It is thus possible for AI to support network pharmacology analysis through the comprehensive integration of metabolomics datasets for the identification of bioactive phytoconstituents, virtual target prediction of the phytoconstituents, and creation of compound-target-pathway networks towards elucidating action pathways. It can aid with virtual screening by illustrating docking and binding affinities and estimates ADMET properties for the safety and effectiveness assessment of compounds. Omics data like genomics and proteomics are also incorporated into AI for the assessment of biological significance or to form hypotheses while analyzing network pharmacology data for significant patterns. This synergy reduces the cycle time for and optimizes validation, making plant-based drug discovery more efficient.

AI can significantly enhance traditional network pharmacology studies of phytoconstituents by automating and improving key steps. AI algorithms can be used to predict drug-target interactions more accurately and efficiently, going beyond the limitations of experimental methods. Machine learning models can also analyze large datasets of biological interactions to identify novel targets and pathways relevant to plant compounds. Furthermore, AI can assist in building and visualizing complex biological networks, making it easier to understand the mechanisms of action of phytoconstituents. Ultimately, AI integration can streamline the validation process of plant-derived compounds, accelerating drug discovery and development.