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  • Artificial Neural Network In Pharmaceutical And Cosmeceutical Research
  • Photo Rajput Anamika* Photo Arya Divya Photo Panshul Chauhan Photo Atul Dixit Photo Debaprasad Ghosh Photo Ashu Mittal

  • 1,2Department of Pharmaceutics, Delhi Institute of Pharmaceutical Sciences and research university, Pushp Vihar New Delhi 11OO35
    3Integrated Academy of Management and Technology, Ghaziabad 201015
    4Seedlings India  pvt. Ltd. B-8, Site-B, UPSIDC, Surajpur Industrial Area Greater Noida, Gautam Budh Nagar 201306 (U.P)
    5,6Department of Pharmacognosy, KIET School of Pharmacy, KIET Group of Institutions, Delhi-NCR, Ghaziabad-201206, Uttar Pradesh, India
     

Abstract

The presented collection of studies highlights the diverse applications of Artificial Neural Networks (ANNs) in pharmaceuticals and cosmeceuticals. Various researchers employ ANNs to optimize pharmaceutical formulations, predict drug release, and explore drug-target interactions. The studies demonstrate ANNs' superiority in handling complex relationships and learning from data patterns, offering enhanced accuracy in optimization and prediction tasks. Applications range from predicting skin permeability and toxicity to formulating stable oil-in-water emulsions and optimizing liposome size. ANNs prove valuable in drug discovery, providing insights into chemogenomic space and identifying potential new targets. The review emphasizes the growing significance of ANNs in revolutionizing approaches to pharmaceutical and cosmetic research. In this there is a discussion on the integration of computer science with theoretical bases, specifically nonlinear dynamics and chaos theory, to create intelligent agents such as artificial neural networks (ANNs). The goal is to address problems of high complexity by allowing these networks to adapt dynamically. The integration of computer science with theoretical bases enables the development of intelligent agents, with ANNs being highlighted as an example. ANNs are described as capable of adapting dynamically to complex problems. ANNs are noted for their ability to reproduce the dynamic interaction of multiple factors simultaneously. This characteristic is beneficial for studying complexity in various contexts. Integrating computer science principles, nonlinear dynamics, and chaos theory with artificial neural networks provides a powerful toolset for studying complex systems, inside the body. The adaptability and capacity for dynamic interaction modeling make ANNs particularly well-suited for addressing challenges associated with high complexity and individualized healthcare. Artificial neural networks can also be used as computational tools with significant potential for analyzing cosmological data. It emphasizes their applications in modelling data, saving computational time, and classifying objects, highlighting the qualities that make ANNs a promising alternative for data analysis in cosmology.

Keywords

artificial neural networks, cosmology, healthcare, dynamic interaction

Reference

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Photo
Rajput Anamika
Corresponding author

Department of Pharmaceutics, Delhi Institute of Pharmaceutical Sciences and research university, Pushp Vihar New Delhi 11OO35

Photo
Arya Divya
Co-author

Department of Pharmaceutics, Delhi Institute of Pharmaceutical Sciences and research university, Pushp Vihar New Delhi 11OO35

Photo
Panshul Chauhan
Co-author

Integrated Academy of Management and Technology, Ghaziabad 201015

Photo
Atul Dixit
Co-author

Seedlings India pvt. Ltd. B-8, Site-B, UPSIDC, Surajpur Industrial Area Greater Noida, Gautam Budh Nagar 201306 (U.P)

Photo
Debaprasad Ghosh
Co-author

Department of Pharmacognosy, KIET School of Pharmacy, KIET Group of Institutions, Delhi-NCR, Ghaziabad-201206, Uttar Pradesh, India

Photo
Ashu Mittal
Co-author

Department of Pharmacognosy, KIET School of Pharmacy, KIET Group of Institutions, Delhi-NCR, Ghaziabad-201206, Uttar Pradesh, India

Rajput Anamika*, Arya Divya, Panshul Chauhan, Atul Dixit, Debaprasad Ghosh, Ashu Mittal, Artificial Neural Network In Pharmaceuticals And Cosmeticeuticals Research, Int. J. of Pharm. Sci., 2024, Vol 2, Issue 1, 410-443. https://doi.org/10.5281/zenodo.10531989

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