Drug Efficacy Recommendation System of Glioblastoma (GBM) Using Deep Learning

Glioblastoma (GBM), a common cancer of the central nervous system (CNS), is considered incurable worldwide. The treatment of GBM varies from patient to patient, as conventional medical treatments do not apply to all patients with similar symptoms. Therefore, drug efficacy recommendation systems are very useful in the treatment of various types of cancers. The Genomics of Drug Sensitivity in Cancer (GDSC) database was used as the primary data source, containing 135,242 cell line-drug interactions, inhibitory concentration of cancer drug for more than 800 cancer cell lines. Each cell line provides gene expression values, which are further normalized through Z-transformation for each gene. However, we utilized only 47 genes in our research due to limitations in computer processing speed and memory. A drug efficacy recommendation system was constructed using a deep learning method that combines gene expression, drug Simplified Molecular Input Line Entry System (SMILES), and inhibitory concentration features. A panel of 47 genes associated with GBM was processed using two deep learning models: Artificial Neural Network (ANN) and Convolutional Neural Network (CNN). This approach addresses the challenge of personalized treatment for GBM, offering the potential for improved therapeutic outcomes. The results of the recommendation system are calculated based on Half Maximal Inhibitory Concentration (IC50) values, which represent the therapeutic effectiveness in inhibiting the growth of GBM cells. CNN outperformed ANN with a significant margin, achieving a Root Mean Square Error (RMSE) of 0.9822 compared to 1.2127. These results are also consistent with other metrics, including Pearson correlation, Spearman correlation, and Mean Absolute Error (MAE). According to the study, the system can accurately predict the effectiveness of drugs on GBM cancer genes. This study has the potential to predict drug efficacy during medical procedures.