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

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Extreme learning machine models for predicting the n-octanol/water partition coefficient (Kow) data of organic compounds
Original language description
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
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10511 - Environmental sciences (social aspects to be 5.7)

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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