Mechanistic role of plant-based bitter principles and bitterness prediction for natural product studies II: prediction tools and case studies

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22310%2F19%3A43919271" target="_blank" >RIV/60461373:22310/19:43919271 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.degruyter.com/view/j/psr.2019.4.issue-8/psr-2019-0007/psr-2019-0007.xml?lang=en" target="_blank" >https://www.degruyter.com/view/j/psr.2019.4.issue-8/psr-2019-0007/psr-2019-0007.xml?lang=en</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1515/psr-2019-0007" target="_blank" >10.1515/psr-2019-0007</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Mechanistic role of plant-based bitter principles and bitterness prediction for natural product studies II: prediction tools and case studies

Popis výsledku v původním jazyce

The first part of this chapter provides an overview of computer-based tools (algorithms, web servers, and software) for the prediction of bitterness in compounds. These tools all implement machine learning (ML) methods and are all freely accessible. For each tool, a brief description of the implemented method is provided, along with the training sets and the benchmarking results. In the second part, an attempt has been made to explain at the mechanistic level why some medicinal plants are bitter and how plants use bitter natural compounds, obtained through the biosynthetic process as important ingredients for adapting to the environment. A further exploration is made on the role of bitter natural products in the defense mechanism of plants against insect pest, herbivores, and other invaders. Case studies have focused on alkaloids, terpenoids, cyanogenic glucosides and phenolic derivatives.

Název v anglickém jazyce

Mechanistic role of plant-based bitter principles and bitterness prediction for natural product studies II: prediction tools and case studies

Popis výsledku anglicky

The first part of this chapter provides an overview of computer-based tools (algorithms, web servers, and software) for the prediction of bitterness in compounds. These tools all implement machine learning (ML) methods and are all freely accessible. For each tool, a brief description of the implemented method is provided, along with the training sets and the benchmarking results. In the second part, an attempt has been made to explain at the mechanistic level why some medicinal plants are bitter and how plants use bitter natural compounds, obtained through the biosynthetic process as important ingredients for adapting to the environment. A further exploration is made on the role of bitter natural products in the defense mechanism of plants against insect pest, herbivores, and other invaders. Case studies have focused on alkaloids, terpenoids, cyanogenic glucosides and phenolic derivatives.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10611 - Plant sciences, botany

Projekt

<a href="/cs/project/EF16_027%2F0008351" target="_blank" >EF16_027/0008351: ChemJets UCT Prague</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Physical Sciences Reviews

ISSN

2365-6581

e-ISSN

2365-659X

Svazek periodika

4

Číslo periodika v rámci svazku

8

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

16

Strana od-do

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Kód UT WoS článku

000476646400004

EID výsledku v databázi Scopus

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