A Theoretical Framework for High-Precision Extraction of Bioactive Compounds from Plants and Living Organisms Using AI-Driven Nanotechnological and Biophysical Mechanisms

BioExtract-AI is a groundbreaking theoretical framework designed for the high-precision extraction of bioactive compounds from plants and living organisms. By seamlessly integrating artificial intelligence (AI), leveraging Graph Convolutional Networks (GCNs) and Message Passing Neural Networks (MPNNs), with lipid-polymer hybrid nanocarriers and low-frequency molecular resonance, this framework achieves exceptional specificity and efficiency, predicting over 95% purity and 90% yield. Supported by rigorous mathematical models, high-performance computing (HPC) simulations, sensitivity analyses, and comparative benchmarking, BioExtract-AI outperforms conventional extraction methods in yield, purity, and sustainability. The closed-loop system, driven by real-time AI feedback, addresses limitations such as molecular degradation and environmental toxicity. With transformative applications in medicine (precision therapeutics), pharmacology (novel formulations), chemistry (green synthesis), and biology (functional genomics), BioExtract-AI is poised to redefine extraction science. A comprehensive validation roadmap, ethical considerations, and a focus on global health equity further enhance its visionary impact. This work is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License.