Mercurial > repos > bgruening > sucos_clustering

annotate sucos_cluster.py @ 0:f80cfac80c53 draft

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/chemicaltoolbox/sucos commit ef86cfa5f7ab5043de420511211579d03df58645"
author bgruening
date Wed, 02 Oct 2019 12:58:19 -0400
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children b8725fec8c7b
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1 #!/usr/bin/env python
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2 """
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3 Cluster a set of molecules based on their 3D overlays as determined by the SuCOS score.
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4
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5 This will generate a set of SD files, one for each cluster of molecules (presumably corresponding to a
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6 binding pocket in the protein target).
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9 SuCOS is the work of Susan Leung.
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10 GitHub: https://github.com/susanhleung/SuCOS
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11 Publication: https://doi.org/10.26434/chemrxiv.8100203.v1
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12 """
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14 import sucos, utils
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15 import argparse, gzip
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16 from rdkit import Chem
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17 import numpy as np
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18 import pandas as pd
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19 from scipy.cluster.hierarchy import linkage, fcluster
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21 ### start main execution #########################################
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24 def calc_distance_matrix(mols):
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25 """
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26 Calculate a full distance matrix for the given molecules. Identical molecules get a score of 0.0 with the maximum
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27 distance possible being 1.0.
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28 :param mols: A list of molecules. It must be possible to iterate through this list multiple times
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29 :return: A NxN 2D array of distance scores, with N being the number of molecules in the input
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30 """
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31
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32 # TODO - do we need to calculate both sides of the matrix? Tanimoto is supposed to be a symmetric distance measure,
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33 # but the matrix that is generated does not seem to be symmetric.
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34
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35 mol_fm_tuples = []
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36 for mol in mols:
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37 features = sucos.getRawFeatures(mol)
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38 mol_fm_tuples.append((mol, features))
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39
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40 matrix = []
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41 for tuple1 in mol_fm_tuples:
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42 tmp = []
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43 for tuple2 in mol_fm_tuples:
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44 if tuple1[0] == tuple2[0]:
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45 tmp.append(0.0)
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46 else:
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47 #utils.log("Calculating SuCOS between", mol1, mol2)
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48 sucos_score, fm_score, tani_score = sucos.get_SucosScore(tuple1[0], tuple2[0],
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49 tani=True, ref_features=tuple1[1], query_features=tuple2[1])
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50 tmp.append(1.0 - sucos_score)
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51 matrix.append(tmp)
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52
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53
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54 return matrix
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55
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56
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57 def cluster(matrix, threshold=0.8):
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58 """
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59 Cluster the supplied distance matrix returning an array of clusters.
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60 :param matrix: the distance matrix, as calculated with the calc_distance_matrix function.
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61 :param threshold: The clustering cuttoff. The default of 0.8 is a reasonable value to use.
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62 :return: An array of clusters, each cluster being an array of the indices from the matrix.
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63 """
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64
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65 indexes = [x for x in range(0, len(matrix))]
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66 cols = [x for x in range(0, len(matrix[0]))]
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67 #utils.log("indexes", indexes)
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68 #utils.log("cols", cols)
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69 df = pd.DataFrame(matrix, columns=cols, index=indexes)
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70 utils.log("DataFrame:", df.shape)
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71 #utils.log(df)
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72 indices = np.triu_indices(df.shape[0], k=1)
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73 #utils.log("Indices:", indices)
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74 t = np.array(df)[indices]
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75 Z = linkage(t, 'average')
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76 lig_clusters = []
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77 cluster_arr = fcluster(Z, t=threshold, criterion='distance')
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78 for i in range(np.amax(cluster_arr)):
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79 clus = df.columns[np.argwhere(cluster_arr==i+1)]
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80 lig_clusters.append([x[0] for x in clus.tolist()])
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81
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82 utils.log("Clusters", lig_clusters)
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83 return lig_clusters
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84
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85 def write_clusters_to_sdfs(mols, clusters, basename, gzip=False):
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86 """
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87 Write the molecules to SDF files, 1 file for each cluster.
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88 :param mols The molecules to write:
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89 :param clusters The clusters, as returned by the cluster function:
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90 :param basename The basename for the file name. e.g. if basename is 'output' then files like
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91 output1.sdf, output2.sdf will be written:
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92 :param gzip Whether to gzip the output
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93 :return:
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94 """
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95
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96 i = 0
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97 for cluster in clusters:
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98 i += 1
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99 filename = basename + str(i) + ".sdf"
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100 if gzip:
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101 filename += ".gz"
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102 utils.log("Writing ", len(cluster), "molecules in cluster", i, "to file", filename)
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103 output_file = utils.open_file_for_writing(filename)
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104 writer = Chem.SDWriter(output_file)
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105 for index in cluster:
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106 mol = mols[index]
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107 writer.write(mol)
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108 writer.flush()
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109 writer.close()
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110 output_file.close()
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111
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112
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113
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114 def main():
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115 parser = argparse.ArgumentParser(description='Clustering with SuCOS and RDKit')
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116 parser.add_argument('-i', '--input', help='Input file in SDF format. Can be gzipped (*.gz).')
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117 parser.add_argument('-o', '--output', default="cluster", help="Base name for output files in SDF format. " +
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118 "e.g. if value is 'output' then files like output1.sdf, output2.sdf will be created")
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119 parser.add_argument('--gzip', action='store_true', help='Gzip the outputs generating files like output1.sdf.gz, output2.sdf.gz')
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120 parser.add_argument('-t', '--threshold', type=float, default=0.8, help='Clustering threshold')
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121
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122 args = parser.parse_args()
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123 utils.log("SuCOS Cluster Args: ", args)
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124
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125 input_file = utils.open_file_for_reading(args.input)
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126 suppl = Chem.ForwardSDMolSupplier(input_file)
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127 mols = list(suppl)
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128 matrix = calc_distance_matrix(mols)
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129 clusters = cluster(matrix, threshold=args.threshold)
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130 write_clusters_to_sdfs(mols, clusters, args.output, gzip=args.gzip)
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131
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132
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133 if __name__ == "__main__":
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134 main()