Upload IUPAC_pKa_preprocessing.py

IUPAC_pKa_preprocessing.py

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+# This is a script to preprocess the IUPAC_pKa dataset
+
+# 1. Load necessary modules
+import pandas as pd
+import numpy as np
+import rdkit
+from rdkit import Chem
+import molvs
+import tqdm
+from pathlib import Path
+
+standardizer = molvs.Standardizer()
+fragment_remover = molvs.fragment.FragmentRemover()
+
+
+# 2. Download dataset
+# https://github.com/IUPAC/Dissociation-Constants/blob/main/iupac_high-confidence_v2_2.csv
+# Suppose that we have downloaded 'iupac_high-confidence_v2_2.csv'
+
+raw_df = pd.read_csv('iupac_high-confidence_v2_2.csv')
+
+
+# 3. SMILES sanitization
+raw_df['X'] = [ \
+    rdkit.Chem.MolToSmiles(
+        fragment_remover.remove(
+        standardizer.standardize(
+        rdkit.Chem.MolFromSmiles(
+        smiles))))
+    for smiles in raw_df['SMILES']]
+
+problems = []
+for index, row in tqdm.tqdm(raw_df.iterrows()):
+    result = molvs.validate_smiles(row['X'])
+    if len(result) == 0:
+        continue
+    problems.append((row['X'], result))
+
+#   Most are because it includes the salt form and/or it is not neutralized
+for result, alert in problems:
+    print(f"SMILES: {result}, problem: {alert[0]}")
+
+raw_df.to_csv('IUPAC_pKa_sanitized.csv', index = False)   
+
+
+
+# 4. Formatting and naming
+sanitized_df = pd.read_csv('IUPAC_pKa_sanitized.csv')
+
+formatted_df = sanitized_df.drop('SMILES', axis=1)  # drop the raw SMILES column 
+formatted_df = formatted_df.rename(columns={'X': 'SMILES'}) # use the sanitized SMILES
+formatted_df = formatted_df.rename(columns={'pka_value': 'Y'}) # rename the pKa column
+formatted_df['Y'] = pd.to_numeric(formatted_df['Y'], errors='coerce') # convert string to float64 for the 'Y' column
+column_to_move = formatted_df.columns[-1] # Move the sanitized SMILES 
+new_order = formatted_df.columns.tolist() 
+new_order.remove(column_to_move)  
+new_order.insert(1, column_to_move) 
+formatted_df = formatted_df[new_order]
+
+formatted_df.to_csv('IUPAC_pKa_formatted.csv', index = False)
+
+
+
+# 5. Import modules to split the dataset
+import sys
+from rdkit import DataStructs
+from rdkit.Chem import AllChem as Chem
+from rdkit.Chem import PandasTools 
+
+
+# 6. Split the dataset into train and test
+
+class MolecularFingerprint:
+    def __init__(self, fingerprint):
+        self.fingerprint = fingerprint
+
+    def __str__(self):
+        return self.fingerprint.__str__()
+
+def compute_fingerprint(molecule):
+    try:
+        fingerprint = Chem.GetMorganFingerprintAsBitVect(molecule, 2, nBits=1024)
+        result = np.zeros(len(fingerprint), np.int32)
+        DataStructs.ConvertToNumpyArray(fingerprint, result)
+        return MolecularFingerprint(result)
+    except:
+        print("Fingerprints for a structure cannot be calculated")
+        return None
+
+def tanimoto_distances_yield(fingerprints, num_fingerprints):
+    for i in range(1, num_fingerprints):
+        yield [1 - x for x in DataStructs.BulkTanimotoSimilarity(fingerprints[i], fingerprints[:i])]
+
+def butina_cluster(fingerprints, num_points, distance_threshold, reordering=False):
+    nbr_lists = [None] * num_points
+    for i in range(num_points):
+        nbr_lists[i] = []
+
+    dist_fun = tanimoto_distances_yield(fingerprints, num_points)
+    for i in range(1, num_points):
+        dists = next(dist_fun)
+
+        for j in range(i):
+            dij = dists[j]
+            if dij <= distance_threshold:
+                nbr_lists[i].append(j)
+                nbr_lists[j].append(i)
+
+    t_lists = [(len(y), x) for x, y in enumerate(nbr_lists)]
+    t_lists.sort(reverse=True)
+
+    res = []
+    seen = [0] * num_points
+    while t_lists:
+        _, idx = t_lists.pop(0)
+        if seen[idx]:
+            continue
+        t_res = [idx]
+        for nbr in nbr_lists[idx]:
+            if not seen[nbr]:
+                t_res.append(nbr)
+                seen[nbr] = 1
+        if reordering:
+            nbr_nbr = [nbr_lists[t] for t in t_res]
+            nbr_nbr = frozenset().union(*nbr_nbr)
+            for x, y in enumerate(t_lists):
+                y1 = y[1]
+                if seen[y1] or (y1 not in nbr_nbr):
+                    continue
+                nbr_lists[y1] = set(nbr_lists[y1]).difference(t_res)
+                t_lists[x] = (len(nbr_lists[y1]), y1)
+            t_lists.sort(reverse=True)
+        res.append(tuple(t_res))
+    return tuple(res)
+
+def hierarchal_cluster(fingerprints):
+
+      num_fingerprints = len(fingerprints)
+
+      av_cluster_size = 8
+      dists = []
+
+      for i in range(0, num_fingerprints):
+          sims = DataStructs.BulkTanimotoSimilarity(fingerprints[i], fingerprints)
+          dists.append([1 - x for x in sims])
+
+      dis_array = ssd.squareform(dists)
+      Z = hierarchy.linkage(dis_array)
+      average_cluster_size = av_cluster_size
+      cluster_amount = int(num_fingerprints / average_cluster_size)
+      clusters = hierarchy.cut_tree(Z, n_clusters=cluster_amount)
+
+      clusters = list(clusters.transpose()[0])
+      cs = []
+      for i in range(max(clusters) + 1):
+          cs.append([])
+
+      for i in range(len(clusters)):
+          cs[clusters[i]].append(i)
+      return cs
+
+def cluster_fingerprints(fingerprints, method="Auto"):
+    num_fingerprints = len(fingerprints)
+
+    if method == "Auto":
+        method = "TB" if num_fingerprints >= 10000 else "Hierarchy"
+
+    if method == "TB":
+        cutoff = 0.56
+        print("Butina clustering is selected. Dataset size is:", num_fingerprints)
+        clusters = butina_cluster(fingerprints, num_fingerprints, cutoff)
+
+    elif method == "Hierarchy":
+        print("Hierarchical clustering is selected. Dataset size is:", num_fingerprints)
+        clusters = hierarchal_cluster(fingerprints)
+
+    return clusters
+
+def split_dataframe(dataframe, smiles_col_index, fraction_to_train, split_for_exact_fraction=True, cluster_method="Auto"):
+    try:
+        import math
+        smiles_column_name = dataframe.columns[smiles_col_index]
+        molecule = 'molecule'
+        fingerprint = 'fingerprint'
+        group = 'group'
+        testing = 'testing'
+
+        try:
+            PandasTools.AddMoleculeColumnToFrame(dataframe, smiles_column_name, molecule)
+        except:
+            print("Exception occurred during molecule generation...")
+
+        dataframe = dataframe.loc[dataframe[molecule].notnull()]
+        dataframe[fingerprint] = [compute_fingerprint(m) for m in dataframe[molecule]]
+        dataframe = dataframe.loc[dataframe[fingerprint].notnull()]
+
+        fingerprints = [Chem.GetMorganFingerprintAsBitVect(m, 2, nBits=2048) for m in dataframe[molecule]]
+        clusters = cluster_fingerprints(fingerprints, method=cluster_method)
+
+        dataframe.drop([molecule, fingerprint], axis=1, inplace=True)
+
+        last_training_index = int(math.ceil(len(dataframe) * fraction_to_train))
+        clustered = None
+        cluster_no = 0
+        mol_count = 0
+
+        for cluster in clusters:
+            cluster_no = cluster_no + 1
+            try:
+                one_cluster = dataframe.iloc[list(cluster)].copy()
+            except:
+                print("Wrong indexes in Cluster: %i, Molecules: %i" % (cluster_no, len(cluster)))
+                continue
+
+            one_cluster.loc[:, 'ClusterNo'] = cluster_no
+            one_cluster.loc[:, 'MolCount'] = len(cluster)
+
+            if (mol_count < last_training_index) or (cluster_no < 2):
+                one_cluster.loc[:, group] = 'training'
+            else:
+                one_cluster.loc[:, group] = testing
+
+            mol_count += len(cluster)
+            clustered = pd.concat([clustered, one_cluster], ignore_index=True)
+
+        if split_for_exact_fraction:
+            print("Adjusting test to train ratio. It may split one cluster")
+            clustered.loc[last_training_index + 1:, group] = testing
+
+        print("Clustering finished. Training set size is %i, Test set size is %i, Fraction %.2f" %
+              (len(clustered.loc[clustered[group] != testing]),
+               len(clustered.loc[clustered[group] == testing]),
+               len(clustered.loc[clustered[group] == testing]) / len(clustered)))
+
+    except KeyboardInterrupt:
+        print("Clustering interrupted.")
+
+    return clustered
+
+
+def realistic_split(df, smile_col_index, frac_train, split_for_exact_frac=True, cluster_method = "Auto"):
+  return split_dataframe(df.copy(), smile_col_index, frac_train, split_for_exact_frac, cluster_method=cluster_method)
+
+def split_df_into_train_and_test_sets(df):
+    df['group'] = df['group'].str.replace(' ', '_')
+    df['group'] = df['group'].str.lower()
+    train = df[df['group'] == 'training']
+    test = df[df['group'] == 'testing']
+    return train, test
+
+
+formatted_df = pd.read_csv('IUPAC_pKa_formatted.csv')
+smiles_index = 1  # Because smiles is in the second column
+realistic = realistic_split(formatted_df.copy(), smiles_index, 0.75, split_for_exact_frac=True, cluster_method="Auto")
+realistic_train, realistic_test = split_df_into_train_and_test_sets(realistic)
+
+
+#8. Test and train datasets have been made
+realistic_train.to_csv("IUPAC_pKa_train.csv", index=False)
+realistic_test.to_csv("IUPAC_pKa_test.csv", index=False)
+
+realistic_train.to_parquet('IUPAC_pKa_train.parquet', index=False)
+realistic_test.to_parquet('IUPAC_pKa_test.parquet', index=False)