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Personalized medicine is expected to maximize the intended drug effects andminimize side effects by treating patients based on their genetic profiles.Thus, it is important to generate drugs based on the genetic profiles ofdiseases, especially in anticancer drug discovery. However, this is challengingbecause the vast chemical space and variations in cancer properties require ahuge time resource to search for proper molecules. Therefore, an efficient andfast search method considering genetic profiles is required for de novomolecular design of anticancer drugs. Here, we propose a faster moleculargenerative model with genetic algorithm and tree search for cancer samples(FasterGTS). FasterGTS is constructed with a genetic algorithm and a MonteCarlo tree search with three deep neural networks: supervised learning,self-trained, and value networks, and it generates anticancer molecules basedon the genetic profiles of a cancer sample. When compared to other methods,FasterGTS generated cancer sample-specific molecules with general chemicalproperties required for cancer drugs within the limited numbers of samplings.We expect that FasterGTS contributes to the anticancer drug generation.

A molecular generative model with genetic algorithm and tree search for cancer samples