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Ranking Methods of Candidates for p53 Inhibitors Considering Cytotoxicity Using Machine Learning

In this paper, we propose new methods to make the rankings of candidates for p53 inhibitors, considering both radioprotective function and cytotoxicity. We use features about compound's structure including fingerprints, machine learning methods and ranking methods. As a result, we presented the regression models of cytotoxicity and radioprotective function of them to determine the rankings.