Abstract

The artificial intelligence (AI) and nanotechnology coming together have created a game-changing horizon in drug discovery, formulation, and delivery of therapy. AI-based nanopharmacology is a machine learning (ML), deep learning (DL), and intelligent computational science approach to designing, optimizing and assessing nanoscale drug systems with unprecedented accuracy. The present paper is a review of recent developments in AI-driven nanoparticle design, autonomous nanosystems and applications of nanomedicine in the clinic. In the present day, AI algorithms make it possible to rapidly predict the effects of molecular interactions, screen the compatibility between drugs and nanocarriers in high throughput, and optimize the search of physicochemical parameters to achieve targeted delivery. Simultaneously, intelligent nanosystems e.g. stimuli-responsive nanocarriers, programmable nanoformulations, and early-stage therapeutic nano robots are transforming clinical pharmacology through the ability to release drugs in a controlled manner, real-time biosensing and individual pharmacokinetic modelling. Although great strides have been achieved, there are still several issues of data integrity, model bias, translational safety and ethical concerns in implementing autonomous nanosystems. It is predicted that future directions will involve AI-based digital twins, predictive nano-AI systems, and completely autonomous nanotherapeutic systems with the capacity to adjust their own responses to patient-specific biomarkers. This review has identified the possibility of AI-driven nanopharmacology to transform contemporary therapeutics, providing new avenues of precision medicine and pharmacological efficacy and personalized treatment of patients.