Identification of novel antibacterials using machine-learning techniques

Yan A. Ivanenkov, Alex Zhavoronkov, Renat S. Yamidanov, Ilya A. Osterman, Petr V. Sergiev, Vladimir A. Aladinskiy, Anastasia V. Aladinskaya, Victor A. Terentiev, Mark S. Veselov, Andrey A. Ayginin, Victor G. Kartsev, Dmitry A. Skvortsov, Alexey V. Chemeris, Alexey Kh Baimiev, Alina A. Sofronova, Alexander S. Malyshev, Gleb I. Filkov, Dmitry S. Bezrukov, Bogdan A. Zagribelnyy, Evgeny O. PutinMaria M. Puchinina, Olga A. Dontsova

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)

Abstract

Many pharmaceutical companies are avoiding the development of novel antibacterials due to a range of rational reasons and the high risk of failure. However, there is an urgent need for novel antibiotics especially against resistant bacterial strains. Available in silico models suffer from many drawbacks and, therefore, are not applicable for scoring novel molecules with high structural diversity by their antibacterial potency. Considering this, the overall aim of this study was to develop an efficient in silico model able to find compounds which have plenty of chances to exhibit antibacterial activity. Based on a proprietary screening campaign, we have accumulated a representative dataset of more than 140,000 molecules with antibacterial activity against E. coli assessed in the same assay and under the same conditions. This intriguing set has no analogue in the scientific literature. We applied six in silico techniques to mine this data. For external validation, we used 5,000 compounds with low similarity towards training samples. Antibacterial activity of the selected molecules against E. coli was assessed using a comprehensive biological study. Kohonen-based non-linear mapping was used for the first time and provided the best predictive power (av. 75.5%). Several compounds showed an outstanding antibacterial potency and were identified as translation machinery inhibitors in vitro and in vivo. For the best compounds, MIC and CC50 values were determined to allow us to estimate a selectivity index (SI). Many active compounds have a robust IP position.

Original languageEnglish
Article number913
JournalFrontiers in Pharmacology
Volume10
Issue numberJULY
DOIs
Publication statusPublished - 2019

Keywords

  • Kohonen-based SOM
  • Machine learning techniques
  • Novel antibacterials
  • Translation inhibitors
  • Virtual screening

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