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Volume 17
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10.3390/toxins17040171
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Article

Advancing Enzyme-Based Detoxification Prediction with ToxZyme: An Ensemble Machine Learning Approach

by
Kashif Iqbal Sahibzada
1,2,
Shumaila Shahid
3,
Mohsina Akhter
4,
Muhammad Faisal
5,6,
Reham A. Abd El Rahman
7,
Muhammad Imran
1,
Yangyong Lv
1,
Dongqing Wei
8,9,10,* and
Yuansen Hu
1,*
1
College of Biological Engineering, Henan University of Technology, Zhengzhou 450001, China
2
Department of Health Professional Technologies, Faculty of Allied Health Sciences, The University of Lahore, Lahore 54570, Pakistan
3
School of Biochemistry and Biotechnology, University of the Punjab, Lahore 54570, Pakistan
4
School of Biological Sciences, University of the Punjab, Lahore 54570, Pakistan
5
Chemical Engineering, School for Engineering of Matter, Transport and Energy (SEMTE), Arizona State University, Tempe, AZ 85281, USA
6
University Institute of Biochemistry and Biotechnology, PMAS-Arid Agriculture University Rawalpindi, Rawalpindi 46000, Pakistan
7
Department of Clinical Laboratory Science, College of Applied Medical Sciences, University of Hafer Al Batin UHB, Hafer Al Batin 39524, Saudi Arabia
8
State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, China
9
Qihe Laboratory, Qishui Guang East, Qibin District, Hebi 458030, China
10
Zhongjing Research and Industrialization Institute of Chinese Medicine, Zhongguancun Scientific Park, Meixi, Nanyang 473006, China
*
Authors to whom correspondence should be addressed.
Toxins 2025, 17(4), 171; https://doi.org/10.3390/toxins17040171
Submission received: 17 February 2025 / Revised: 20 March 2025 / Accepted: 28 March 2025 / Published: 1 April 2025
(This article belongs to the Special Issue Mycotoxins in Food Chain: Occurrence, Analysis and Risk Management)
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Abstract

The aaccurate prediction of enzymes with environment detoxification functions is crucial, not only to achieve a better understanding of bioremediation strategies, but also to alleviate environmental pollution. In the present study, a novel machine learning model was introduced which classifies enzymes by their toxin degradation ability. In this model, two different sets of data were used which include enzymes that can catalyze the toxin degradation as a positive dataset and non-toxin-degrading enzymes as a negative dataset. Further, a comparison of multiple classifiers was performed to find the best model and a Random Forest (RF) classifier was selected due to its strong performance. To enhance the accuracy, we combined RF with a Deep Neural Network (DNN), forming an ensemble model which effectively integrated both techniques. This combination achieved 95% precision, surpassing individual models. Our ensemble model not only ensures high prediction accuracy but also reliably differentiates toxin-degrading enzymes from non-degrading ones. This study highlights the power of combining classical machine learning with deep learning to advance prediction. Our model represents a significant step in enzyme classification and serves as a valuable resource for environmental biotechnology, food nutrition, and health applications.
Keywords: toxins; machine learning; bioremediation; deep neural network; random forest toxins; machine learning; bioremediation; deep neural network; random forest
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MDPI and ACS Style

Sahibzada, K.I.; Shahid, S.; Akhter, M.; Faisal, M.; Abd El Rahman, R.A.; Imran, M.; Lv, Y.; Wei, D.; Hu, Y. Advancing Enzyme-Based Detoxification Prediction with ToxZyme: An Ensemble Machine Learning Approach. Toxins 2025, 17, 171. https://doi.org/10.3390/toxins17040171

AMA Style

Sahibzada KI, Shahid S, Akhter M, Faisal M, Abd El Rahman RA, Imran M, Lv Y, Wei D, Hu Y. Advancing Enzyme-Based Detoxification Prediction with ToxZyme: An Ensemble Machine Learning Approach. Toxins. 2025; 17(4):171. https://doi.org/10.3390/toxins17040171

Chicago/Turabian Style

Sahibzada, Kashif Iqbal, Shumaila Shahid, Mohsina Akhter, Muhammad Faisal, Reham A. Abd El Rahman, Muhammad Imran, Yangyong Lv, Dongqing Wei, and Yuansen Hu. 2025. "Advancing Enzyme-Based Detoxification Prediction with ToxZyme: An Ensemble Machine Learning Approach" Toxins 17, no. 4: 171. https://doi.org/10.3390/toxins17040171

APA Style

Sahibzada, K. I., Shahid, S., Akhter, M., Faisal, M., Abd El Rahman, R. A., Imran, M., Lv, Y., Wei, D., & Hu, Y. (2025). Advancing Enzyme-Based Detoxification Prediction with ToxZyme: An Ensemble Machine Learning Approach. Toxins, 17(4), 171. https://doi.org/10.3390/toxins17040171

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