Abstract

Cancer therapy remains challenging due to the dynamic and adaptive nature of drug resistance, often caused by genetic and micro environmental influences. This review explores how artificial intelligence (AI) and computational drug design (CADD) can revolutionize the discovery of multi-targeted natural product-based therapies. Unlike traditional approaches, AI-driven modeling can predict cancer resistance pathways, optimize synergistic compound combinations, and enhance drug repurposing efforts. Flavonoids and terpenoids, known for their potent anti-cancer properties, serve as case studies to explore network-based pharmacology and AI-driven molecular docking techniques. Despite promising advancements, challenges remain in developing AI models that fully capture tumor heterogeneity and evolving resistance mechanisms. This review synthesizes recent advancements, emphasizing the integration of AI-driven simulations with natural product research to develop adaptive and personalized cancer therapies

Keywords:Artificial Intelligence; Flavonoids; Terpenoids; Natural Product-Based Drugs; Multi-Targeted Therapies

Introduction

Cancer remains a leading cause of mortality worldwide, with conventional treatments such as chemotherapy and radiation therapy facing significant limitations, including toxicity, resistance, and tumor heterogeneity. One of the most critical challenges in cancer treatment is drug resistance, which arises from both innate and acquired mechanisms influenced by genetic, epigenetic, proteomic, metabolic, and micro-environmental factors [1]. Historically, cancer treatment modalities have included chemotherapy, hormone therapy, radiation therapy, and immunotherapy. While chemotherapy remains a mainstay, its cytotoxic effects often extend beyond cancer cells, damaging healthy tissues and leading to severe side effects [2,3]. Targeted therapies and combination treatments have emerged to enhance precision by disrupting specific molecular signaling pathways [4]. More recently, the focus has shifted towards multi-targeted therapies, wherein drug combinations or polypharmacological agents improve treatment efficacy and reduce resistance [5].

Natural products have been extensively explored for their anti-cancer potential, with plant-derived phytochemicals demonstrating significant therapeutic activity [6]. Among the 300,000 known natural products, various classes including alkaloids, flavonoids, terpenoids, and polypeptides have shown cytotoxic effects on cancer cells [7]. Compounds such as vinca alkaloids, paclitaxel, and camptothecin have undergone clinical validation and have been incorporated into mainstream chemotherapy regimens [8-10]. Research has confirmed the effectiveness of natural compounds in cancer therapy. Among 1,394 small molecule drugs approved between 1981 and 2019, approximately 67% were derived from natural sources [8]. These compounds induce apoptosis and disrupt essential cellular pathways, making them promising candidates for multi-targeted cancer therapies. The ability of phytochemicals to simultaneously target multiple cancer pathways such as receptor tyrosine kinases (RTKs), phosphoinositide 3-kinase (PI3K), mammalian target of rapamycin (mTOR), Wnt, Notch, and transforming growth factor-beta (TGF-β) highlights their potential in personalized oncology [11,12].

The integration of artificial intelligence (AI) and computational modeling has revolutionized drug discovery, offering innovative approaches to phytochemical screening, lead optimization, and personalized therapy design. Computational tools and machine learning algorithms enhance our ability to predict the bioactivity of natural compounds, accelerating drug development [13]. Structural and computational biology techniques, including network pharmacology and computer-aided drug design (CADD), provide unprecedented insights into how phytochemicals interact with cancer-related targets, guiding experimental validation [14- 16]. In recent years, deep learning models and AI-driven virtual screening have significantly reduced the time required to identify potent natural product-based drug candidates. AI tools like DeepTox and ProCTOR enhance toxicity prediction and screening, enabling the selection of phytochemicals with reduced off-target effects [14].

This review explores how AI-driven approaches can revolutionize the discovery and application of multi-targeted, natural product-based therapies for cancer treatment. A critical focus is on understanding the mechanisms of cancer drug resistance, particularly the genetic and epigenetic alterations that contribute to therapy failure. The role of natural compounds in oncology is examined, highlighting their multi-targeting potential and ability to modulate key signaling pathways involved in cancer progression. Additionally, this review delves into the advancements in AI-powered drug discovery, emphasizing the role of machine learning, computational modeling and optimizing lead identification and drug development. Finally, the challenges and future perspectives of this evolving field are discussed, addressing the limitations of AI models, data bias, and the necessity for experimental validation. By integrating natural product research with AI-driven drug discovery, this review underscores the potential of multi-targeted therapies as a promising strategy to overcome cancer treatment resistance and advance the field of personalized medicine.

Cancer drug resistance: a multifaceted challenge

Cancer drug resistance is a significant challenge in oncology, limiting the effectiveness of chemotherapy, targeted therapy, and immunotherapy. Resistance can be intrinsic, where cancer cells are naturally unresponsive to treatment, or acquired, which develops over time due to genetic and epigenetic modifications [17]. Mutations in oncogenes and tumor suppressor genes, along with DNA methylation and histone alterations, enable cancer cells to evade therapeutic effects. Tumor heterogeneity further complicates treatment, as different cancer cell populations within the same tumor may respond differently to drugs. One major mechanism of resistance is the overexpression of drug efflux pumps, such as P-glycoprotein (P-gp), which actively expel chemotherapeutic agents, reducing intracellular drug accumulation [18]. Other mechanisms include altered drug metabolism, where cancer cells neutralize drugs before they exert cytotoxic effects, and apoptosis evasion, where proteins like Bcl-2 suppress programmed cell death [19].

Additionally, the tumor microenvironment (TME), which includes hypoxia and immune suppression, protects cancer cells from drug effects, further contributing to resistance. In breast cancer, resistance to HER2-targeted therapies remains a major challenge [20]. A recent study found that tumor heterogeneity and alternative signaling pathways drive resistance to trastuzumab (Herceptin), leading to treatment failure. Researchers suggest that combination therapies targeting multiple pathways may improve outcomes [21]. In lung cancer, resistance to epidermal growth factor receptor (EGFR) inhibitors is well documented. A case study on non-small cell lung cancer (NSCLC) reported that a patient with a rare L833V/H835L EGFR mutation became resistant to afatinib, but sequential therapy with furmonertinib restored treatment efficacy [22]. Similarly, in neuroendocrine neoplasms (NENs), resistance to somatostatin analogs (SSAs) has been linked to changes in receptor expression and downstream signaling. Research suggests that modifying treatment strategies and using novel SSAs with improved receptor affinity may enhance patient response [23].

Role of natural compounds in cancer therapy

Natural compounds have played a pivotal role in cancer treatment due to their diverse bioactivities, including antioxidant, anti-inflammatory, pro-apoptotic, and anti-proliferative effects. Among these, flavonoids, terpenoids, and alkaloids have demonstrated significant potential in multi-targeted cancer therapy by modulating critical signaling pathways involved in tumor genesis [24,25]. Despite their therapeutic promise, bioavailability and stability challenges remain a concern, necessitating novel delivery systems and AI-driven optimization approaches to enhance their clinical applications [26].

Flavonoids: natural polyphenolic agents in cancer therapy

Flavonoids, a diverse group of polyphenolic compounds found in fruits, vegetables, and medicinal plants, exhibit antioxidant, anti-inflammatory, and anti-cancer properties. Quercetin, rutin, luteolin, and apigenin have shown remarkable ability to inhibit tumor growth, induce apoptosis, and modulate key cancer-related pathways such as PI3K/Akt, NF-κB, and MAPK [27]. Luteolin, for instance, has been reported to inhibit head and neck squamous cell carcinoma (HNSCC) in preclinical models by targeting p300 lysine acetyltransferase activity, regulating gene expression, and suppressing tumor progression [8]. Additionally, genistein, an isoflavone found in soy, has been shown to influence caspase activation, Bcl-2/Bax ratio, and β-catenin signaling, thereby inducing apoptosis in cancer cells [28]. Table 1 provides an overview of flavonoids and their molecular targets in cancer therapy, highlighting their diverse mechanisms of action and therapeutic potential. Another potent flavonoid, epigallocatechin gallate (EGCG), found in green tea, has demonstrated pro-oxidant and tumor-suppressive effects by modulating angiogenesis, metastasis, and oxidative stress pathways [29]. EGCG also inhibits NF-κB activity, thereby preventing inflammatory responses and promoting apoptosis in various cancers [30]. However, flavonoids bioavailability remains a limitation due to intestinal metabolism, poor solubility, and rapid excretion, necessitating novel drugdelivery strategies to improve therapeutic efficacy [31].

Terpenoids: multi-targeted anti-cancer agents

Terpenoids, a structurally diverse class of natural compounds, have demonstrated promising anti-cancer, anti-inflammatory, and immunomodulatory effects. Over 20,000 known terpenoids, derived from C30 precursors, exhibit anti-cancer activity through mechanisms such as inducing apoptosis, modulating oxidative stress, and inhibiting metastatic pathways [32]. Table 2 provides a comprehensive overview of terpenoids and their molecular targets in cancer therapy, highlighting their diverse therapeutic applications. Ursolic acid, a pentacyclic triterpenoid, has been shown to regulate NF-κB, STAT3, and TRAIL pathways, leading to ROS generation and inhibition of tumor growth [33]. Another important triterpenoid, oleanolic acid (OA), has been used as a platform for developing semi-synthetic derivatives with enhanced anti-cancer activity. Notably, CDDO (2-cyano- 3,12-dioxooleana-1,9(11)-dien-28-oic Acid) and its analogs have progressed to clinical trials, demonstrating effectiveness in solid tumors and hematological malignancies [34]. Additionally, taxane diterpenoids, including paclitaxel and docetaxel, derived from Taxus species, remain some of the most effective chemotherapeutic agents used for breast, lung, and ovarian cancers [35].

Alkaloids: targeting cancer signaling pathways

Alkaloids, nitrogen-containing secondary metabolites, have been widely investigated for their cytotoxic and anti-proliferative effects. Many alkaloids act by modulating mitogen-activated protein kinase (MAPK) pathways, inhibiting NF-κB, and inducing apoptosis [36]. For instance, hirsutin and rohitukin activate p38 MAPK, demonstrating cytotoxicity against breast, ovarian, and lung cancers [37]. Chaetoglobosin Fex, derived from the fungus Chaetomium globosum, reduces pro-inflammatory cytokine production and NF-κB signaling, effectively suppressing tumor progression [38]. Additionally, α-tomatine, a glycoalkaloid from tomatoes, induces apoptosis by inhibiting NF-κB nuclear translocation, a mechanism also observed in kahalalide F (KF), which induces necrosis-like cell death and ErbB3 depletion in cancer cells [39]. Several alkaloid-derived drugs, including vinblastine, vincristine, and irinotecan, are currently used in clinical oncology for leukemia, lung, and colorectal cancer treatment [40].

AI in natural product optimization

Developing effective anticancer drugs is challenging due to tumor heterogeneity, drug resistance, poor targeting, and high toxicity [41]. Advances in artificial intelligence (AI) and big data analytics have transformed drug discovery, particularly in natural product-based therapies, by enabling predictive modeling and biomarker identification [42]. AI accelerates drug development by identifying bioactive compounds, optimizing drug interactions, and predicting therapeutic efficacy and toxicity (Figure 1) [43]. A study by Bagdad and Miteva, 2024, showcased AI’s ability to repurpose existing compounds for cancer therapy. By screening over 100 million molecules, a deep learning model identified halicin, originally an antibiotic, as a potent inducer of apoptosis in drug-resistant cancer cells through mitochondrial disruption [44]. Machine learning (ML) and deep learning (DL) analyze vast datasets from genomics, proteomics, and cheminformatics to uncover oncogenic targets, drug-protein interactions, and resistance mechanisms, enhancing multi-targeted cancer therapies [45]. AlphaFold, an AI-driven protein structure prediction tools have significantly improved cancer target identification, aiding drug development [46]. Similarly, network pharmacology models have revealed synergistic interactions between flavonoids and alkaloids, supporting their use in combination therapies for drugresistant cancers [47].

In a recent study, AI-assisted molecular docking identified quercetin as a PI3K/Akt pathway inhibitor, demonstrating strong anti-cancer effects in colorectal cancer cells [48]. Additionally, integrating AutoDock Vina with RF-Score-VS has improved ligand-receptor binding predictions, reducing false positives and enhancing drug discovery efficiency [49]. AI-driven big data analytics have advanced drug repurposing by integrating multiomics datasets, patient records, and real-world evidence to uncover new therapeutic uses for existing drugs [50]. A recent study by Zou et al., 2024, highlighted AI’s role in repurposing statins for colorectal cancer therapy. Using machine learning models, researchers analyzed clinical databases and genomic profiles, revealing that statins suppress tumor growth by inhibiting the PI3K/Akt/mTOR pathway. Preclinical validation confirmed that combining statins with standard chemotherapy enhanced tumor regression, demonstrating AI’s ability to identify novel therapeutic applications for existing drugs [51].

Vinayagam et al., 2016, employed network controllability analysis to identify indispensable proteins in the human proteinprotein interaction (PPI) network, uncovering 56 critical genes across nine cancer types, 46 of which were novel cancer associated genes [52]. Similarly, Lai et al., 2021, integrated RNA sequencing data with network-based algorithms to identify miRNA-based strategies for enhancing dendritic cell (DC)- elicited immune responses. Their approach revealed key miRNAs, such as miR-15a and miR-16, which could improve DC-based immunotherapy, demonstrating the power of AI in uncovering novel immunotherapeutic targets [53]. In precision oncology, AI analyzes tumor molecular profiles to tailor personalized treatment strategies. AI-driven clinical trial models optimize patient selection, trial design, and endpoint prediction, significantly improving success rates in targeted cancer therapies [54]. A study by Segun, 2024, demonstrated the impact of AI in personalized cancer treatment. By leveraging genomic and transcriptomic data, AI models identified individualized therapeutic targets for lung cancer patients, leading to a higher response rate to immunotherapy [55].

Challenges and future perspectives

Despite the promising advancements in AI-driven natural product-based therapies for cancer treatment, several challenges remain that need to be addressed to fully realize their potential. One of the primary challenges is the complexity of tumor heterogeneity, which makes it difficult to develop AI models that can accurately predict the behavior of diverse cancer cell populations within the same tumor [56]. Additionally, the dynamic nature of cancer resistance mechanisms, driven by genetic and epigenetic alterations, poses a significant hurdle in creating robust AI algorithms that can adapt to evolving resistance patterns. Data bias and the lack of comprehensive, high-quality datasets also limit the accuracy and generalizability of AI models, necessitating the integration of diverse data sources and rigorous validation processes [57]. Furthermore, the translation of AI-predicted drug candidates into clinically effective therapies requires extensive experimental validation, which can be time-consuming and resource-intensive. Future research should focus on developing more sophisticated AI models that can integrate multi-omics data, improve predictive accuracy, and account for the complex interplay between cancer cells and their microenvironment [58,59].

Conclusion & Future prospects

The integration of natural product-based therapies with AIdriven drug discovery offers a promising strategy to overcome cancer drug resistance and improve treatment precision. Flavonoids, terpenoids, and alkaloids have demonstrated significant potential in targeting multiple oncogenic pathways, while AI-driven computational models, including molecular docking, network pharmacology, and virtual screening, have accelerated drug identification and optimization. Despite these advancements, challenges remain in fully capturing tumor heterogeneity, refining AI prediction accuracy, and translating computational findings into clinically effective therapies. Future research should focus on improving AI models by integrating multi-omics data to enhance drug-target interaction predictions and optimize compound selection. Additionally, experimental validation through in vitro and in vivo studies is essential to confirm the efficacy and safety of AI-identified drug candidates. Addressing data bias and improving the quality of training datasets will enhance AI reliability, while AI-assisted drug delivery systems can help overcome the bioavailability limitations of natural compounds. Moreover, AI-guided combination therapy strategies can optimize synergistic effects and minimize toxicity, paving the way for more effective treatment approaches. By bridging the gap between computational drug design and clinical application, AIdriven natural product research holds great potential to develop multi-targeted, adaptive, and personalized cancer therapies.

Conflicts of Interest

The authors declare that they don’t have any conflict of interest in the publication.

Acknowledgement

The authors are highly obliged for the kind and needful support of Banasthali Vidyapith, for providing the necessary facility to conduct this study. The authors are also thankful to the DBT funded Bioinformatics Centre at Department of Bioscience and Biotechnology and “Workshop in Bioinformatics” project of Banasthali Vidyapith.

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