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.

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

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.

@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}
}

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  -