AI-Enhanced Biochemical Discovery and Optimization of Antimalarial Compounds from Indigenous Medicinal Plants: An Integrative Framework for Data-Driven Natural Product Drug Development

This work presents an integrated biochemical and artificial intelligence (AI) framework for the discovery and optimization of antimalarial compounds derived from indigenous medicinal plants. Using advanced analytical techniques—such as HPLC, LC-MS/MS, and NMR—we isolate and characterize bioactive phytochemicals with potential anti-plasmodial activity. These biochemical datasets are then combined with machine learning models to predict compound activity, toxicity, ADMET properties, and structure–activity relationships (SAR). We further employ molecular docking and AI-driven generative optimization to refine phytochemical structures and identify synergistic interactions among plant compounds. This integrative approach accelerates natural product drug discovery and highlights the therapeutic potential of African medicinal plants as sources of novel, accessible, and sustainable antimalarial agents. The dataset, computational pipeline, and conceptual framework presented here contribute to the fields of drug discovery, ethnopharmacology, precision medicine, and AI-assisted phytochemistry, providing a foundation for future applications in malaria treatment and biomedical innovation.