ARTIFICIAL INTELLIGENCE IN THE DISCOVERY AND DEVELOPMENT OF SOLANACEAE-DERIVED ANTICANCER AGENTS:

Abstract

Cancer remains one of the foremost causes of morbidity and mortality worldwide, posing a substantial challenge to healthcare systems despite remarkable advances in diagnosis and treatment. Although chemotherapy, radiotherapy, immunotherapy, and targeted therapies have improved patient outcomes, their clinical application is frequently constrained by adverse effects, multidrug resistance, limited selectivity, and high treatment costs. These limitations have intensified the search for safer, multi-targeted, and biologically effective therapeutic agents from natural sources.

Medicinal plants continue to play a pivotal role in drug discovery because they contain structurally diverse bioactive molecules with broad pharmacological activities and favorable safety profiles. Among these, the Solanaceae family has emerged as an important source of phytochemicals exhibiting significant anticancer potential. Numerous secondary metabolites isolated from Solanaceae species have demonstrated the ability to inhibit cancer progression by regulating apoptosis, cell proliferation, angiogenesis, metastasis, oxidative stress, inflammation, and multiple intracellular signaling pathways.

This review critically examines the current evidence on the anticancer potential of Solanaceae-derived bioactive compounds while emphasizing the growing contribution of Artificial Intelligence (AI) to natural-product-based drug discovery. A comprehensive analysis of studies published between 2011 and 2025 was performed using major scientific databases, including PubMed, Scopus, Web of Science, Google Scholar, Wiley Online Library, and Semantic Scholar. The collected literature was systematically evaluated to summarize phytochemical composition, pharmacological activities, molecular targets, mechanisms of action, and therapeutic prospects of Solanaceae species in cancer management.

Beyond conventional experimental findings, this review highlights how AI-driven technologies are reshaping phytopharmaceutical research. Advanced computational approaches, including machine learning, deep learning, quantitative structure–activity relationship (QSAR) modeling, molecular docking, molecular dynamics simulations, network pharmacology, predictive toxicology, and multi-omics data integration, are increasingly being employed to identify promising phytochemicals, predict biological targets, optimize lead compounds, evaluate pharmacokinetic characteristics, and estimate safety profiles. These AI-enabled methodologies substantially reduce the time, cost, and complexity associated with traditional drug discovery while improving the efficiency of identifying therapeutically relevant natural compounds.

The review compiles evidence on approximately eighteen Solanaceae species reported to possess anticancer activity, encompassing nearly sixty-three bioactive phytochemicals with demonstrated efficacy in preclinical investigations. Their mechanisms involve modulation of key oncogenic pathways, including PI3K/Akt/mTOR, MAPK, NF-κB, JAK/STAT, Wnt/β-catenin, and p53 signaling, resulting in suppression of tumor growth, induction of programmed cell death, inhibition of metastatic progression, and regulation of the tumor microenvironment. Comprehensive tables summarizing plant sources, major phytochemicals, molecular targets, experimental evidence, and AI-assisted computational predictions are included to facilitate comparison and future investigation.

Furthermore, this review identifies current knowledge gaps and discusses future opportunities for integrating AI with phytochemistry, systems biology, precision oncology, and translational pharmacology. The convergence of artificial intelligence with natural-product research provides an innovative framework for accelerating the discovery and development of next-generation anticancer agents derived from the Solanaceae family. By combining computational intelligence with experimental validation, AI has the potential to transform traditional medicinal knowledge into evidence-based precision therapeutics, ultimately contributing to safer, more effective, and personalized cancer treatment strategies.