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Here, we introduce state-constrained SHAP (SC-SHAP), an attribution-to-rule distillation framework for fixed-interval polymer screening. Using XGBoost as the teacher, SC-SHAP reorganizes SHAP and SHAP-interaction attributions on chemically interpretable MACCS fingerprints into support-qualified structural states defined by required-present and required-absent substructures. These states provide auditable property ordering, executable structural conditions, and candidate-level rule traces, while the learned library is reused across target windows with interpolation-only boundary calibration. Evaluation on experimental PoLyInfo datasets for glass-transition temperature (Tg), tensile stress at break (TSS), and electrical conductivity (EC) yields Spearman correlations between state scores and mean measured properties of 0.908 \u00b1 0.008, 0.817 \u00b1 0.069, and 0.835 \u00b1 0.025, respectively. Benchmarks include direct XGBoost, MACCS-GCN, MACCS-Transformer, and rule-based screening. In the fixed-interval screening benchmark, SC-SHAP achieves higher mean scores than direct XGBoost, MACCS-GCN, MACCS-Transformer, and WRAcc on the smaller TSS and EC datasets. Multiple seeds and alternative structural splits further assess transfer to structurally distinct polymers. By unifying nonlinear knowledge distillation, chemically explicit rules, and cross-window reuse, SC-SHAP provides an interpretable screening framework spanning thermal, mechanical, and electrical polymer properties.<\/jats:p>","DOI":"10.1021\/acs.jcim.6c02120","type":"journal-article","created":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T20:02:02Z","timestamp":1791489722000},"page":"13237-13251","source":"Crossref","is-referenced-by-count":0,"title":["State-Constrained\nSHAP Distills Polymer Property Models\ninto Transferable Structural Rules for Fixed-Interval Screening"],"prefix":"10.1021","volume":"66","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-4445-7074","authenticated-orcid":true,"given":"Weihao","family":"Wang","sequence":"first","affiliation":[{"name":"University of Tokyo , , ,","place":["Tokyo, Japan, 113-8656"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoou","family":"Ruan","sequence":"additional","affiliation":[{"name":"University of Tokyo , , ,","place":["Tokyo, Japan, 113-8656"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5432-5597","authenticated-orcid":true,"given":"Keith T.","family":"Butler","sequence":"additional","affiliation":[{"name":"University College London , , ,","place":["London, United Kingdom, WC1H 0AJ"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akiko","family":"Kumada","sequence":"additional","affiliation":[{"name":"University of Tokyo , , ,","place":["Tokyo, Japan, 113-8656"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9606-1104","authenticated-orcid":true,"given":"Masahiro","family":"Sato","sequence":"additional","affiliation":[{"name":"University of Tokyo , , ,","place":["Tokyo, Japan, 113-8656"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,10,8]]},"reference":[{"key":"2026102608085608400_cit1","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1038\/s41586-018-0337-2","article-title":"Machine learning\nfor molecular and materials science","volume":"559","author":"Butler","year":"2018","journal-title":"Nature"},{"key":"2026102608085608400_cit2","doi-asserted-by":"publisher","DOI":"10.1038\/s41524-017-0056-5","article-title":"Machine learning in materials informatics:\nrecent applications and\nprospects","volume":"3","author":"Ramprasad","year":"2017","journal-title":"npj Comput. 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In  PoLyInfo: Polymer Database for\nPolymeric Materials Design, 2011 International Conference\non Emerging Intelligent Data and Web Technologies; IEEE, 2011; pp 22\u201329.","DOI":"10.1109\/EIDWT.2011.13"},{"key":"2026102608085608400_cit7","doi-asserted-by":"publisher","first-page":"17575","DOI":"10.1021\/acs.jpcc.8b02913","article-title":"Polymer genome:\na data-powered polymer informatics platform for property predictions","volume":"122","author":"Kim","year":"2018","journal-title":"J. Phys. Chem. C"},{"key":"2026102608085608400_cit8","doi-asserted-by":"publisher","DOI":"10.1063\/5.0023759","article-title":"Machine-learning predictions\nof polymer properties with Polymer Genome","volume":"128","author":"Doan Tran","year":"2020","journal-title":"J.\nAppl. 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In  A Unified Approach\nto Interpreting Model Predictions, Advances in Neural Information\nProcessing Systems; NIPS, 2017."},{"key":"2026102608085608400_cit25","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","article-title":"From local explanations\nto global\nunderstanding with explainable AI for trees","volume":"2","author":"Lundberg","year":"2020","journal-title":"Nat. Mach. Intell."},{"key":"2026102608085608400_cit26","doi-asserted-by":"crossref","unstructured":"Molnar, C.; Ko\u0308nig, G.; Herbinger, J.; Freiesleben, T.; Dandl, S.; Scholbeck, C. A.; Casalicchio, G.; Grosse-Wentrup, M.; Bischl, B. 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Phys."},{"key":"2026102608085608400_cit47","volume-title":"Mechanical Properties\nof Solid Polymers","author":"Ward","year":"1971"},{"key":"2026102608085608400_cit48","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780198520597.001.0001","volume-title":"Polymer Physics","author":"Rubinstein","year":"2003"},{"key":"2026102608085608400_cit49","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-69002-5","volume-title":"Physical Properties\nof Polymers Handbook","author":"Mark","year":"2007"},{"key":"2026102608085608400_cit50","author":"Heeger","year":"2001","journal-title":"Semiconducting and\nMetallic Polymers: The Fourth Generation of Polymeric Materials"},{"key":"2026102608085608400_cit51","volume-title":"Handbook of Conducting\nPolymers","author":"Skotheim","year":"1997"},{"key":"2026102608085608400_cit52","doi-asserted-by":"publisher","first-page":"1038","DOI":"10.1038\/nmat3722","article-title":"A general relationship\nbetween disorder, aggregation and charge transport in conjugated polymers","volume":"12","author":"Noriega","year":"2013","journal-title":"Nat. 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T.; Singh, S.; Guestrin, C. In  Anchors:\nHigh-Precision\nModel-Agnostic Explanations, Proceedings of the AAAI conference\non artificial intelligence; AAAI Press, 2018.","DOI":"10.1609\/aaai.v32i1.11491"},{"key":"2026102608085608400_cit57","unstructured":"Guidotti, R.; Monreale, A.; Ruggieri, S.; Pedreschi, D.; Turini, F.; Giannotti, F.\n          Local Rule-Based\nExplanations of Black Box Decision Systems. 2018; arXiv:1805.10820. arXiv.org e-Printarchive. https:\/\/arxiv.org\/abs\/1805.10820."},{"key":"2026102608085608400_cit58","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2021.103457","article-title":"Glocalx-from\nlocal to global explanations of black\nbox ai models","volume":"294","author":"Setzu","year":"2021","journal-title":"Artif. Intell."},{"key":"2026102608085608400_cit59","unstructured":"Alkhatib, A.; Bostro\u0308m, H.; Vazirgiannis, M. 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