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A fundamental limitation of most supervised pipelines is their failure to explicitly model intermolecular similarities during task-specific training. Neglecting this macroscopic topology of the chemical space leads to fragmented latent representations, poor generalization, and high false negative rates. To address this, we propose MacroTox, a macroscopic topology-driven multimodal deep learning framework. Beyond intramolecular fusion, its core innovation is the dynamic construction of an intrabatch drug\u2013drug similarity graph. Guided by a topology-aware synergistic optimization, this mechanism captures latent network correlations, maximizing the utilization of the chemical space to resolve representational fragmentation and enhance the predictive capacity under data scarcity. Evaluated on a rigorously curated bone toxicity data set, MacroTox achieved an area under the receiver operating characteristic curve of 0.93 and a Matthews correlation coefficient of 0.73. Crucially, it effectively balances the sensitivity-specificity trade-off (SEN: 0.88, SPE: 0.86), substantially mitigating the underreporting risks in bone toxicity screening. Notably, ablation studies confirm that these predictive enhancements stem from the synergistic effect of dynamic graph topology and edge loss regularization. Extensive benchmarking of MoleculeNet further confirms its robust transferability. Furthermore, via multilevel feature attribution, MacroTox elucidates chemically intuitive structure\u2013activity relationships. By pinpointing specific toxicophores and resolving complex activity cliffs, the framework proves that it captures authentic toxicological mechanisms rather than memorizing superficial data set biases. Ultimately, MacroTox offers an reliable, generalizable, and interpretable virtual screening engine for early stage drug discovery.<\/jats:p>","DOI":"10.1021\/jacsau.6c01105","type":"journal-article","created":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T04:29:19Z","timestamp":1788409759000},"page":"5420-5432","source":"Crossref","is-referenced-by-count":0,"title":["MacroTox: A Macroscopic\nGraph Topology-Based Multimodal\nLearning Framework for Robust Molecular Toxicity Prediction"],"prefix":"10.1021","volume":"6","author":[{"given":"He","family":"Huang","sequence":"first","affiliation":[{"name":"East China Normal University , , ,","place":["Shanghai, China, 200062"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinyi","family":"Wang","sequence":"additional","affiliation":[{"name":"East China Normal University , , ,","place":["Shanghai, China, 200062"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manzhan","family":"Zhang","sequence":"additional","affiliation":[{"name":"East China University of Science and Technology , , ,","place":["Shanghai, China, 200237"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pan","family":"Dou","sequence":"additional","affiliation":[{"name":"East China Normal University , , ,","place":["Shanghai, China, 200062"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaobo","family":"Yang","sequence":"additional","affiliation":[{"name":"East China Normal University , , ,","place":["Shanghai, China, 200062"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2270-1900","authenticated-orcid":true,"given":"Honglin","family":"Li","sequence":"additional","affiliation":[{"name":"East China Normal University , , ,","place":["Shanghai, China, 200062"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4414-237X","authenticated-orcid":true,"given":"Shiliang","family":"Li","sequence":"additional","affiliation":[{"name":"East China Normal University , , ,","place":["Shanghai, China, 200062"]},{"name":"HuaDong Hospital Affiliated to Fudan University , , ,","place":["Shanghai, China, 200040"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,8,31]]},"reference":[{"key":"2026092808094478600_cit1","doi-asserted-by":"publisher","first-page":"895","DOI":"10.1038\/s41573-023-00774-7","article-title":"Artificial Intelligence for Natural Product Drug Discovery","volume":"22","author":"Mullowney","year":"2023","journal-title":"Nat. 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