Artificial Intelligence–Driven Discovery of Plant-Derived Bioactive Compounds in Cancer Therapeutics: Computational Approaches and Molecular Target Identification

Abstract

Objective: Artificial intelligence (AI) use and computational approaches have transformed the discovery of plant-derived bioactive chemicals for cancer treatment. This review synthesized evidence on AI-based approaches, including network pharmacology, molecular docking, and machine learning, for identifying and validating anticancer plant-derived phytochemicals, focusing on their molecular targets and signaling pathways. Data Sources: PRISMA 2020 guidelines were followed. PubMed, Scopus, Web of Science, and Google Scholar were searched between October 2020 and July 2025. Study Selection: Studies with at least one AI or computational method, investigating plant-derived phytochemicals for anticancer effects, reporting quantitative computational outcomes, and offering free full-text access in English were included. Review articles, conference abstracts, non-plant studies, and non-English publications were excluded. Data Extraction: Two reviewers extracted data on predefined variables. An adapted PROBAST tool was used to assess risk of bias for in silico studies and modified SYRCLE tools for in vitro and in vivo studies, with certainty of evidence evaluated using the GRADE method. Data Synthesis: Eighteen studies satisfied the inclusion criteria. The most frequently discovered bioactive chemical was quercetin, which targets breast cancer, hepatocellular carcinoma (HCC), and lung cancer via AKT1, EGFR, TP53, and MAPK signaling pathways. The majority of in silico studies have a high risk of bias, while in vitro and in vivo studies were rated as having some concerns. The overall certainty of evidence was low. Conclusion: AI-based computational techniques have become critical tools for discovering plant-derived compounds and identifying multi-target mechanisms involving AKT1, EGFR, and PI3K/Akt signaling.