{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T03:57:16Z","timestamp":1790567836492,"version":"4.1.0"},"reference-count":103,"publisher":"Wiley","issue":"18","license":[{"start":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T00:00:00Z","timestamp":1789948800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T00:00:00Z","timestamp":1789948800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["chemistry-europe.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["ChemMedChem"],"published-print":{"date-parts":[[2026,9,28]]},"abstract":"<jats:p>\n                    Identifying safe and effective anti\u2010inflammatory agents is challenging, especially given the adverse effects of long\u2010term conventional therapies. Here, we present a multistage computational framework to prioritize early natural compounds with potential anti\u2010inflammatory activity across diverse chemical spaces. The proposed workflow integrates machine learning, consensus prediction, drug\u2010likeness evaluation, synthetic accessibility assessment, toxicity prediction, and molecular docking to enable stepwise refinement of candidate selection. Six predictive models were developed and evaluated with gold\u2010standard metrics, all showing consistent performance with accuracies, AUROCs, and AUPRCs above 0.8. A consensus strategy was used to reduce model variability and improve prediction reliability. SHAP analysis identified structural scaffolds associated with predicted anti\u2010inflammatory activity, providing interpretability for compound prioritization. Sequential filtering selected compounds that met predefined criteria for drug\u2010likeness, synthetic feasibility, and predicted safety profiles. Application of the framework to the NPASS database yielded 20 prioritized natural products, of which 17 candidates were further selected based on molecular docking analysis, exhibiting predicted binding affinities of &lt;\u22127\u2009kcal\/mol. This framework was deployed on a web server, CAIP (\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/caip-predictor.streamlit.app\/\">https:\/\/caip\u2010predictor.streamlit.app\/<\/jats:ext-link>\n                    ), enabling rapid prediction and preliminary virtual screening of novel compounds. In total, the proposed framework offers a computational approach to systematically support the prioritization of anti\u2010inflammatory candidate molecules.\n                  <\/jats:p>","DOI":"10.1002\/cmdc.70497","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T01:43:48Z","timestamp":1790041428000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Multistage Computational Framework for Early Prioritization of Natural Anti\u2010Inflammatory Candidates From Diverse Chemical Spaces"],"prefix":"10.1002","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-0436-6014","authenticated-orcid":false,"given":"Huynh Anh","family":"Duy","sequence":"first","affiliation":[{"name":"Graduate School in the Program of Research and Development in Pharmaceuticals Faculty of Pharmaceutical Sciences Khon Kaen University   Khon Kaen Thailand"},{"name":"Faculty of Health Sciences College of Natural Sciences Can Tho University  Can Tho Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8512-5379","authenticated-orcid":false,"given":"Tarapong","family":"Srisongkram","sequence":"additional","affiliation":[{"name":"Division of Pharmaceutical Chemistry Faculty of Pharmaceutical Sciences Khon Kaen University  Khon Kaen 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