Alternative cancer therapy through modeling pteridines photosensitizer quantum yield singlet oxygen production using swarm-based support vector regression and extreme learning machine

Abstract

Photodynamic cancer therapy circumvents the major side effects associated with the conventional cancer treatment methods, such as chemotherapy, surgery and exposure to radiation. Experimental measurement of photosensitizer quantum yield (PQY) singlet production of oxygen through either sensitive laser spectroscopy or luminescence detection at the wavelength of 1270 nm is costly; time consuming and intensive while unreliability of chemical traps experimental approach is of serious concern. Quantitative structure–activity relationship (QSAR) computational method proposed in the literature for computing PQY of singlet oxygen production has characteristics deviation from the measured values. PQY singlet oxygen production of twenty-nine pteridines photosensitizer compounds is modeled and predicted in this present contribution using extreme learning machine (ELM) and support vector regression (SVR) with hybridized particle swarm optimization (PSO) method for ensuring combinatory parameter selection. The performances of the developed SVR-PSO computational method are assessed using mean absolute error (MAE), correlation coefficient (CC), root mean square error (RMSE) and mean absolute percentage deviation (MAPD). The developed SVR-PSO model outperforms QSAR (2016) model with performance superiority of 34.78%, 3.65%, 17.64% and 42.16% on the basis of RMSE, CC, MAE and MAPD performance measuring parameters, respectively. The developed ELM-SINE (with sine activation function) and ELM-SIG (with sigmoid activation function) respectively outperform the existing QSAR (2016) model with improvement of 6.54% and 4.70% using R-squared metric. The demonstrated outstanding performance of the present predictive models is immensely meritorious in strengthening the potentials of alternative cancer therapy and circumventing the experimental challenges of PQY singlet oxygen production determination.

Keywords:

• Photosensitizer
• support vector regression
• pteridines
• singlet oxygen
• particle swarm optimization
• extreme learning machine

REVIEWING EDITOR:

• Jones Ian Philip PhD, Senior Editor, Imam Abdulrahman Bin Faisal University, Computer Science, Saudi Arabia
• The University of Birmingham, Metallurgy & Materials, Elms Rd, Birmingham, UNITED KINGDOM. N/A B15 2TT

SUBJECTS:

• Science
• Physical Sciences
• Material Science
• Technology
• Computer Science
• Artificial Intelligence
• Machine Learning – Design

Disclosure statement

Author has no competing interest.

Data availability statement

The raw data needed to reproduce these findings are cited in Section 3.1 of the manuscript.

Additional information

Notes on contributors

Nahier Aldhafferi

Dr. Nahier Aldhafferi is an associate professor at the Department of Compteure Information Systems, College of Computer Science and Information Technology, Imam Abdurrahman Bin Faisal University in Saudi Arabia. He received his BS in 2005 from Dammam Teachers College, Saudi Arabia. He also received his master’s degree in 2009 in Internet Technology from Wollongong University, Australia. Currently, he has a PhD in Information Technology from New England University, Australia. He has published alot of research in Data Science and Information Privacy and Artificial Intelligence. His current research interests are in the fields of Data Science, Data Mining, E-gov Services Integration, Privacy, Machine Learning, and Software Engineering. He can be contaced at email: [email protected]