AI as a catalyst for mechanistic target discovery: Integrating systems pharmacology and multimodal data

AI as a catalyst for mechanistic target discovery: Integrating systems pharmacology and multimodal data | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL British Journal of Pharmacology This is a preprint and has not been peer reviewed. Data may be preliminary. 12 May 2026 V1 Latest version Share on AI as a catalyst for mechanistic target discovery: Integrating systems pharmacology and multimodal data Authors : Xuerui Song [email protected] , Zhi Chen [email protected] , Yunfei An [email protected] , and Jie Zhang [email protected] Authors Info & Affiliations https://doi.org/10.22541/authorea.15003184/v1 Under Review British Journal of Pharmacology Peer review timeline 16 views 9 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Artificial intelligence (AI) is evolving from a predictive tool into a foundational computational infrastructure for mechanism-driven pharmacology, fundamentally reshaping drug discovery. This review examines how this transformation addresses persistent challenges in target validation, including data biases and the need for model interpretability, by integrating network pharmacology with advanced deep learning architectures. Specifically, graph neural networks decipher the complex topology of biological systems, and Transformer models facilitate the fusion of multimodal data, from genomics to real-world clinical records. Coupled with physics-informed neural networks, this integrated framework operates as a predictive computational microscope. It enables comprehensive in silico simulations that span multiple biological scales, encompassing atomic-level molecular interactions and longitudinal patient trajectories. We demonstrate that this AI-driven paradigm is essential for advancing precision medicine, as it systematically translates vast and heterogeneous datasets into testable mechanistic hypotheses. Consequently, this approach accelerates the development of safer, more effective, and patient-specific therapies by de-risking target validation and elucidating novel therapeutic mechanisms. It directly addresses some of the most pressing inefficiencies in contemporary drug discovery and development, offering a pathway toward more rational and efficient therapeutic innovation. Information & Authors Information Version history V1 Version 1 12 May 2026 Peer review timeline Under Review British Journal of Pharmacology 16 May 2026 Review Complete Collection British Journal of Pharmacology Authors Affiliations Xuerui Song [email protected] View all articles by this author Zhi Chen [email protected] View all articles by this author Yunfei An [email protected] View all articles by this author Jie Zhang [email protected] Xi'an Children’s Hospital, Xi’an, China View all articles by this author Metrics & Citations Metrics Article Usage 16 views 9 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Xuerui Song, Zhi Chen, Yunfei An, et al. AI as a catalyst for mechanistic target discovery: Integrating systems pharmacology and multimodal data. Authorea . 12 May 2026. DOI: https://doi.org/10.22541/authorea.15003184/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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