Multimodal AI and Multi-Omics in Natural Product Therapeutics: A Bibliometric Review of Research Frontiers, Data Modalities, and Translational Opportunities

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Multimodal artificial intelligence and multi-omics approaches are increasingly used to connect natural-product chemistry with biosynthetic pathways, molecular targets, biological responses, safety signals, and translational evidence, yet the intellectual structure and maturity of this research landscape remain incompletely characterized. This bibliometric review maps research frontiers, knowledge clusters, dominant data modalities, computational approaches, translational opportunity domains, and evidence gaps in multimodal AI and multi-omics research relevant to natural product therapeutics. A bounded pilot corpus was constructed through public bibliographic discovery across PubMed/PMC and DOI-indexed publisher and Crossref records, using OpenAlex-compatible metadata fields. The search covered peer-reviewed English-language journal articles published from 1 January 2017 to 11 July 2026. Forty-four candidate records underwent DOI-based deduplication and eligibility screening; one duplicate and four ineligible records were removed, producing a final corpus of 39 articles. Analyses included annual publication distribution, source distribution, reviewer-coded thematic clustering, data-modality mapping, AI-method mapping, translational-opportunity mapping, and evidence-boundary assessment. The corpus included 34 journal sources and peaked in 2022 with 11 publications; Natural Product Reports and Briefings in Bioinformatics contributed three articles each, while Pharmaceuticals contributed two. Four reviewer-coded clusters were identified: bibliometric methods and open metadata infrastructure, comprising eight records; natural-product omics, databases, and network knowledge, comprising nine; AI-enabled multimodal and multi-omics integration, comprising 14; and translational validation, bibliometric synthesis, and research governance, comprising eight. Dominant modalities included metabolomics, genomics, transcriptomics, chemical structures and spectra, network data, knowledge graphs, and microbiome data. Translational opportunity themes included mechanism discovery, target prediction, biomarker discovery, safety assessment, phytopharmaceutical standardization, and evidence integration. Bibliometric patterns can identify research fronts and support agenda setting, but they do not establish methodological quality, biological validity, clinical utility, therapeutic effectiveness, safety, or regulatory readiness.