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

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.