Abstract Traditional medicinal plants Mitragyna Speciosa and Plumbago Indica have exhibited several neuroprotective activities against Parkinson’s disease (PD) in several research studies. Nevertheless, further elucidation is needed about the molecular mechanism by which these medicinal plants exert their neuroprotective effects, as well as the relationship between their active constituents’ structure and activity. Using a polypharmacology approach, the study identified metabolic pathways targeted by active phytochemicals of these medicinal plants. Extensive system biology approaches, including protein-protein interaction network analysis, KEGG pathway analysis, and gene functional enrichment study, pinpointed that AKT1 was the key gene involved in the molecular mechanism actions of the active phytochemicals concerning neuroprotective actions. A robust machine-learning guided bioactivity prediction model-based web application ( https://akt1pred.streamlit.app/ ) against AKT1 was developed by implementing PubChem and Substructure fingerprint molecular signatures. Further validation of the model was done by conducting ROC and applicability domain analysis, with subsequent molecular docking studies to understand the molecular mechanisms of the phytochemicals. The web application predicted that delphinidin and kaempferol were the most active phytochemicals responsible for the neuroprotective effects of the medicinal plants, which was further supported by extensive molecular docking and molecular dynamics simulation study. These findings indicate a correlation between the structure of these compounds and their bioactivity, with some phytochemicals performing comparably or better than known FDA drugs. Results suggest significant potential for natural products in therapeutic applications, urging further in vitro and in vivo investigation and offering a robust foundation for future research into natural product-based small molecule binding and drug discovery in PD. Heighlights ♦ Mitragyna Speciosa and Plumbago Indica share certain neuroprotective qualities. ♦ We determined metabolomics pathways by active plant-based constituents using the polypharmacology technique. ♦ The development of a reliable stable machine-learning model and a web application ♦ Web-based application predicted neuroprotective effects of delphinidin and kaempferol Graphical Abstract

Elucidating the molecular mechanism of phytochemicals against Parkinson’s disease through an integrated systems biology and molecular modeling approach