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The core of GQ consists of stacked layers of Hoogsteen-paired guanine bases (G-tetrads), which can arise from various topologies adopted by the backbone strands of guanine tracts (G-tracts). The polymorphic nature of GQs presents significant challenges for therapeutic targeting. An as-yet-addressed question is how, if at all, chemical moieties of different G-tetrads and G-tracts mediate distinct mechanical strengths such that the strands and layers display nonuniform mechanical resistance. As a first attempt toward a quantitative understanding of this fundamental problem, we generalize the recently developed structure-mechanics statistical learning framework. The method transforms an all-atom molecular dynamics trajectory into Hookean spring coefficients between base, ribose, backbone, and metal-ion moieties, which allows a chemical-scale comparison of mechanical heterogeneity across GQ topologies. A recurring theme that emerges from this analysis is the cross-influence among chemical moieties. Contacts with loops may cooperatively strengthen base stacking in one strand while leading to compensatory weakening in another. Importantly, end-capping of intra-molecular GQs with small-molecule stabilizers is shown to induce system-wide and topology-specific variations in chemical-scale mechanical properties. Strong stabilizer\u2013GQ interactions may compete with base\u2013metal ion coordination or base stacking and reduce their strengths. However, a weakly bound stabilizer can trigger loop reorganization, which in turn leads to new interactions that enhance structural stability. Our analysis of chemical-scale mechanical heterogeneity thus provides a generalizable framework that could be applicable to understanding more complicated inter-molecular GQs and their responses to small-molecule modulations.<\/jats:p>","DOI":"10.1063\/5.0351724","type":"journal-article","created":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T10:32:28Z","timestamp":1790332348000},"update-policy":"https:\/\/doi.org\/10.1063\/aip-crossmark-policy-page","source":"Crossref","is-referenced-by-count":0,"title":["Chemical-scale force responses across G-quadruplex topologies via machine learning"],"prefix":"10.1063","volume":"165","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-4504-4760","authenticated-orcid":false,"given":"Shih-Hsueh","family":"Hsu","sequence":"first","affiliation":[{"name":"Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University 1 , Hsinchu 30010, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0268-6352","authenticated-orcid":false,"given":"Haw","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Chemistry, Princeton University 2 , Princeton, New Jersey 08544,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3842-2893","authenticated-orcid":false,"given":"Jhih-Wei","family":"Chu","sequence":"additional","affiliation":[{"name":"Institute of Bioinformatics and Systems Biology, Department of Biological Science and Technology, Institute of Molecular Medicine and Bioengineering, Center for Intelligent Drug Systems and Smart Bio-devices ( 3 IDS , Hsinchu 30010, Taiwan"},{"name":"B), National Yang Ming Chiao Tung University 3 IDS , Hsinchu 30010, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"317","published-online":{"date-parts":[[2026,9,25]]},"reference":[{"key":"2026092506322231300_c1","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1038\/nchem.1548","volume":"5","year":"2013","journal-title":"Nat. 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