Extreme learning machine models for predicting the n-octanol/water partition coefficient (Kow) data of organic compounds

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41330%2F22%3A91505" target="_blank" >RIV/60460709:41330/22:91505 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S2213343722014257" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2213343722014257</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jece.2022.108552" target="_blank" >10.1016/j.jece.2022.108552</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Extreme learning machine models for predicting the n-octanol/water partition coefficient (Kow) data of organic compounds

Popis výsledku v původním jazyce

The partition coefficient of n-octanol/water (Kow) is generally considered a valuable characteristic in many natural sciences fields. Predictive quantitative structure-property and relationship (QSPR) models for Kow are encouraged since the experimental data are not always available. Extreme learning machine (ELM) method with the advantages of fast learning speed and good generalization ability has been applied to various domains. This work introduced COSMO descriptors of molecules as descriptors and the ELM algorithm to construct estimation models for Kow of organics. Four ELM models were developed and their outcomes were compared with the MLR models produced by the same descriptors. Results revealed that the proposed ELM models were reliable and applicable to predicting the logKow values of compounds, especially the ELM4 model with the best correlation coefficient R2 (0,949) and root-mean-square error RMSE (0,358). Therefore, the proposed approaches are effective and feasible, and can be extensivel

Název v anglickém jazyce

Extreme learning machine models for predicting the n-octanol/water partition coefficient (Kow) data of organic compounds

Popis výsledku anglicky

The partition coefficient of n-octanol/water (Kow) is generally considered a valuable characteristic in many natural sciences fields. Predictive quantitative structure-property and relationship (QSPR) models for Kow are encouraged since the experimental data are not always available. Extreme learning machine (ELM) method with the advantages of fast learning speed and good generalization ability has been applied to various domains. This work introduced COSMO descriptors of molecules as descriptors and the ELM algorithm to construct estimation models for Kow of organics. Four ELM models were developed and their outcomes were compared with the MLR models produced by the same descriptors. Results revealed that the proposed ELM models were reliable and applicable to predicting the logKow values of compounds, especially the ELM4 model with the best correlation coefficient R2 (0,949) and root-mean-square error RMSE (0,358). Therefore, the proposed approaches are effective and feasible, and can be extensivel

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10511 - Environmental sciences (social aspects to be 5.7)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING

ISSN

2213-3437

e-ISSN

2213-3437

Svazek periodika

10

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

8

Strana od-do

1-8

Kód UT WoS článku

000859750100005

EID výsledku v databázi Scopus

2-s2.0-85139105297