Anti-breast Cancer Drug Design and ADMET Prediction of ERa Antagonists Based on QSAR Study

Wentao Gao; Ziyi Huang; Hao Zhang; Jianfeng Lu

doi:10.1007/978-3-031-13829-4_3

Anti-breast Cancer Drug Design and ADMET Prediction of ERa Antagonists Based on QSAR Study

Wentao Gao, Ziyi Huang, Hao Zhang^*, Jianfeng Lu

^*Corresponding author for this work

Department of Electrical Engineering

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

The development of breast cancer is closely related to ERα gene, which has been identified as an important target for the treatment of breast cancer. The establishment of effective Quantitative structure-activity relationship model (QSAR) of compounds can predict the biological activity of new compounds well and provide help for the research and development of anti-breast cancer drugs. However, it is not enough to screen potential compounds only depending on biological activity. ADMET properties of drugs also need to be considered. In this paper, based on the existing data set, we perform hierarchical clustering on 729 variables, and calculate the Pearson correlation coefficient between them and the pIC₅₀ value of biological activity, and screen out five variables that have a significant impact on biological activity. Perform multiple linear regression on these five molecular descriptors and the biological activity values, and then use the multiple stepwise regression method to optimize to establish a QSAR model. Furthermore, Fisher discriminant analysis is used to classify and predict the ADMET properties of the new compounds. Both models have good statistical parameters and reliable prediction ability. As a result, we come to a conclusion that Oc1ccc(cc1)C2 = C(c3ccc(C = O)cc3)c4ccc(F)cc4OCC2 and other compounds not only have high biological activity, but also have great ADMET properties, which could be used as potential anti-breast cancer drug compounds. These results provide a certain theoretical basis for the development and validation of new anti-breast cancer drugs in the future.

Original language	English
Title of host publication	Intelligent Computing Theories and Application
Subtitle of host publication	18th International Conference, ICIC 2022, Proceedings, Part II
Editors	De-Shuang Huang, Kang-Hyun Jo, Junfeng Jing, Prashan Premaratne, Vitoantonio Bevilacqua, Abir Hussain
Publisher	Springer, Cham
Pages	28-40
Edition	1
ISBN (Electronic)	978-3-031-13829-4
ISBN (Print)	978-3-031-13828-7
DOIs	https://doi.org/10.1007/978-3-031-13829-4_3
Publication status	Published - 2022
Event	18th International Conference on Intelligent Computing, ICIC 2022 - Xi'an, China Duration: 7 Aug 2022 → 11 Aug 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13394 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	18th International Conference on Intelligent Computing, ICIC 2022
Place	China
City	Xi'an
Period	7/08/22 → 11/08/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Research Keywords

ADMET properties
Fisher discriminant
Pearson correlation coefficient
QSAR

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10.1007/978-3-031-13829-4_3

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Gao, W., Huang, Z., Zhang, H., & Lu, J. (2022). Anti-breast Cancer Drug Design and ADMET Prediction of ERa Antagonists Based on QSAR Study. In D.-S. Huang, K.-H. Jo, J. Jing, P. Premaratne, V. Bevilacqua, & A. Hussain (Eds.), Intelligent Computing Theories and Application: 18th International Conference, ICIC 2022, Proceedings, Part II (1 ed., pp. 28-40). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13394 LNCS). Springer, Cham. https://doi.org/10.1007/978-3-031-13829-4_3

Gao, Wentao ; Huang, Ziyi ; Zhang, Hao et al. / Anti-breast Cancer Drug Design and ADMET Prediction of ERa Antagonists Based on QSAR Study. Intelligent Computing Theories and Application: 18th International Conference, ICIC 2022, Proceedings, Part II. editor / De-Shuang Huang ; Kang-Hyun Jo ; Junfeng Jing ; Prashan Premaratne ; Vitoantonio Bevilacqua ; Abir Hussain. 1. ed. Springer, Cham, 2022. pp. 28-40 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Anti-breast Cancer Drug Design and ADMET Prediction of ERa Antagonists Based on QSAR Study",

abstract = "The development of breast cancer is closely related to ERα gene, which has been identified as an important target for the treatment of breast cancer. The establishment of effective Quantitative structure-activity relationship model (QSAR) of compounds can predict the biological activity of new compounds well and provide help for the research and development of anti-breast cancer drugs. However, it is not enough to screen potential compounds only depending on biological activity. ADMET properties of drugs also need to be considered. In this paper, based on the existing data set, we perform hierarchical clustering on 729 variables, and calculate the Pearson correlation coefficient between them and the pIC50 value of biological activity, and screen out five variables that have a significant impact on biological activity. Perform multiple linear regression on these five molecular descriptors and the biological activity values, and then use the multiple stepwise regression method to optimize to establish a QSAR model. Furthermore, Fisher discriminant analysis is used to classify and predict the ADMET properties of the new compounds. Both models have good statistical parameters and reliable prediction ability. As a result, we come to a conclusion that Oc1ccc(cc1)C2 = C(c3ccc(C = O)cc3)c4ccc(F)cc4OCC2 and other compounds not only have high biological activity, but also have great ADMET properties, which could be used as potential anti-breast cancer drug compounds. These results provide a certain theoretical basis for the development and validation of new anti-breast cancer drugs in the future.",

keywords = "ADMET properties, Fisher discriminant, Pearson correlation coefficient, QSAR",

author = "Wentao Gao and Ziyi Huang and Hao Zhang and Jianfeng Lu",

year = "2022",

doi = "10.1007/978-3-031-13829-4_3",

language = "English",

isbn = "978-3-031-13828-7",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer, Cham",

pages = "28--40",

editor = "De-Shuang Huang and Kang-Hyun Jo and Junfeng Jing and Prashan Premaratne and Vitoantonio Bevilacqua and Abir Hussain",

booktitle = "Intelligent Computing Theories and Application",

edition = "1",

note = "18th International Conference on Intelligent Computing, ICIC 2022 ; Conference date: 07-08-2022 Through 11-08-2022",

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Gao, W, Huang, Z, Zhang, H & Lu, J 2022, Anti-breast Cancer Drug Design and ADMET Prediction of ERa Antagonists Based on QSAR Study. in D-S Huang, K-H Jo, J Jing, P Premaratne, V Bevilacqua & A Hussain (eds), Intelligent Computing Theories and Application: 18th International Conference, ICIC 2022, Proceedings, Part II. 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13394 LNCS, Springer, Cham, pp. 28-40, 18th International Conference on Intelligent Computing, ICIC 2022, Xi'an, China, 7/08/22. https://doi.org/10.1007/978-3-031-13829-4_3

Anti-breast Cancer Drug Design and ADMET Prediction of ERa Antagonists Based on QSAR Study. / Gao, Wentao; Huang, Ziyi; Zhang, Hao et al.
Intelligent Computing Theories and Application: 18th International Conference, ICIC 2022, Proceedings, Part II. ed. / De-Shuang Huang; Kang-Hyun Jo; Junfeng Jing; Prashan Premaratne; Vitoantonio Bevilacqua; Abir Hussain. 1. ed. Springer, Cham, 2022. p. 28-40 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13394 LNCS).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Anti-breast Cancer Drug Design and ADMET Prediction of ERa Antagonists Based on QSAR Study

AU - Gao, Wentao

AU - Huang, Ziyi

AU - Zhang, Hao

AU - Lu, Jianfeng

PY - 2022

Y1 - 2022

N2 - The development of breast cancer is closely related to ERα gene, which has been identified as an important target for the treatment of breast cancer. The establishment of effective Quantitative structure-activity relationship model (QSAR) of compounds can predict the biological activity of new compounds well and provide help for the research and development of anti-breast cancer drugs. However, it is not enough to screen potential compounds only depending on biological activity. ADMET properties of drugs also need to be considered. In this paper, based on the existing data set, we perform hierarchical clustering on 729 variables, and calculate the Pearson correlation coefficient between them and the pIC50 value of biological activity, and screen out five variables that have a significant impact on biological activity. Perform multiple linear regression on these five molecular descriptors and the biological activity values, and then use the multiple stepwise regression method to optimize to establish a QSAR model. Furthermore, Fisher discriminant analysis is used to classify and predict the ADMET properties of the new compounds. Both models have good statistical parameters and reliable prediction ability. As a result, we come to a conclusion that Oc1ccc(cc1)C2 = C(c3ccc(C = O)cc3)c4ccc(F)cc4OCC2 and other compounds not only have high biological activity, but also have great ADMET properties, which could be used as potential anti-breast cancer drug compounds. These results provide a certain theoretical basis for the development and validation of new anti-breast cancer drugs in the future.

AB - The development of breast cancer is closely related to ERα gene, which has been identified as an important target for the treatment of breast cancer. The establishment of effective Quantitative structure-activity relationship model (QSAR) of compounds can predict the biological activity of new compounds well and provide help for the research and development of anti-breast cancer drugs. However, it is not enough to screen potential compounds only depending on biological activity. ADMET properties of drugs also need to be considered. In this paper, based on the existing data set, we perform hierarchical clustering on 729 variables, and calculate the Pearson correlation coefficient between them and the pIC50 value of biological activity, and screen out five variables that have a significant impact on biological activity. Perform multiple linear regression on these five molecular descriptors and the biological activity values, and then use the multiple stepwise regression method to optimize to establish a QSAR model. Furthermore, Fisher discriminant analysis is used to classify and predict the ADMET properties of the new compounds. Both models have good statistical parameters and reliable prediction ability. As a result, we come to a conclusion that Oc1ccc(cc1)C2 = C(c3ccc(C = O)cc3)c4ccc(F)cc4OCC2 and other compounds not only have high biological activity, but also have great ADMET properties, which could be used as potential anti-breast cancer drug compounds. These results provide a certain theoretical basis for the development and validation of new anti-breast cancer drugs in the future.

KW - ADMET properties

KW - Fisher discriminant

KW - Pearson correlation coefficient

KW - QSAR

UR - http://www.scopus.com/inward/record.url?scp=85139820198&partnerID=8YFLogxK

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85139820198&origin=recordpage

U2 - 10.1007/978-3-031-13829-4_3

DO - 10.1007/978-3-031-13829-4_3

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 978-3-031-13828-7

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 28

EP - 40

BT - Intelligent Computing Theories and Application

A2 - Huang, De-Shuang

A2 - Jo, Kang-Hyun

A2 - Jing, Junfeng

A2 - Premaratne, Prashan

A2 - Bevilacqua, Vitoantonio

A2 - Hussain, Abir

PB - Springer, Cham

T2 - 18th International Conference on Intelligent Computing, ICIC 2022

Y2 - 7 August 2022 through 11 August 2022

ER -

Gao W, Huang Z, Zhang H, Lu J. Anti-breast Cancer Drug Design and ADMET Prediction of ERa Antagonists Based on QSAR Study. In Huang DS, Jo KH, Jing J, Premaratne P, Bevilacqua V, Hussain A, editors, Intelligent Computing Theories and Application: 18th International Conference, ICIC 2022, Proceedings, Part II. 1 ed. Springer, Cham. 2022. p. 28-40. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Epub 2022 Aug 15. doi: 10.1007/978-3-031-13829-4_3

Anti-breast Cancer Drug Design and ADMET Prediction of ERa Antagonists Based on QSAR Study

Abstract

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UN SDGs

Research Keywords

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