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import pandas as pd
from vina_gpu import QuickVina2GPU, VINA
df_20k = pd.read_csv('lig_llm_prop.csv')
df_drug = pd.read_csv('20_targets/drugs_filtered/PDE4A_HUMAN.csv')
combined_df = pd.concat([df_20k, df_drug], ignore_index=True)
vina = QuickVina2GPU(vina_path=VINA, target="PDE4A_3tvx")
smiles_list = combined_df['SMILES'].tolist()
mols, affinities = [],[]
batchsize = 1000
for i in range(0, len(smiles_list), batchsize):
batch_smiles = smiles_list[i:i + batchsize]
batch_mols, batch_affinities = vina.calculate_rewards(batch_smiles)
mols.extend(batch_mols)
affinities.extend(batch_affinities)
#save mols and affinities to a txt file
with open('20_targets/docked/PDE4A_docked.txt', 'w') as f:
for mol, affinity in zip(mols, affinities):
f.write(f"{mol}\t{affinity}\n")
# Save the results to a CSV file
combined_df['Affinity'] = affinities
combined_df.to_csv('20_targets/docked/PDE4A_docked.csv', index=False)
# df_20k = pd.read_csv('lig_llm_prop.csv')
# df_drug = pd.read_csv('20_targets/drugs_filtered/PPARA_HUMAN.csv')
# combined_df = pd.concat([df_20k, df_drug], ignore_index=True)
# vina = QuickVina2GPU(vina_path=VINA, target="PPARA_7bpz")
# smiles_list = combined_df['SMILES'].tolist()
# mols, affinities = vina.calculate_rewards(smiles_list)
# #save mols and affinities to a txt file
# with open('20_targets/docked/PPARA_docked.txt', 'w') as f:
# for mol, affinity in zip(mols, affinities):
# f.write(f"{mol}\t{affinity}\n")
# # Save the results to a CSV file
# combined_df['Affinity'] = affinities
# combined_df.to_csv('20_targets/docked/PPARA_docked.csv', index=False)
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