Machine Learning Models for Predicting Nanoparticle Behavior

Authors

  • Irshad Hussain Professor, Department of Chemistry and Chemical Engineering Lahore University of Management Sciences (LUMS), Lahore, Pakistan
  • Yasmeen Akhtar Assistant Professor, Department of Computer Science University of the Punjab, Lahore, Pakistan
  • Shabbir Ahmad Associate Professor, Department of Electrical Engineering University of Engineering and Technology (UET), Lahore, Pakistan

Keywords:

Machine Learning, Nanoparticles, Drug Delivery, Toxicity Prediction, Nanomedicine, Random Forest, Support Vector Machines, Neural Networks

Abstract

The integration of machine learning (ML) models in predicting nanoparticle (NP) behavior has revolutionized the field of nanomedicine, offering enhanced precision in drug delivery, toxicity assessment, and therapeutic efficacy. This study explores various ML algorithms, including Random Forest, Support Vector Machines, and Neural Networks, to model NP interactions within biological systems. By analyzing physicochemical properties such as size, surface charge, and morphology, the research aims to establish predictive frameworks that can guide the design of safer and more effective nanotherapies. The findings underscore the potential of ML in bridging the gap between experimental limitations and the complex dynamics of NP behavior in vivo.

Downloads

Published

2024-06-30

Issue

Jan-Jun 2024

Section

Articles

License

Copyright (c) 2024 International journal of advanced sciences and computing

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

All articles published in the International Journal of Advanced Sciences and Computing (IJASC) are open access and licensed under the Creative Commons Attribution (CC BY 4.0) license. Authors retain copyright, and users may read, share, reuse, and adapt the content with proper attribution.