Inproceedings
Book title :
2023 International Conference on Networking and Advanced Systems (ICNAS)
Address :
Skikda, Algeria
Publisher :
Information
Period : October 2023
Pages : 1-7
Details
Evaluating the Effectiveness of Machine Learning Models for Classifying Chemical Inhibitors: A Case Study of Aromatase Inhibitors and Pubchem's Molecular Fingerprint Descriptors
Ali Abdelkrim Abdelkrim Bouramoul Imene Zenbout Said Brahimi
In place of traditional experimentation, modern drug development increasingly makes use of machine learning approaches, particularly for making predictions based on the chemical structure of components. As a case study, Aramotase is a catalyst that has been linked to hormonal abnormalities, which in turn can have negative effects on sexual and skeletal maturation. Whereas, it is a major obstacle to create molecules with the aromatase enzyme. Using the ChEMBL database, which at the outset had 2818 compounds of aromatase inhibitors, we offer here a large-scale categorization structure-activity connection analysis. For further investigation, the Ic50 have been chosen as the bioactivity unit. The data curation process resulted in a final dataset consisting of 2071 compounds. We used a methodology to test the performance of different classifiers built using Pubchem's molecular fingerprint descriptors to solve the problem at hand, namely, Naive Bayes, Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Multilayer Perceptron, and Ad-aBoost. Considering the model's quality metrics, the Multilayer perceptron was considered the most effective model with 80 % for both accuracy and sensitivity for 10-fold cross-validation and 75% and 81 %, respectively, on the test set, and also an Fl-measure of 75%.
Key words :
Logistic regression Sensitivity Inhibitors Biological system modeling Buildings Fingerprint recognition Multilayer perceptrons Machine Learning Aromatase Breast Cancer QSAR Pubchem Fingerprints
Ref. laboratory citation :
misc-lab-429
DOI :
10.1109/ICNAS59892.2023.10330448
Link :
Texte intégral
ACM :
A. Abdelkrim, A. Bouramoul, I. Zenbout and S. Brahimi. 2023. Evaluating the Effectiveness of Machine Learning Models for Classifying Chemical Inhibitors: A Case Study of Aromatase Inhibitors and Pubchem's Molecular Fingerprint Descriptors. In Proceedings of the 2023 International Conference on Networking and Advanced Systems (ICNAS), Skikda, Algeria (October 2023), IEEE, 1-7. DOI: https://doi.org/10.1109/ICNAS59892.2023.10330448.
APA :
Abdelkrim, A., Bouramoul, A., Zenbout, I. & Brahimi, S. (2023, October). Evaluating the Effectiveness of Machine Learning Models for Classifying Chemical Inhibitors: A Case Study of Aromatase Inhibitors and Pubchem's Molecular Fingerprint Descriptors. In Proceedings of the 2023 International Conference on Networking and Advanced Systems (ICNAS), Skikda, Algeria, IEEE, 1-7. DOI: https://doi.org/10.1109/ICNAS59892.2023.10330448
IEEE :
A. Abdelkrim, A. Bouramoul, I. Zenbout and S. Brahimi, "Evaluating the Effectiveness of Machine Learning Models for Classifying Chemical Inhibitors: A Case Study of Aromatase Inhibitors and Pubchem's Molecular Fingerprint Descriptors". In Proceedings of the 2023 International Conference on Networking and Advanced Systems (ICNAS), Skikda, Algeria, IEEE, pp. 1-7, October, 2023. DOI: https://doi.org/10.1109/ICNAS59892.2023.10330448.
BibTeX :
@inproceedings{misc-lab-429,
author = {Abdelkrim, Ali and Bouramoul, Abdelkrim and Zenbout, Imene and Brahimi, Said},
title = {Evaluating the Effectiveness of Machine Learning Models for Classifying Chemical Inhibitors: A Case Study of Aromatase Inhibitors and Pubchem's Molecular Fingerprint Descriptors},
booktitle = {2023 International Conference on Networking and Advanced Systems (ICNAS)},
location = {Skikda, Algeria},
pages = {1--7},
publisher = {IEEE},
year = {2023},
month = {October},
doi = {10.1109/ICNAS59892.2023.10330448},
url = {https://ieeexplore.ieee.org/document/10330448},
keywords = {Logistic regression, Sensitivity, Inhibitors, Biological system modeling, Buildings, Fingerprint recognition, Multilayer perceptrons, Machine Learning, Aromatase, Breast Cancer, QSAR, Pubchem Fingerprints}
}
RIS :
TY  - CONF
TI - Evaluating the Effectiveness of Machine Learning Models for Classifying Chemical Inhibitors: A Case Study of Aromatase Inhibitors and Pubchem's Molecular Fingerprint Descriptors
AU - A. Abdelkrim
AU - A. Bouramoul
AU - I. Zenbout
AU - S. Brahimi
PY - 2023
BT - 2023 International Conference on Networking and Advanced Systems (ICNAS), Skikda, Algeria
SP - 1
EP - 7
PB - IEEE
AB - In place of traditional experimentation, modern drug development increasingly makes use of machine learning approaches, particularly for making predictions based on the chemical structure of components. As a case study, Aramotase is a catalyst that has been linked to hormonal abnormalities, which in turn can have negative effects on sexual and skeletal maturation. Whereas, it is a major obstacle to create molecules with the aromatase enzyme. Using the ChEMBL database, which at the outset had 2818 compounds of aromatase inhibitors, we offer here a large-scale categorization structure-activity connection analysis. For further investigation, the Ic50 have been chosen as the bioactivity unit. The data curation process resulted in a final dataset consisting of 2071 compounds. We used a methodology to test the performance of different classifiers built using Pubchem's molecular fingerprint descriptors to solve the problem at hand, namely, Naive Bayes, Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Multilayer Perceptron, and Ad-aBoost. Considering the model's quality metrics, the Multilayer perceptron was considered the most effective model with 80 % for both accuracy and sensitivity for 10-fold cross-validation and 75% and 81 %, respectively, on the test set, and also an Fl-measure of 75%.
KW - Logistic regression
KW - Sensitivity
KW - Inhibitors
KW - Biological system modeling
KW - Buildings
KW - Fingerprint recognition
KW - Multilayer perceptrons
KW - Machine Learning
KW - Aromatase
KW - Breast Cancer
KW - QSAR
KW - Pubchem Fingerprints
DO - 10.1109/ICNAS59892.2023.10330448
UR - https://ieeexplore.ieee.org/document/10330448
ID - misc-lab-429
ER -