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Artificial intelligence-based screening of phytochemicals for targeted cancer therapy
Livia Ramos Santiago1, Estéfani Alves Asevedo1, Maria Eduarda Jeunon de Oliveira1
1Department of Experimental Pathology, Federal University of São João del-Rei, Sebastião Gonçalves Coelho Street, 400-Chanadour, Divinópolis, MG, 35501-296, Brazil.
Abstract:
Cancer remains one of the leading causes of death worldwide and continues to pose a serious public health challenge. The limited success of many current treatments-often due to toxicity, poor selectivity, and the development of drug resistance-highlights the need for new and more effective therapeutic options. Phytochemicals have emerged as a valuable source of anticancer agents, offering rich structural diversity and a wide range of biological activities. However, identifying promising compounds from the vast chemical space of natural products remains difficult using conventional screening methods, which are typically slow, costly, and inefficient. In recent years, artificial intelligence (AI) has begun to transform phytochemical-based drug discovery. Machine learning and deep learning approaches are now used to support key steps in the discovery process, including metabolite identification, virtual screening, target prediction, and toxicity assessment. By integrating chemical, biological, and multi-omics data, AI enables a more systematic and data-driven exploration of natural product diversity. Despite these advances, challenges persist, particularly the scarcity of high-quality experimental data, the structural complexity of phytochemicals, and their limited representation in public databases. This review critically examines current AI applications in phytochemical-based anticancer drug discovery and discusses emerging strategies aimed at overcoming these limitations. Overall, AI-driven phytochemical screening represents a promising path toward accelerating the development of next-generation cancer therapies.
Insights
Artificial intelligence (AI) accelerates the discovery of anticancer drugs from phytochemicals, overcoming limitations of traditional methods. AI-driven screening offers a promising route to develop novel cancer therapies by analyzing complex natural product data.
Area of Science:
- Natural Product Chemistry
- Computational Drug Discovery
- Oncology Therapeutics
Background:
- Cancer is a major global health challenge with limited treatment success due to toxicity and drug resistance.
- Phytochemicals offer diverse structures and biological activities for novel anticancer agent development.
- Conventional screening of natural products is inefficient, costly, and time-consuming.
Purpose of the Study:
- To review current artificial intelligence (AI) applications in phytochemical-based anticancer drug discovery.
- To discuss emerging strategies for overcoming AI-related challenges in natural product drug discovery.
- To highlight AI's potential to accelerate the development of next-generation cancer therapies.
Main Methods:
- Application of machine learning and deep learning for metabolite identification, virtual screening, target prediction, and toxicity assessment.
- Integration of chemical, biological, and multi-omics data for systematic exploration of natural product diversity.
- Critical examination of AI's role in addressing limitations of traditional phytochemical screening.
Main Results:
- AI enables more efficient and data-driven exploration of phytochemicals for anticancer drug discovery.
- AI tools support key steps in the drug discovery pipeline, from identification to toxicity assessment.
- AI integration facilitates a systematic approach to harnessing natural product diversity for therapeutic potential.
Conclusions:
- AI-driven phytochemical screening is a promising strategy to accelerate the development of novel cancer therapies.
- Addressing challenges like data scarcity and structural complexity is crucial for maximizing AI's impact.
- AI represents a transformative approach to discovering effective and selective anticancer agents from natural sources.
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