PuSH - Publikationsserver des Helmholtz Zentrums München: Rational design of isonicotinic acid hydrazide derivatives with antitubercular activity: Machine learning, molecular docking, synthesis and biological testing.

Informationen

Hinweise zu Qualitätskriterien von Zeitschriften

Open-Access-Richtlinie der Helmholtz-Gemeinschaft 2016

CC Licencences

Metriken für Publikationen

Navigation

Startseite

English

EVA: Veröffentlichen

Neuer EVA Antrag

Recherche

Erweiterte Suche

Durchblättern nach ...

... HMGU-Autoren/Konsortien

... Organisationsstruktur

... Zeitschriften

... Publikationstypen

... Forschungsdaten

... Arbeitsgruppen

... Erscheinungsjahr

Publikationen im Überblick

Statistik

HGF Fortschrittsbericht

OA Publikationen

Eintragen

Neue Publikation eintragen

Neue Publikation holen aus...

...EVA

Fehlende Publikation melden

Highlights

Suche

Hilfe & Kontakt

Ansprechpartner

Hilfe

Datenschutz

Helmholtz Open Science

Bibliometrische Indikatoren

SHERPA/RoMEO

DOAJ

Export:

Text

Endnote (RIS) BIB

BibTeX

Kovalishyn, V.V.* ; Grouleff, J.* ; Semenyuta, I.* ; Sinenko, V.O.* ; Slivchuk, S.R.* ; Hodyna, D.* ; Brovarets, V.* ; Blagodatny, V.* ; Poda, G.* ; Tetko, I.V. ; Metelytsia, L.*

Rational design of isonicotinic acid hydrazide derivatives with antitubercular activity: Machine learning, molecular docking, synthesis and biological testing.

Chem. Biol. Drug Des. 92, 1272-1278 (2018)

Postprint

DOI

	Open Access Green

Abstract
Metriken
Zusatzinfos

The problem of designing new antitubercular drugs against multiple drug‐resistant tuberculosis (MDR‐TB) was addressed using advanced machine learning methods. As there are only few published measurements against MDR‐TB, we collected a large literature data set and developed models against the non‐resistant H37Rv strain. The predictive accuracy of these models had a coefficient of determination q² = .7–.8 (regression models) and balanced accuracies of about 80% (classification models) with cross‐validation and independent test sets. The models were applied to screen a virtual chemical library, which was designed to have MDR‐TB activity. The seven most promising compounds were identified, synthesized and tested. All of them showed activity against the H37Rv strain, and three molecules demonstrated activity against the MDR‐TB strain. The docking analysis indicated that the discovered molecules could bind enoyl reductase, InhA, which is required in mycobacterial cell wall development. The models are freely available online (http://ochem.eu/article/103868) and can be used to predict potential anti‐TB activity of new chemicals.

Impact Factor

Scopus SNIP

Web of Science
Times Cited

Scopus
Cited By

Altmetric

2.328

0.686