(=O)Onnn', 'OS(=O)(=O)C(F)(F)F', 'N#CC[OH]', 'N#CC(=O)', 'S(=O)(=O)C#N', 'N[CH2]C#N', 'C1(=O)NCC1', 'S(=O)(=O)[O-,OH]', 'NC[F,Cl,Br,I]', 'C=[C!r]O', '[NX2+0]=[O+0]', '[OR0,NR0][OR0,NR0]', 'C(=O)O[C,H1].C(=O)O[C,H1].C(=O)O[C,H1]', '[CX2R0][NX3R0]', 'c1ccccc1[C;!R]=[C;!R]c2ccccc2', '[NX3R0,NX4R0,OR0,SX2R0][CX4][NX3R0,NX4R0,OR0,SX2R0]', '[s,S,c,C,n,N,o,O]~[n+,N+](~[s,S,c,C,n,N,o,O])(~[s,S,c,C,n,N,o,O])~[s,S,c,C,n,N,o,O]', '[s,S,c,C,n,N,o,O]~[nX3+,NX3+](~[s,S,c,C,n,N])~[s,S,c,C,n,N]', '[*]=[N+]=[*]', '[SX3](=O)[O-,OH]', 'N#N', 'F.F.F.F', '[R0;D2][R0;D2][R0;D2][R0;D2]', '[cR,CR]~C(=O)NC(=O)~[cR,CR]', 'C=!@CC=[O,S]', '[#6,#8,#16][C,c](=O)O[C,c]', 'c[C;R0](=[O,S])[C,c]', 'c[SX2][C;!R]', 'C=C=C', 'c1nc([F,Cl,Br,I,S])ncc1', 'c1ncnc([F,Cl,Br,I,S])c1', 'c1nc(c2c(n1)nc(n2)[F,Cl,Br,I])', '[C,c]S(=O)(=O)c1ccc(cc1)F', '[15N]', '[13C]', '[18O]', '[34S]' ] StructuralAlerts = [] for smarts in StructuralAlertSmarts: StructuralAlerts.append(Chem.MolFromSmarts(smarts)) # ADS parameters for the 8 molecular properties: [row][column] # rows[8]: MW, ALOGP, HBA, HBD, PSA, ROTB, AROM, ALERTS # columns[7]: A, B, C, D, E, F, DMAX # ALOGP parameters from Gregory Gerebtzoff (2012, Roche) pads1 = [ [2.817065973, 392.5754953, 290.7489764, 2.419764353, 49.22325677, 65.37051707, 104.9805561], [0.486849448, 186.2293718, 2.066177165, 3.902720615, 1.027025453, 0.913012565, 145.4314800], [2.948620388, 160.4605972, 3.615294657, 4.435986202, 0.290141953, 1.300669958, 148.7763046], [1.618662227, 1010.051101, 0.985094388, 0.000000001, 0.713820843, 0.920922555, 258.1632616], [1.876861559, 125.2232657, 62.90773554, 87.83366614, 12.01999824, 28.51324732, 104.5686167], [0.010000000, 272.4121427, 2.558379970, 1.565547684, 1.271567166, 2.758063707, 105.4420403], [3.217788970, 957.7374108, 2.274627939, 0.000000001, 1.317690384, 0.375760881, 312.3372610], [0.010000000, 1199.094025, -0.09002883, 0.000000001, 0.185904477, 0.875193782, 417.7253140] ] # ALOGP parameters from the original publication pads2 = [ [2.817065973, 392.5754953, 290.7489764, 2.419764353, 49.22325677, 65.37051707, 104.9805561], [3.172690585, 137.8624751, 2.534937431, 4.581497897, 0.822739154, 0.576295591, 131.3186604], [2.948620388, 160.4605972, 3.615294657, 4.435986202, 0.290141953, 1.300669958, 148.7763046], [1.618662227, 1010.051101, 0.985094388, 0.000000001, 0.713820843, 0.920922555, 258.1632616], [1.876861559, 125.2232657, 62.90773554, 87.83366614, 12.01999824, 28.51324732, 104.5686167], [0.010000000, 272.4121427, 2.558379970, 1.565547684, 1.271567166, 2.758063707, 105.4420403], [3.217788970, 957.7374108, 2.274627939, 0.000000001, 1.317690384, 0.375760881, 312.3372610], [0.010000000, 1199.094025, -0.09002883, 0.000000001, 0.185904477, 0.875193782, 417.7253140] ] def ads(x, a, b, c, d, e, f, dmax): return ((a+(b/(1+exp(-1*(x-c+d/2)/e))*(1-1/(1+exp(-1*(x-c-d/2)/f))))) / dmax) def properties(mol): """ Calculates the properties that are required to calculate the QED descriptor. """ matches = [] if (mol is None): raise WrongArgument("properties(mol)", "mol argument is \'None\'") x = [0] * 9 x[0] = Descriptors.MolWt(mol) # MW x[1] = Descriptors.MolLogP(mol) # ALOGP for hba in Acceptors: # HBA if (mol.HasSubstructMatch(hba)): matches = mol.GetSubstructMatches(hba) x[2] += len(matches) x[3] = Descriptors.NumHDonors(mol) # HBD x[4] = Descriptors.TPSA(mol) # PSA x[5] = Descriptors.NumRotatableBonds(mol) # ROTB x[6] = Chem.GetSSSR(Chem.DeleteSubstructs(deepcopy(mol), AliphaticRings)) # AROM for alert in StructuralAlerts: # ALERTS if (mol.HasSubstructMatch(alert)): x[7] += 1 ro5_failed = 0 if x[3] > 5: ro5_failed += 1 #HBD if x[2] > 10: ro5_failed += 1 #HBA if x[0] >= 500: ro5_failed += 1 if x[1] > 5: ro5_failed += 1 x[8] = ro5_failed return x def qed(w, p, gerebtzoff): d = [0.00] * 8 if gerebtzoff: for i in range(0, 8): d[i] = ads(p[i], pads1[i][0], pads1[i][1], pads1[i][2], pads1[i][3], pads1[i][4], pads1[i][5], pads1[i][6]) else: for i in range(0, 8): d[i] = ads(p[i], pads2[i][0], pads2[i][1], pads2[i][2], pads2[i][3], pads2[i][4], pads2[i][5], pads2[i][6]) t = 0.0 for i in range(0, 8): t += w[i] * log(d[i]) return (exp(t / sum(w))) def weights_max(mol, gerebtzoff = True, props = False): """ Calculates the QED descriptor using maximal descriptor weights. If props is specified we skip the calculation step and use the props-list of properties. """ if not props: props = properties(mol) return qed([0.50, 0.25, 0.00, 0.50, 0.00, 0.50, 0.25, 1.00], props, gerebtzoff) def weights_mean(mol, gerebtzoff = True, props = False): """ Calculates the QED descriptor using average descriptor weights. If props is specified we skip the calculation step and use the props-list of properties. """ if not props: props = properties(mol) return qed([0.66, 0.46, 0.05, 0.61, 0.06, 0.65, 0.48, 0.95], props, gerebtzoff) def weights_none(mol, gerebtzoff = True, props = False): """ Calculates the QED descriptor using unit weights. If props is specified we skip the calculation step and use the props-list of properties. """ if not props: props = properties(mol) return qed([1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00], props, gerebtzoff) def default(mol, gerebtzoff = True): """ Calculates the QED descriptor using average descriptor weights and Gregory Gerebtzoff parameters. """ return weights_mean(mol, gerebtzoff) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument('-i', '--input', required=True, help='path to the input file name') parser.add_argument("-m", "--method", dest="method", choices=['max', 'mean', 'unweighted'], default="mean", help="Specify the method you want to use.") parser.add_argument("--iformat", help="Input format. It must be supported by openbabel.") parser.add_argument('-o', '--outfile', type=argparse.FileType('w+'), default=sys.stdout, help="path to the result file, default it sdtout") parser.add_argument("--header", dest="header", action="store_true", default=False, help="Write header line.") args = parser.parse_args() # Elucidate filetype and open supplier ifile = os.path.abspath(args.input) if not os.path.isfile(ifile): print "Error: ", ifile, " is not a file or cannot be found." sys.exit(1) if not os.path.exists(ifile): print "Error: ", ifile, " does not exist or cannot be found." sys.exit(1) if not os.access(ifile, os.R_OK): print "Error: ", ifile, " is not readable." sys.exit(1) if not args.iformat: # try to guess the filetype filetype = check_filetype( ifile ) else: filetype = args.iformat # sdf or smi """ We want to store the original SMILES in the output. So in case of a SMILES file iterate over the file and convert each line separate. """ if filetype == 'sdf': supplier = Chem.SDMolSupplier( ifile ) # Process file if args.header: args.outfile.write("MW\tALOGP\tHBA\tHBD\tPSA\tROTB\tAROM\tALERTS\tLRo5\tQED\tNAME\n") count = 0 for mol in supplier: count += 1 if mol is None: print "Warning: skipping molecule ", count, " and continuing with next." continue props = properties(mol) if args.method == 'max': calc_qed = weights_max(mol, True, props) elif args.method == 'unweighted': calc_qed = weights_none(mol, True, props) else: calc_qed = weights_mean(mol, True, props) args.outfile.write( "%.2f\t%.3f\t%d\t%d\t%.2f\t%d\t%d\t%d\t%s\t%.3f\t%-s\n" % ( props[0], props[1], props[2], props[3], props[4], props[5], props[6], props[7], props[8], calc_qed, mol.GetProp("_Name"), )) elif filetype == 'smi': supplier = Chem.SmilesMolSupplier( ifile, " \t", 0, 1, False, True ) # Process file if args.header: args.outfile.write("MW\tALOGP\tHBA\tHBD\tPSA\tROTB\tAROM\tALERTS\tLRo5\tQED\tNAME\tSMILES\n") count = 0 for line in open(ifile): tokens = line.strip().split('\t') if len(tokens) > 1: smiles, title = tokens else: smiles = tokens[0] title = '' mol = Chem.MolFromSmiles(smiles) count += 1 if mol is None: print "Warning: skipping molecule ", count, " and continuing with next." continue props = properties(mol) if args.method == 'max': calc_qed = weights_max(mol, True, props) elif args.method == 'unweighted': calc_qed = weights_none(mol, True, props) else: calc_qed = weights_mean(mol, True, props) args.outfile.write( "%.2f\t%.3f\t%d\t%d\t%.2f\t%d\t%d\t%d\t%s\t%.3f\t%-s\t%s\n" % ( props[0], props[1], props[2], props[3], props[4], props[5], props[6], props[7], props[8], calc_qed, title, smiles )) else: sys.exit("Error: unknown file-type: ", filetype)