Autoencoder with regression-based model for repurposing drugs for epithelial ovarian cancer - Agraviador, Julianne Marie A.
Ovarian cancer is one of the lethal cancers with a high incidence, high mortality and low survival rates. Other factors that worsen the situation are the limited options for effective and safer drugs and the development of resistance to treatments. One of the ways to solve this issue is through drug repurposing. The current study utilized a sequence-tosequence autoencoder with regression and variational autoencoder with regression machine learning models to predict pIC50 values of compounds against the PIK3CA receptor. The canonical SMILES strings of compounds retrieved from the Drugbank database and their corresponding pIC50 values from the ChEMBL database were used as input in the training, the testing and the validation of the model. The top five compounds with the best predicted pIC50 values were analyzed using ADMETlab 2.0 for their carcinogenicity, lipophilicity, and blood brain barrier (BBB) penetration characteristics and were further subjected to molecular docking. The compounds CHEMBL379000, βApopicropodophyllin, Bicyclol, Griseofulvin, and 4-Methoxymethyl Fentanyl exhibited the best predicted pIC50 values. This was determined using the variational autoencoder with regression model, which had a maximum r2 value of 0.69. β-Apopicropodophyllin exhibited the most favorable binding affinity values among the top compounds.