Combined substituent number utilized machine learning for the development of antimicrobial agent.

Journal: Scientific reports
Published Date:

Abstract

The utilization of machine learning has a potential to improve the environment of the development of antimicrobial agents. For practical use of machine learning, it is important that the conversion of molecules information to an appropriate descriptor because too informative descriptor requires enormous computation time and experiments for gathering data, whereas a less informative descriptor has problems in validity. In this study, we utilized a descriptor only focused on substituent. The type and the position of substituents on the molecules that have a 4-quinolone structure (11,879 compounds) were converted to the combined substituent number (CSN). While the CSN does not include information on the detailed structure, physical properties, and quantum chemistry of molecules, the prediction model constructed by machine learning of CSN indicated a sufficient coefficient of determination (0.719 for the training dataset and 0.519 for the validation dataset). In addition, this CSN can easily construct the unknown molecules library which has a relatively consistent structure by recombination of substituents (32,079,318 compounds) and screening of them. The validity of the prediction model was also confirmed by growth inhibition experiments for E. coli using the model-suggested molecules and commercially available antimicrobial agents.

Authors

  • Keitaro Yamauchi
    Institute of Multidisciplinary Research for Advance Materials (IMRAM), Tohoku University, Aoba-Ku, Sendai, Miyagi, 980-8577, Japan.
  • Hirotaka Nakatsuji
    Institute of Multidisciplinary Research for Advance Materials (IMRAM), Tohoku University, Aoba-Ku, Sendai, Miyagi, 980-8577, Japan. hirotaka.nakatsuji.d1@tohoku.ac.jp.
  • Takaaki Kamishima
    East Tokyo Laboratory, Genesis Research Institute, Inc., 717-86 Futamata, Ichikawa, Chiba, 272-0001, Japan.
  • Yoshitaka Koseki
    Institute of Multidisciplinary Research for Advance Materials (IMRAM), Tohoku University, Aoba-Ku, Sendai, Miyagi, 980-8577, Japan.
  • Masaki Kubo
    Department of Chemical Engineering, Graduate School of Engineering, Tohoku University, Aoba-Ku, Sendai, Miyagi, 980-8579, Japan.
  • Hitoshi Kasai
    Institute of Multidisciplinary Research for Advance Materials (IMRAM), Tohoku University, Aoba-Ku, Sendai, Miyagi, 980-8577, Japan. kasai@tohoku.ac.jp.