Open Access

Frontiers in Pharmacology, volume 10

Identification of novel antibacterials using machine-learning techniques

Ivanenkov Yan A ^{1, 2, 3, 4}

Zhavoronkov Alex ¹

Yamidanov Renat S. ^{1, 4}

Osterman Ilya A. ^{2, 5}

Sergiev Petr V. ^{5, 6}

Aladinskiy Vladimir A. ^{1, 3}

Aladinskaya Anastasia V. ^{1, 3}

Terentiev Victor A. ^{1, 3, 4}

Veselov Mark S. ^{1, 3, 4}

Ayginin Andrey A. ^{3, 4}

Kartsev Victor G. ⁷

Skvortsov Dmitry A. ^{2, 8}

Chemeris Alexey V. ⁴

Baimiev Alexey Kh. ⁴

Sofronova Alina A. ⁹

Malyshev Alexander S. ¹⁰

Filkov Gleb I. ³

Bezrukov Dmitry S. ^{2, 5}

Zagribelnyy Bogdan A. ²

Putin Evgeny O ¹¹

Puchinina Maria M. ³

Dontsova Olga A. ^{2, 5, 6}

Hide authors affiliations Show authors affiliations: 11 affiliations

Insilico Medicine, Inc. Johns Hopkins University, United States |

Department of Chemistry, Lomonosov Moscow State University, Russia |

Moscow Institute of Physics and Technology (State University), Russia |

⁴

Institute of Biochemistry and Genetics Russian Academy of Science (IBG RAS) Ufa Scientific Centre, Russia

Institute of Biochemistry and Genetics of the Ufa Federal Research Center of the Russian Academy of Sciences

Ufa Federal Research Center of the Russian Academy of Sciences

⁵

Skolkovo Institute of Science and Technology, Russia |

⁶

Department of Chemistry and A.N. Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Russia |

⁷

InterBioScreen ltd, Russia |

⁸

Faculty of Biology and Biotechnologies, Higher School of Economics, Russia |

⁹

Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russia |

¹⁰

Faculty of Medicine, Lomonosov Moscow State University, Moscow |

¹¹

Computer Technologies Lab, ITMO University, Russia |

Publication type: Journal Article

Publication date: 2019-08-27

Frontiers Media S.A.

Journal: Frontiers in Pharmacology

Quartile SCImago

Quartile WOS

Impact factor: 5.6

ISSN: 16639812

DOI: 10.3389/fphar.2019.00913

Copy DOI

PubMed ID: 31507413

Pharmacology

Pharmacology (medical)

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 that 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 Escherichia 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 these data. For external validation, we used 5,000 compounds with low similarity towards training samples. The antibacterial activity of the selected molecules against E. coli was assessed using a comprehensive biological study. Kohonen-based nonlinear 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.

By date By citations

Citations by journals

	1 2 3
Frontiers in Pharmacology	Frontiers in Pharmacology, 3, 12.5% Frontiers in Pharmacology 3 publications, 12.5%
Frontiers in Microbiology	Frontiers in Microbiology, 1, 4.17% Frontiers in Microbiology 1 publication, 4.17%
Bioorganic and Medicinal Chemistry Letters	Bioorganic and Medicinal Chemistry Letters, 1, 4.17% Bioorganic and Medicinal Chemistry Letters 1 publication, 4.17%
Future Medicinal Chemistry	Future Medicinal Chemistry, 1, 4.17% Future Medicinal Chemistry 1 publication, 4.17%
Antibiotics	Antibiotics, 1, 4.17% Antibiotics 1 publication, 4.17%
Molecular Diversity	Molecular Diversity, 1, 4.17% Molecular Diversity 1 publication, 4.17%
Journal of Medical Systems	Journal of Medical Systems, 1, 4.17% Journal of Medical Systems 1 publication, 4.17%
Drug Discovery Today	Drug Discovery Today, 1, 4.17% Drug Discovery Today 1 publication, 4.17%
Artificial Intelligence in the Life Sciences	Artificial Intelligence in the Life Sciences, 1, 4.17% Artificial Intelligence in the Life Sciences 1 publication, 4.17%
Computers in Biology and Medicine	Computers in Biology and Medicine, 1, 4.17% Computers in Biology and Medicine 1 publication, 4.17%
Current Opinion in Chemical Biology	Current Opinion in Chemical Biology, 1, 4.17% Current Opinion in Chemical Biology 1 publication, 4.17%
Chemical Biology and Drug Design	Chemical Biology and Drug Design, 1, 4.17% Chemical Biology and Drug Design 1 publication, 4.17%
Journal of Chemical Information and Modeling	Journal of Chemical Information and Modeling, 1, 4.17% Journal of Chemical Information and Modeling 1 publication, 4.17%
ACS Infectious Diseases	ACS Infectious Diseases, 1, 4.17% ACS Infectious Diseases 1 publication, 4.17%
Molecular Pharmaceutics	Molecular Pharmaceutics, 1, 4.17% Molecular Pharmaceutics 1 publication, 4.17%
mSystems	mSystems, 1, 4.17% mSystems 1 publication, 4.17%
Biochemistry (Moscow)	Biochemistry (Moscow), 1, 4.17% Biochemistry (Moscow) 1 publication, 4.17%
Expert Opinion on Drug Discovery	Expert Opinion on Drug Discovery, 1, 4.17% Expert Opinion on Drug Discovery 1 publication, 4.17%
Journal of Biomolecular Structure and Dynamics	Journal of Biomolecular Structure and Dynamics, 1, 4.17% Journal of Biomolecular Structure and Dynamics 1 publication, 4.17%
Lecture Notes in Computer Science	Lecture Notes in Computer Science, 1, 4.17% Lecture Notes in Computer Science 1 publication, 4.17%
Annals of the New York Academy of Sciences	Annals of the New York Academy of Sciences, 1, 4.17% Annals of the New York Academy of Sciences 1 publication, 4.17%
	1 2 3

Citations by publishers

	1 2 3 4 5
Elsevier	Elsevier, 5, 20.83% Elsevier 5 publications, 20.83%
Frontiers Media S.A.	Frontiers Media S.A., 4, 16.67% Frontiers Media S.A. 4 publications, 16.67%
Springer Nature	Springer Nature, 3, 12.5% Springer Nature 3 publications, 12.5%
American Chemical Society (ACS)	American Chemical Society (ACS), 3, 12.5% American Chemical Society (ACS) 3 publications, 12.5%
Wiley	Wiley, 2, 8.33% Wiley 2 publications, 8.33%
Taylor & Francis	Taylor & Francis, 2, 8.33% Taylor & Francis 2 publications, 8.33%
Future Medicine	Future Medicine, 1, 4.17% Future Medicine 1 publication, 4.17%
Multidisciplinary Digital Publishing Institute (MDPI)	Multidisciplinary Digital Publishing Institute (MDPI), 1, 4.17% Multidisciplinary Digital Publishing Institute (MDPI) 1 publication, 4.17%
American Society for Microbiology	American Society for Microbiology, 1, 4.17% American Society for Microbiology 1 publication, 4.17%
Pleiades Publishing	Pleiades Publishing, 1, 4.17% Pleiades Publishing 1 publication, 4.17%
	1 2 3 4 5

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Ivanenkov Y. A. et al. Identification of novel antibacterials using machine-learning techniques // Frontiers in Pharmacology. 2019. Vol. 10.

GOST all authors (up to 50) Copy

Ivanenkov Y. A., Zhavoronkov A., Yamidanov R. S., Osterman I. A., Sergiev P. V., Aladinskiy V. A., Aladinskaya A. V., Terentiev V. A., Veselov M. S., Ayginin A. A., Kartsev V. G., Skvortsov D. A., Chemeris A. V., Baimiev A. K., Sofronova A. A., Malyshev A. S., Filkov G. I., Bezrukov D. S., Zagribelnyy B. A., Putin E. O., Puchinina M. M., Dontsova O. A. Identification of novel antibacterials using machine-learning techniques // Frontiers in Pharmacology. 2019. Vol. 10.

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RIS Copy

TY - JOUR

DO - 10.3389/fphar.2019.00913

UR - https://doi.org/10.3389%2Ffphar.2019.00913

TI - Identification of novel antibacterials using machine-learning techniques

T2 - Frontiers in Pharmacology

AU - Ivanenkov, Yan A

AU - Zhavoronkov, Alex

AU - Yamidanov, Renat S.

AU - Osterman, Ilya A.

AU - Sergiev, Petr V.

AU - Aladinskiy, Vladimir A.

AU - Aladinskaya, Anastasia V.

AU - Terentiev, Victor A.

AU - Veselov, Mark S.

AU - Ayginin, Andrey A.

AU - Kartsev, Victor G.

AU - Skvortsov, Dmitry A.

AU - Chemeris, Alexey V.

AU - Baimiev, Alexey Kh.

AU - Sofronova, Alina A.

AU - Malyshev, Alexander S.

AU - Filkov, Gleb I.

AU - Bezrukov, Dmitry S.

AU - Zagribelnyy, Bogdan A.

AU - Putin, Evgeny O

AU - Puchinina, Maria M.

AU - Dontsova, Olga A.

PY - 2019

DA - 2019/08/27 00:00:00

PB - Frontiers Media S.A.

VL - 10

PMID - 31507413

SN - 1663-9812

ER -

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@article{2019_Ivanenkov,

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

title = {Identification of novel antibacterials using machine-learning techniques},

journal = {Frontiers in Pharmacology},

year = {2019},

volume = {10},

publisher = {Frontiers Media S.A.},

month = {aug},

url = {https://doi.org/10.3389%2Ffphar.2019.00913},

doi = {10.3389/fphar.2019.00913}

}

Found error?

Publisher

Frontiers Media S.A.

Journal

Frontiers in Pharmacology

Quartile SCImago

Quartile WOS

Impact factor

5.6

ISSN

16639812 (Print)

Labs

Laboratory of Nucleoprotein Chemistry, Department of Chemistry of Natural Compounds, Faculty of Chemistry

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