Application of machine learning models to predict ecotoxicity of ionic liquids (Vibrio fischeri) using VolSurf principal properties

Amabel Tabaaza Grace; Nii Tackie Otoo Bennet; B. Zaini Dzulkarnain; Lal Bhajan

doi:10.21741/9781644902516-27

Application of machine learning models to predict ecotoxicity of ionic liquids (Vibrio fischeri) using VolSurf principal properties

Amabel Tabaaza Grace, Nii Tackie Otoo Bennet, B. Zaini Dzulkarnain, Lal Bhajan^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Owing to the rapid growth in IL synthesis due to feasible cation-anion combinations, knowledge of their toxicity is pertinent for their successful application. Toxicity information measurement of various ILs on a broad spectrum of conditions through experimental techniques is way demanding on time, resources, and is at times impractical. Various research works have been performed in Quantitative Structure Activity/Property Relationship (QSAR/QSPR) for IL toxicity prediction. In this study, ML models have been trained and tested on Vibrio fischeri toxicity data set using in silico principal properties (PPs) as descriptors. Deploying this properties aid in considering both the effect of cations and anions on Vibrio fischeri toxicity prediction. Among the models trained, the Random Forest model proved to be the most precise nevertheless, decision tree model was the most accurate and consistent. Considering the importance of the descriptors to Vibrio fischeri toxicity selection techniques and model optimization.

Original language	English
Title of host publication	Sustainable Processes and Clean Energy Transition- International Conference on Sustainable Processes and Clean Energy Transition, ICSuPCET 2022
Editors	Yeong Yin Fong, Lam Man Kee, Norwahyu Jusoh, Chong Yang Chuah
Publisher	Association of American Publishers
Pages	234-247
Number of pages	14
ISBN (Print)	9781644902509
DOIs	https://doi.org/10.21741/9781644902516-27
State	Published - 2023
Externally published	Yes
Event	International Conference on Sustainable Processes and Clean Energy Transition, ICSuPCET 2022 - Kuching, Malaysia Duration: 1 Dec 2022 → 2 Dec 2022

Publication series

Name	Materials Research Proceedings
Volume	29
ISSN (Print)	2474-3941
ISSN (Electronic)	2474-395X

Conference

Conference	International Conference on Sustainable Processes and Clean Energy Transition, ICSuPCET 2022
Country/Territory	Malaysia
City	Kuching
Period	1/12/22 → 2/12/22

Bibliographical note

Publisher Copyright:
© 2023, Association of American Publishers. All rights reserved.

Keywords

Ionic liquids
Machine learning
QSPR/QSAR and Principal Properties
Toxicity
Vibrio Fischeri

ASJC Scopus subject areas

General Materials Science

Access to Document

10.21741/9781644902516-27

Cite this

Grace, A. T., Bennet, N. T. O., Dzulkarnain, B. Z., & Bhajan, L. (2023). Application of machine learning models to predict ecotoxicity of ionic liquids (Vibrio fischeri) using VolSurf principal properties. In Y. Yin Fong, L. Man Kee, N. Jusoh, & C. Y. Chuah (Eds.), Sustainable Processes and Clean Energy Transition- International Conference on Sustainable Processes and Clean Energy Transition, ICSuPCET 2022 (pp. 234-247). (Materials Research Proceedings; Vol. 29). Association of American Publishers. https://doi.org/10.21741/9781644902516-27

Grace, Amabel Tabaaza ; Bennet, Nii Tackie Otoo ; Dzulkarnain, B. Zaini et al. / Application of machine learning models to predict ecotoxicity of ionic liquids (Vibrio fischeri) using VolSurf principal properties. Sustainable Processes and Clean Energy Transition- International Conference on Sustainable Processes and Clean Energy Transition, ICSuPCET 2022. editor / Yeong Yin Fong ; Lam Man Kee ; Norwahyu Jusoh ; Chong Yang Chuah. Association of American Publishers, 2023. pp. 234-247 (Materials Research Proceedings).

@inproceedings{25645e90cb1942e197e0268de8156de9,

title = "Application of machine learning models to predict ecotoxicity of ionic liquids (Vibrio fischeri) using VolSurf principal properties",

abstract = "Owing to the rapid growth in IL synthesis due to feasible cation-anion combinations, knowledge of their toxicity is pertinent for their successful application. Toxicity information measurement of various ILs on a broad spectrum of conditions through experimental techniques is way demanding on time, resources, and is at times impractical. Various research works have been performed in Quantitative Structure Activity/Property Relationship (QSAR/QSPR) for IL toxicity prediction. In this study, ML models have been trained and tested on Vibrio fischeri toxicity data set using in silico principal properties (PPs) as descriptors. Deploying this properties aid in considering both the effect of cations and anions on Vibrio fischeri toxicity prediction. Among the models trained, the Random Forest model proved to be the most precise nevertheless, decision tree model was the most accurate and consistent. Considering the importance of the descriptors to Vibrio fischeri toxicity selection techniques and model optimization.",

keywords = "Ionic liquids, Machine learning, QSPR/QSAR and Principal Properties, Toxicity, Vibrio Fischeri",

author = "Grace, \{Amabel Tabaaza\} and Bennet, \{Nii Tackie Otoo\} and Dzulkarnain, \{B. Zaini\} and Lal Bhajan",

note = "Publisher Copyright: {\textcopyright} 2023, Association of American Publishers. All rights reserved.; International Conference on Sustainable Processes and Clean Energy Transition, ICSuPCET 2022 ; Conference date: 01-12-2022 Through 02-12-2022",

year = "2023",

doi = "10.21741/9781644902516-27",

language = "English",

isbn = "9781644902509",

series = "Materials Research Proceedings",

publisher = "Association of American Publishers",

pages = "234--247",

editor = "\{Yin Fong\}, Yeong and \{Man Kee\}, Lam and Norwahyu Jusoh and Chuah, \{Chong Yang\}",

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}

Grace, AT, Bennet, NTO, Dzulkarnain, BZ & Bhajan, L 2023, Application of machine learning models to predict ecotoxicity of ionic liquids (Vibrio fischeri) using VolSurf principal properties. in Y Yin Fong, L Man Kee, N Jusoh & CY Chuah (eds), Sustainable Processes and Clean Energy Transition- International Conference on Sustainable Processes and Clean Energy Transition, ICSuPCET 2022. Materials Research Proceedings, vol. 29, Association of American Publishers, pp. 234-247, International Conference on Sustainable Processes and Clean Energy Transition, ICSuPCET 2022, Kuching, Malaysia, 1/12/22. https://doi.org/10.21741/9781644902516-27

Application of machine learning models to predict ecotoxicity of ionic liquids (Vibrio fischeri) using VolSurf principal properties. / Grace, Amabel Tabaaza; Bennet, Nii Tackie Otoo; Dzulkarnain, B. Zaini et al.
Sustainable Processes and Clean Energy Transition- International Conference on Sustainable Processes and Clean Energy Transition, ICSuPCET 2022. ed. / Yeong Yin Fong; Lam Man Kee; Norwahyu Jusoh; Chong Yang Chuah. Association of American Publishers, 2023. p. 234-247 (Materials Research Proceedings; Vol. 29).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Application of machine learning models to predict ecotoxicity of ionic liquids (Vibrio fischeri) using VolSurf principal properties

AU - Grace, Amabel Tabaaza

AU - Bennet, Nii Tackie Otoo

AU - Dzulkarnain, B. Zaini

AU - Bhajan, Lal

PY - 2023

Y1 - 2023

N2 - Owing to the rapid growth in IL synthesis due to feasible cation-anion combinations, knowledge of their toxicity is pertinent for their successful application. Toxicity information measurement of various ILs on a broad spectrum of conditions through experimental techniques is way demanding on time, resources, and is at times impractical. Various research works have been performed in Quantitative Structure Activity/Property Relationship (QSAR/QSPR) for IL toxicity prediction. In this study, ML models have been trained and tested on Vibrio fischeri toxicity data set using in silico principal properties (PPs) as descriptors. Deploying this properties aid in considering both the effect of cations and anions on Vibrio fischeri toxicity prediction. Among the models trained, the Random Forest model proved to be the most precise nevertheless, decision tree model was the most accurate and consistent. Considering the importance of the descriptors to Vibrio fischeri toxicity selection techniques and model optimization.

AB - Owing to the rapid growth in IL synthesis due to feasible cation-anion combinations, knowledge of their toxicity is pertinent for their successful application. Toxicity information measurement of various ILs on a broad spectrum of conditions through experimental techniques is way demanding on time, resources, and is at times impractical. Various research works have been performed in Quantitative Structure Activity/Property Relationship (QSAR/QSPR) for IL toxicity prediction. In this study, ML models have been trained and tested on Vibrio fischeri toxicity data set using in silico principal properties (PPs) as descriptors. Deploying this properties aid in considering both the effect of cations and anions on Vibrio fischeri toxicity prediction. Among the models trained, the Random Forest model proved to be the most precise nevertheless, decision tree model was the most accurate and consistent. Considering the importance of the descriptors to Vibrio fischeri toxicity selection techniques and model optimization.

KW - Ionic liquids

KW - Machine learning

KW - QSPR/QSAR and Principal Properties

KW - Toxicity

KW - Vibrio Fischeri

UR - https://www.scopus.com/pages/publications/85161639763

U2 - 10.21741/9781644902516-27

DO - 10.21741/9781644902516-27

M3 - Conference contribution

AN - SCOPUS:85161639763

SN - 9781644902509

T3 - Materials Research Proceedings

SP - 234

EP - 247

BT - Sustainable Processes and Clean Energy Transition- International Conference on Sustainable Processes and Clean Energy Transition, ICSuPCET 2022

A2 - Yin Fong, Yeong

A2 - Man Kee, Lam

A2 - Jusoh, Norwahyu

A2 - Chuah, Chong Yang

PB - Association of American Publishers

T2 - International Conference on Sustainable Processes and Clean Energy Transition, ICSuPCET 2022

Y2 - 1 December 2022 through 2 December 2022

ER -

Grace AT, Bennet NTO, Dzulkarnain BZ, Bhajan L. Application of machine learning models to predict ecotoxicity of ionic liquids (Vibrio fischeri) using VolSurf principal properties. In Yin Fong Y, Man Kee L, Jusoh N, Chuah CY, editors, Sustainable Processes and Clean Energy Transition- International Conference on Sustainable Processes and Clean Energy Transition, ICSuPCET 2022. Association of American Publishers. 2023. p. 234-247. (Materials Research Proceedings). doi: 10.21741/9781644902516-27

Application of machine learning models to predict ecotoxicity of ionic liquids (Vibrio fischeri) using VolSurf principal properties

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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