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import pandas as pd
import numpy as np
def combine_csv_files(csv_file_list):
# Read and combine all CSV files into a single dataframe
combined_df = pd.concat([pd.read_csv(file) for file in csv_file_list], ignore_index=True)
# Remove duplicate rows
combined_df = combined_df.drop_duplicates(subset=['seq', 'smiles_can', 'neg_log10_affinity_M'])
# Aggregate duplicates more efficiently using agg function
combined_df = combined_df.groupby(['seq', 'smiles_can']).agg(
neg_log10_affinity_M=('neg_log10_affinity_M', 'mean')
).reset_index()
np.random.seed(42)
mask_value_5 = combined_df['neg_log10_affinity_M'] == 5
rows_to_keep = ~mask_value_5 | (mask_value_5 & (np.random.rand(len(combined_df)) < 0.3))
combined_df = combined_df[rows_to_keep].reset_index(drop=True)
# Sort by neg_log10_affinity_M
combined_df = combined_df.sort_values('neg_log10_affinity_M', ascending=False)
# Calculate mean and standard deviation of affinities
affinity_mean = combined_df['neg_log10_affinity_M'].mean()
affinity_std = combined_df['neg_log10_affinity_M'].std()
# Add columns for mean, std and normalized values
combined_df['affinity_mean'] = affinity_mean
combined_df['affinity_std'] = affinity_std
combined_df['affinity_norm'] = (combined_df['neg_log10_affinity_M'] - affinity_mean) / affinity_std
return combined_df
combined_df = combine_csv_files(['glaser.parquet', 'davis-filtered.parquet', 'pdbbind-2020-combined.parquet', 'bindingdb-kd-filtered.parquet', 'bindingdb-ki.parquet', 'bindingdb-ic50.parquet'])
combined_df.to_parquet('affinity-data-combined.parquet', index=False) |