AI-Driven Molecular Communication for Targeted Drug Delivery with Adaptive Release Optimization

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 8

Year of Publication : 2025

Authors : Harsha Sanap, Vinitkumar Jayaprakash Dongre

10.14445/23488549/IJECE-V12I8P125

How to Cite?

Harsha Sanap, Vinitkumar Jayaprakash Dongre, "AI-Driven Molecular Communication for Targeted Drug Delivery with Adaptive Release Optimization," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 8, pp. 281-292, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I8P125

Abstract:

Molecular Communication has emerged as a prominent area of research, particularly for drug-based medical therapies. Nanomachines are utilized to inject drugs (such as anti-inflammatory molecules) into the human body, targeting infected cells through communication technologies. Nevertheless, creating an effective Targeted Drug Delivery (TDD) system that minimizes drug wastage remains a significant challenge. Previous research has tackled various issues, including improper drug delivery and low success rates. These challenges have inspired us to set the goal of accurately delivering drugs to the intended location using molecular Communication in TDD. Furthermore, we propose an AI-enhanced TDD system aimed at achieving improved therapeutic outcomes for a range of conditions, including cancer and heart disease. This research also addresses the problems of high side effects, improper path selection, and inefficient drug delivery. The main objective of this research is to design an advanced targeted drug delivery using molecular Communication. So, we employ Artificial Intelligence (AI) technology for Targeted Drug Delivery with an adaptive Drug Release Rate optimization method (AI-TDD) to overcome the existing issues. We execute a Double Deep Q Network (DDQN)- based adaptive Drug Release method to minimize drug wastage. This method uses biomarker concentrations and timely signal provisioning from external devices and entities. This work’s simulation is performed using Python-based simulations with fine-tuned system and simulation configurations. Our work’s performance assessment is carried out using four major metrics: Delivery Error Analysis, RMSE Analysis, Drug Release Rate Analysis, and Drug Reception Rate Analysis, which shows that our proposed AI-TDD model outperforms the existing model.

Keywords:

Molecular Communication, Targeted Drug Delivery, Adaptive Drug Release Rate, Artificial Intelligence, Nanomachines.

References:

[1] Marco Ferrari et al., “Channel Characterization of Diffusion-Based Molecular Communication with Multiple Fully-Absorbing Receivers,” IEEE Transactions on Communications, vol. 70, no. 5, pp. 3006-3019, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Tadashi Nakano et al., “Molecular Communication and Networking: Opportunities and Challenges,” IEEE Transaction on Nanobioscience, vol. 11, no. 2, pp. 135-148, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Nariman Farsad et al., “A Comprehensive Survey of Recent Advancements in Molecular Communication,” IEEE Communications Surveys & Tutorials, vol. 18, no. 3, pp. 1887-1919, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Nariman Farsad, Weisi Guo, and Andrew W. Eckford, “Tabletop Molecular Communication: Text Messages through Chemical Signals,” Plos One, vol. 8, no. 12, pp. 1-13, 2013.
[CrossRef] [Google Scholar] [Publisher Link]
[5] L. Felicetti et al., “Applications of Molecular Communications to Medicine: A Survey,” Nano Communication Networks, vol. 7, pp. 27-45, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Uche A.K. Chude-Okonkwo et al., “Molecular Communication and Nanonetwork for Targeted Drug Delivery: A Survey,” IEEE Communications Surveys & Tutorials, vol. 19, no. 4, pp. 3046-3096, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Diana Guimarães et al., “Design of Liposomes as Drug Delivery System for Therapeutic Applications,” International Journal of Pharmaceutics, vol. 601, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Rohan Gupta et al., “Artificial Intelligence to Deep Learning: Machine Intelligence Approach for Drug Discovery,” Molecular Diversity, vol. 25, pp. 1315-1360, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Qian Liao et al., “Application of Artificial Intelligence in Drug-Target Interactions Prediction: A Review,” NPJ Biomedical Innovations, vol. 2, no. 1, pp. 1-19, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Mohammed Elmowafy, and Mohammad M. Al-Sanea, “Nanostructured Lipid Carriers (NLCs) as Drug Delivery Platform: Advances in Formulation and Delivery Strategies,” Saudi Pharmaceutical Journal, vol. 29, no. 9, pp. 999-1012, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Vishwajeet M. Khot et al., “Nanomedicine-Driven Molecular Targeting, Drug Delivery, and Therapeutic Approaches to Cancer Chemoresistance,” Drug Discovery Today, vol. 26, no. 3, pp. 724-739, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Amit K. Shrivastava et al., “Transmission and Detection Techniques For Internet Of Bio-Nano Things Applications With Static And Mobile Molecular Communication: A Survey,” ITU Journal on Future and Evolving Technologies, vol. 2, no. 3, pp. 1-46, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Asad Jamal et al., “Insights into Infusion-Based Targeted Drug Delivery in the Brain: Perspectives, Challenges and Opportunities,” International Journal of Molecular Sciences, vol. 23, no. 6, pp. 1-29, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Hamid Khoshfekr Rudsari et al., “Targeted Drug Delivery for Cardiovascular Disease: Modeling of Modulated Extracellular Vesicle Release Rates,” IEEE Transactions on NanoBioscience, vol. 20, no. 4, pp. 444-454, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Seyda Senturk, Ibrahim Kok, and Fatmana Senturk, “Internet of Nano, Bio-Nano, Biodegradable and Ingestible Things: A Survey,” arXiv preprint, pp. 1-9, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Michael Taynnan Barro et al., “Molecular Communications in Viral Infections Research: Modeling, Experimental Data, and Future Directions,” IEEE Transactions on Molecular, Biological and Multi-Scale Communications, vol. 7, no. 3, pp. 121-141, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Tania Islam, Ethungshan Shitiri, and Ho-Shin Cho, “A Molecular Communication-Based Simultaneous Targeted-Drug Delivery Scheme,” IEEE Access, vol. 9, pp. 96658-96670, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Saied M. Abd El-atty et al., “Bioinspired Molecular Communications System For Targeted Drug Delivery With Iobnt-Based Sustainable Biocyber Interface,” Journal of Computers and Electrical Engineering, vol. 118, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[19] A.R. Junejo, Mohammed K.A. Kaabar, and Xiang Li, “Optimization: Molecular Communication Networks for Viral Disease Analysis Using Deep Leaning Autoencoder,” Computational and Mathematical Methods in Medicine, vol. 2021, no. 1, pp. 1-11, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Neda Sharifi et al., “Overcoming Channel Uncertainties in Touchable Molecular Communication for Direct-Drug-Targeting-Assisted Immuno-Chemotherapy,” IEEE Transactions on NanoBioscience, vol. 19, no. 2, pp. 249-258, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Tania Islam, Ethungshan Shitiri, and Ho-Shin Cho, “In-Body Sequential Multi-Drug Delivery Scheme using Molecular Communication,” IEEE Access, vol. 10, pp. 39975-39985, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Nelson R.C. Monteiro, Bernardete Ribeiro, and Joel P. Arrais, “Drug-Target Interaction Prediction: End-to-End Deep Learning Approach,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 18, no. 6, pp. 2364-2374, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Muneer M. Al-Zu’bi, and Ananda Sanagavarapu Mohan, “Modelling of Implantable Drug Delivery System in Tumor Microenvironment Using Molecular Communication Paradigm,” IEEE Access, vol. 7, pp. 141929-141940, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Lu Tang et al., “Advanced and Innovative Nano-Systems for Anticancer Targeted Drug Delivery,” Journals Pharmaceutics, vol. 13, no. 8, pp. 1-30, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Sibi Raj et al., “Specific Targeting Cancer Cells with Nanoparticles and Drug Delivery in Cancer Therapy,” Seminars in Cancer Biology, vol. 69, pp. 166-177, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Aya El-Fatyany, Hongzhi Wang, and Saied M. Abd El-atty, “Efficient Framework Analysis for Targeted Drug Delivery Based on Internet of Bio-NanoThings,” Arabian Journal for Science and Engineering, vol. 46, pp. 9965-9980, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Qingying Zhao, Min Li, and Lin Lin, “Release Rate Optimization in Molecular Communication for Local Nanomachine-Based Targeted Drug Delivery,” IEEE Transactions on NanoBioscience, vol. 20, no. 4, pp. 396-405, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Youssef Chahibi et al., “A Molecular Communication System Model for Particulate Drug Delivery Systems,” IEEE Transactions on Biomedical Engineering, vol. 60, no. 12, pp. 3468-3483, 2013.
[CrossRef] [Google Scholar] [Publisher Link]