Explainable Artificial Intelligence for Mechanism-Based Discovery of Multi-Component Herbal Therapies
Keywords:
explainable artificial intelligence, herbal medicine, mechanism-based discovery, network pharmacology, systems biology, causal inference, interpretability, governanceAbstract
Multi-component herbal therapies present a distinctive challenge for computational discovery because their therapeutic effects are rarely reducible to single molecular targets. Instead, they emerge from complex interactions among phytochemicals, biological pathways, disease states, and patient-specific contexts. Explainable artificial intelligence offers a promising route toward mechanism-based discovery in this domain, yet its application requires careful integration of network pharmacology, systems biology, causal reasoning, and regulatory knowledge. This paper provides a systems-level analysis of explainable artificial intelligence for multi-component herbal therapy discovery. It examines the structural trade-offs between predictive performance and interpretability, the role of network-based representations, the epistemic demands of mechanism-based explanation, and the deployment constraints imposed by clinical and regulatory environments. The discussion emphasizes that explanation in this setting cannot be reduced to feature attribution alone. It must connect model outputs to modular biological mechanisms that can be interrogated, replicated, and evaluated within a governance framework. The paper further considers robustness, fairness, and reproducibility as properties of the broader socio-technical infrastructure rather than as after-the-fact checks. By framing explainable artificial intelligence as part of an integrated discovery system, the analysis clarifies how computational models can support, rather than displace, the interpretive practices of pharmacologists, clinicians, and herbal medicine researchers. The paper concludes with forward-looking observations on the institutional and technical conditions needed for credible translation of multi-component herbal therapies into clinical settings.
References
1. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
2. Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2018). A survey of methods for explaining black box models. ACM Computing Surveys, 51(5), 1-42.
3. Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems 30 (pp. 4765-4774).
4. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). Why should I trust you? Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1135-1144).
5. Hopkins, A. L. (2008). Network pharmacology: The next paradigm in drug discovery. Nature Chemical Biology, 4(11), 682-690.
6. Barabási, A.-L., Gulbahce, N., & Loscalzo, J. (2011). Network medicine: A network-based approach to human disease. Nature Reviews Genetics, 12(1), 56-68.
7. Li, S., & Zhang, B. (2013). Traditional Chinese medicine network pharmacology: Theory, methodology and application. Chinese Journal of Natural Medicines, 11(2), 110-120.
8. Tang, J., Alelyani, S., & Liu, H. (2014). Feature selection for classification: A review. In Data Classification: Algorithms and Applications (pp. 37-64). CRC Press.
9. Tshitoyan, V., Dagdelen, J., Weston, L., Dunn, A., Rong, Z., Kononova, O., Persson, K. A., Ceder, G., & Jain, A. (2019). Unsupervised word embeddings capture latent knowledge from materials science literature. Nature, 571(7763), 95-98.
10. Sun, M., Zhao, S., Gilvary, C., Elemento, O., Zhou, J., & Wang, F. (2020). Graph convolutional networks for computational drug development and discovery. Briefings in Bioinformatics, 21(3), 919-935.
11. Zhang, W., Chien, J., Yong, J., & Kuang, R. (2017). Network-based machine learning and graph theory algorithms for precision oncology. npj Precision Oncology, 1(1), 25.
12. Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press.
13. Schölkopf, B., Locatello, F., Bauer, S., Ke, N. R., Kalchbrenner, N., Goyal, A., & Bengio, Y. (2021). Toward causal representation learning. Proceedings of the IEEE, 109(5), 612-634.
14. Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.
15. Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey of explainable artificial intelligence (XAI). IEEE Access, 6, 52138-52160.
16. Ratti, E., & Graves, M. (2022). Explainable machine learning practices: Opening another black box for reliable medical AI. AI and Ethics, 2(4), 801-814.
17. Wang, Y., Yao, J., Sui, Y., Jiang, H., Ma, B., Lai, S., ... & Tan, N. (2026). HerbSyner_Finder: a network community-based model for identifying synergistic combinations from herbal medicines and complex systems. Targetome, 2(2).
18. Lamb, J., Crawford, E. D., Peck, D., Modell, J. W., Blat, I. C., Wrobel, M. J., ... & Golub, T. R. (2006). The Connectivity Map: Using gene-expression signatures to connect small molecules, genes, and disease. Science, 313(5795), 1929-1935.
19. Amann, J., Blasimme, A., Vayena, E., Frey, D., & Madai, V. I. (2020). Explainability for artificial intelligence in healthcare: A multidisciplinary perspective. BMC Medical Informatics and Decision Making, 20(1), 310.
20. Boezio, B., Audouze, K., Ducrot, P., & Taboureau, O. (2017). Network-based approaches in pharmacology. Molecular Informatics, 36(10), 1700048.
21. Goodman, B., & Flaxman, S. (2017). European Union regulations on algorithmic decision-making and a "right to explanation". AI Magazine, 38(3), 50-57.
22. Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358.
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