AI-integrated Responsive Polymer Composites for Controlled Drug Delivery

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 14 | 04 | Page :
By

A.R. Shinde,

Chandrashekhar Ramesh Ramtirthkar,

Valli Nachiyar C,

Tanveer Ahmad Wani,

Akella Yeswanth,

Swati Sucharita,

  1. Associate Professor, Department of Pharmacology, Krishna Institute of Science and Technology, Krishna Vishwa Vidyapeeth “Deemed to be University”, Karad, Satara, Maharashtra, India
  2. Associate Professor, Department of Mechanical Engineering, Vishwakarma Institute of Technology, Pune, Maharashtra, India
  3. Associate Professor, Department of Research, Meenakshi Academy of Higher Education and Research, Chennai, Tamil Nadu, India
  4. Associate Professor, Department of Physics, Noida international University, Greater Noida, Uttar Pradesh, India
  5. Associate Professor, Department of Mechanical Engineering, Pragati Engineering College, Kakinada District, Andhra Pradesh, India
  6. Associate Professor, Department of Computer Science and Engineering (AI&ML), Vardhaman College of Engineering, Shamshabad, Hyderabad, Telangana, India

Abstract

In the current biomedical engineering, it has been established that the development of smart drug delivery systems has become a paramount of relevance especially in ensuring precise, controlled and targeted therapeutic effects. This paper proposes a responsive polymer composite architecture with built-in AI, which is used to deliver drugs in a controlled manner and involves the development of advanced material design and predictive modeling based on data. Biocompatible materials and nanocomposite improvements are used to create responsive polymer composites which can respond to various stimuli including pH, temperature and biochemical signals, to enhance efficiency of drug encapsulation and release. To address the shortcomings of traditional drug delivery models, the system under consideration combines the Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) to approximate a complicated nonlinear relationship and temporal release affects. The hybrid ANN-CNN architecture is created to be used to process physicochemical parameters and time-dependent data of drug release simultaneously. Such reinforcement learning is also included so that adaptive control of drug release can be controlled in dynamic physiological conditions. AI predictions are added to mathematical models, such as diffusion-based and empirical release kinetics, in order to increase the accuracy and interpretability. The experimental and simulation findings prove that the suggested approach has better performance as the prediction accuracy and error are better than in the traditional models. The system is efficient in capturing the behavior which changes in response to the stimuli and this makes it possible to optimize the parameters of the polymer design to achieve the desired release profiles. The paper reveals the opportunities of merging artificial intelligence and responsive biomaterials in order to come up with the next-generation smart drug delivery systems that can sustain personalized medicine and real-time therapeutic control.

Keywords: AI-integrated drug delivery, Responsive polymer composites, Controlled drug release, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN).

How to cite this article: A.R. Shinde, Chandrashekhar Ramesh Ramtirthkar, Valli Nachiyar C, Tanveer Ahmad Wani, Akella Yeswanth, Swati Sucharita. AI-integrated Responsive Polymer Composites for Controlled Drug Delivery. Journal of Polymer & Composites. 2026; 14(04):-.
How to cite this URL: A.R. Shinde, Chandrashekhar Ramesh Ramtirthkar, Valli Nachiyar C, Tanveer Ahmad Wani, Akella Yeswanth, Swati Sucharita. AI-integrated Responsive Polymer Composites for Controlled Drug Delivery. Journal of Polymer & Composites. 2026; 14(04):-. Available from: https://journals.stmjournals.com/jopc/article=2026/view=253033

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Ahead of Print Subscription Original Research
Volume 14
04
Received 01/05/2026
Accepted 30/06/2026
Published 21/08/2026
Publication Time 112 Days

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