{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T08:13:21Z","timestamp":1783930401971,"version":"3.55.0"},"reference-count":38,"publisher":"American Association for the Advancement of Science (AAAS)","issue":"1","funder":[{"DOI":"10.13039\/501100004561","name":"Ministry of Education and Science of the Republic of Kazakhstan","doi-asserted-by":"publisher","award":["AP23488419"],"award-info":[{"award-number":["AP23488419"]}],"id":[{"id":"10.13039\/501100004561","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012632","name":"Nazarbayev University","doi-asserted-by":"publisher","award":["040225FD4734"],"award-info":[{"award-number":["040225FD4734"]}],"id":[{"id":"10.13039\/501100012632","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012632","name":"Nazarbayev University","doi-asserted-by":"publisher","award":["20122022FD4120"],"award-info":[{"award-number":["20122022FD4120"]}],"id":[{"id":"10.13039\/501100012632","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012632","name":"Nazarbayev University","doi-asserted-by":"publisher","award":["040225FD4745"],"award-info":[{"award-number":["040225FD4745"]}],"id":[{"id":"10.13039\/501100012632","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["spj.science.org"],"crossmark-restriction":true},"short-container-title":["Comput Struct Biotechnol J"],"published-print":{"date-parts":[[2026,3,1]]},"abstract":"\n Computational drug discovery relies on virtual screening to prioritize a small number of compounds for experimental testing, yet combining heterogeneous\n in silico<\/jats:italic>\n scoring methods into a single robust ranking remains challenging. We present Calibrated Weighted Rank Aggregation (CWRA), a data-driven rank-optimized score-fusion framework that (a) direction-corrects modality outputs, (b) harmonizes them via per-modality normalization, and (c) learns nonnegative fusion weights from known actives by optimizing a rank-based early-recognition objective (Boltzmann-enhanced discrimination of receiver operating characteristic [BEDROC]) under simplex constraints. Applied to the vitamin D receptor (VDR), we fuse machine-learning affinity predictors (GraphDTA endpoints, MLT-LE, TankBind, DrugBAN, and MolTrans), structure-based scores (AutoDock Vina and Boltz-2 affinity and confidence), and ligand similarity (Uni-Mol; computed in a split-honest manner by recomputing the active-set centroid on the training splits). Across 5 repeated random splits over the\n \n \n P<\/mml:mi>\n =<\/mml:mo>\n 503<\/mml:mn>\n <\/mml:math>\n <\/jats:inline-formula>\n known VDR binders, evaluated on the drug-likeness prefiltered pool of\n \n \n \n N<\/mml:mi>\n \u2032<\/mml:mo>\n <\/mml:msup>\n =<\/mml:mo>\n 14<\/mml:mn>\n ,<\/mml:mo>\n 902<\/mml:mn>\n <\/mml:math>\n <\/jats:inline-formula>\n compounds, CWRA achieves strong early enrichment on held-out actives (\n \n \n EF<\/mml:mtext>\n @<\/mml:mo>\n 1<\/mml:mn>\n %<\/mml:mo>\n =<\/mml:mo>\n 21.32<\/mml:mn>\n \u00b1<\/mml:mo>\n 6.41<\/mml:mn>\n <\/mml:math>\n <\/jats:inline-formula>\n , corresponding to 16 \u00b1 5 recovered binders within the top 1% of the ranked list), substantially outperforming equal-weight fusion (\n \n \n EF<\/mml:mi>\n @<\/mml:mo>\n 1<\/mml:mn>\n %<\/mml:mo>\n =<\/mml:mo>\n 10.79<\/mml:mn>\n \u00b1<\/mml:mo>\n 2.81<\/mml:mn>\n <\/mml:math>\n <\/jats:inline-formula>\n ) and the strongest single modality (GraphDTA\n IC<\/jats:italic>\n 50<\/jats:sub>\n ;\n \n \n EF<\/mml:mtext>\n @<\/mml:mo>\n 1<\/mml:mn>\n %<\/mml:mo>\n =<\/mml:mo>\n 16.32<\/mml:mn>\n \u00b1<\/mml:mo>\n 3.96<\/mml:mn>\n <\/mml:math>\n <\/jats:inline-formula>\n ). Performance remains strong at broader cutoffs (\n \n \n EF<\/mml:mtext>\n @<\/mml:mo>\n 2.5<\/mml:mn>\n %<\/mml:mo>\n =<\/mml:mo>\n 14.13<\/mml:mn>\n \u00b1<\/mml:mo>\n 2.34<\/mml:mn>\n <\/mml:math>\n <\/jats:inline-formula>\n ,\n \n \n EF<\/mml:mtext>\n @<\/mml:mo>\n 5<\/mml:mn>\n %<\/mml:mo>\n =<\/mml:mo>\n 9.47<\/mml:mn>\n \u00b1<\/mml:mo>\n 0.60<\/mml:mn>\n <\/mml:math>\n <\/jats:inline-formula>\n , and\n \n \n EF<\/mml:mtext>\n @<\/mml:mo>\n 10<\/mml:mn>\n %<\/mml:mo>\n =<\/mml:mo>\n 7.13<\/mml:mn>\n \u00b1<\/mml:mo>\n 0.72<\/mml:mn>\n <\/mml:math>\n <\/jats:inline-formula>\n ), indicating that BEDROC-optimized fusion of normalized scores improves both early prioritization and moderate-depth screening. To assess generalization beyond VDR, we further applied CWRA to the mechanistically distinct \u03b3-aminobutyric acid type A (GABA\n A<\/jats:sub>\n ) receptor. Target-specific calibration again improved very early enrichment (\n \n \n EF<\/mml:mtext>\n @<\/mml:mo>\n 1<\/mml:mn>\n %<\/mml:mo>\n =<\/mml:mo>\n 25.41<\/mml:mn>\n \u00b1<\/mml:mo>\n 3.96<\/mml:mn>\n <\/mml:math>\n <\/jats:inline-formula>\n ) over both equal-weight fusion (\n \n \n EF<\/mml:mtext>\n @<\/mml:mo>\n 1<\/mml:mn>\n %<\/mml:mo>\n =<\/mml:mo>\n 17.47<\/mml:mn>\n \u00b1<\/mml:mo>\n 6.17<\/mml:mn>\n <\/mml:math>\n <\/jats:inline-formula>\n ) and the best individual modality at this cutoff (Boltz-2 confidence;\n \n \n EF<\/mml:mtext>\n @<\/mml:mo>\n 1<\/mml:mn>\n %<\/mml:mo>\n =<\/mml:mo>\n 11.64<\/mml:mn>\n \u00b1<\/mml:mo>\n 2.12<\/mml:mn>\n <\/mml:math>\n <\/jats:inline-formula>\n ). Strict cross-target source-model transfer between VDR and GABA\n A<\/jats:sub>\n was weaker than target-specific recalibration, indicating that CWRA should be interpreted as a transferable target-adaptive framework rather than a universal fixed-weight model. A qualitative review of the top-ranked generated candidates suggests that CWRA preferentially selects chemically coherent VDR-like chemotypes, recapitulating established VDR pharmacophore patterns without imposing handcrafted structural rules.\n <\/jats:p>","DOI":"10.34133\/csbj.0155","type":"journal-article","created":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T09:09:56Z","timestamp":1781514596000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark_01","source":"Crossref","is-referenced-by-count":0,"title":["Calibrated Weighted Rank Aggregation for Virtual Screening Independently Rediscovers Privileged Vitamin D Receptor Ligand Scaffolds"],"prefix":"10.34133","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6630-585X","authenticated-orcid":false,"given":"Abylay","family":"Salimzhanov","sequence":"first","affiliation":[{"name":"Department of Computer Science, School of Digital Sciences and Engineering, \rNazarbayev University, Astana, Kazakhstan."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1594-5157","authenticated-orcid":false,"given":"Askar","family":"Boranbayev","sequence":"additional","affiliation":[{"name":"Department of Computer Science, School of Digital Sciences and Engineering, \rNazarbayev University, Astana, Kazakhstan."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9008-4233","authenticated-orcid":true,"given":"Ferdinand","family":"Moln\u00e1r","sequence":"additional","affiliation":[{"name":"Department of Biology, School of Sciences and Humanities, \rNazarbayev University, Astana, Kazakhstan."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3397-0647","authenticated-orcid":true,"given":"Siamac","family":"Fazli","sequence":"additional","affiliation":[{"name":"Department of Computer Science, School of Digital Sciences and Engineering, \rNazarbayev University, Astana, Kazakhstan."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"221","published-online":{"date-parts":[[2026,7,13]]},"reference":[{"issue":"16","key":"e_1_3_4_2_2","doi-asserted-by":"crossref","first-page":"1923","DOI":"10.2174\/1568026614666140929124445","article-title":"Structure-based virtual screening for drug discovery: Principles, applications and recent advances","volume":"14","author":"Lionta E","year":"2014","unstructured":"Lionta E, Spyrou G, Vassilatis DK, Cournia Z. 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