Predicting Radiation Protection and Toxicity of p53 Targeting Radioprotectors using Machine Learning

摘要

This paper explores machine learning application in the case of drug discovery. We apply extreme gradient boosting and K-nearest neighbor to biomedical data and it significantly outperform former studies using feature selection and proper tuning parameters. The novel application motivated by a recent circumstance that there is a need for rapid development of radioprotectors. It mainly targets the DNA of growing cancer cells, whereas it has adverse side effects, including p53-induced apoptosis of normal tissues and cells. It considered that p53 would be a target for therapeutic and mitigated radioprotection to escape from the apoptotic fate. On the other hand, many types of compounds contain several level of toxicity, so it is important to consider not only radiation protection but also the level of toxicity of candidate compounds for radioprotectors. Compounds of radio-protectors that have low toxicity and high radiation protection are expected. It is possible to do efficiently the compounds discovery using machine learning. This study predicts compounds of radioprotectors using plural machine learning methods, Extreme Gradient Boosting, K-nearest neighbor, SVM and Random Forest. We compare these methods and suggest proper methods to predict radioprotectors.