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adecf03 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from rdkit import Chem
from rdkit.Chem import AllChem
def load_and_process_data(file_path_train, file_path_screening, threshold_price=100, threshold_mwt=1000):
# Load train dataset
train = pd.read_excel(file_path_train)
train['Canonical SMILES'] = train['Canonical SMILES'].apply(lambda x: Chem.MolToSmiles(Chem.MolFromSmiles(x)) if pd.notnull(x) else None)
train.dropna(subset=['Canonical SMILES'], inplace=True)
train_blue = train[train['Reactivity'] == 1]
train_red = train[train['Reactivity'] == -1]
# Load screening dataset
screening = pd.read_excel(file_path_screening)
# Apply both price and molecular weight thresholds
screening_filtered = screening[(screening['Cheapest Unit Price (USD per g/mL)'] < threshold_price) &
(screening['MWt'] < threshold_mwt)].copy()
screening_filtered['Canonical SMILES'] = screening_filtered['SMILES'].apply(lambda x: Chem.MolToSmiles(Chem.MolFromSmiles(x)) if pd.notnull(x) else None)
screening_filtered.dropna(subset=['Canonical SMILES'], inplace=True)
screening_grey = screening_filtered
return train_blue, train_red, screening_grey
def generate_fingerprints_and_reduce_dimensions(dfs):
combined_smiles = pd.concat([df['Canonical SMILES'] for df in dfs if not df.empty])
fingerprints = [AllChem.GetMorganFingerprintAsBitVect(Chem.MolFromSmiles(smiles), radius=2, nBits=1024) for smiles in combined_smiles]
fingerprints_array = np.array([list(fp) for fp in fingerprints])
# PCA
pca = PCA(n_components=2)
pca_result = pca.fit_transform(fingerprints_array)
# t-SNE with explicit parameter settings to avoid FutureWarnings
tsne = TSNE(n_components=2, random_state=42, init='random', learning_rate='auto')
tsne_result = tsne.fit_transform(fingerprints_array)
return pca_result, tsne_result, combined_smiles.index
def plot_combined_data(pca_result, tsne_result, indices, colors, labels):
plt.figure(figsize=(12, 6))
# Adjust the color list to include the RGBA tuple for grey with 20% transparency
colors = ['blue', 'red', (0.5, 0.5, 0.5, 0.02)] # Adjusting the grey color for 2% transparency
# Plot PCA results
plt.subplot(1, 2, 1)
# Ensure grey points are plotted first for them to be in the background
for i in [2, 0, 1]: # Reordering to plot grey first, followed by blue and red
idx = indices[i]
plt.scatter(pca_result[idx, 0], pca_result[idx, 1], c=colors[i], label=labels[i])
plt.title('PCA of Molecular Fingerprints')
plt.xlabel('PC1')
plt.ylabel('PC2')
plt.legend()
# Plot t-SNE results
plt.subplot(1, 2, 2)
# Same reordering logic for t-SNE plot
for i in [2, 0, 1]: # Reordering to plot grey first, followed by blue and red
idx = indices[i]
plt.scatter(tsne_result[idx, 0], tsne_result[idx, 1], c=colors[i], label=labels[i])
plt.title('t-SNE of Molecular Fingerprints')
plt.xlabel('t-SNE 1')
plt.ylabel('t-SNE 2')
plt.legend()
plt.tight_layout()
plt.show()
def main(file_path_train, file_path_screening, price=100, mwt=1000):
train_blue, train_red, screening_grey = load_and_process_data(file_path_train, file_path_screening, price, mwt)
pca_result, tsne_result, _ = generate_fingerprints_and_reduce_dimensions([train_blue, train_red, screening_grey])
# Calculate indices for coloring
len_blue = len(train_blue)
len_red = len(train_red)
len_grey = len(screening_grey)
indices = [range(len_blue), range(len_blue, len_blue + len_red), range(len_blue + len_red, len_blue + len_red + len_grey)]
print(f"Number of blue points: {len_blue}")
print(f"Number of red points: {len_red}")
print(f"Number of grey points: {len_grey}")
colors = ['blue', 'red', 'grey'] # Blue for Reactivity 1, Red for Reactivity -1, Grey for Screening
labels = ['Train Reactivity 1', 'Train Reactivity -1', 'Screening']
plot_combined_data(pca_result, tsne_result, indices, colors, labels)
return screening_grey
file_name = "Inventory-2024-03-30.xlsx"
processed_file_name = 'processed_' + file_name
# Example usage
screening_df = main('SF2. EChem Reaction Screening Dataset.xlsx', processed_file_name,50, 500)
screening_df.to_excel(processed_file_name, index=False)
print("Processing complete. The new file is saved as:", processed_file_name)
print(screening_df50_500) |