{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T19:23:54Z","timestamp":1792178634170,"version":"4.4.0"},"reference-count":41,"publisher":"American Chemical Society (ACS)","issue":"8","license":[{"start":{"date-parts":[[2020,7,17]],"date-time":"2020-07-17T00:00:00Z","timestamp":1594944000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,7,17]],"date-time":"2020-07-17T00:00:00Z","timestamp":1594944000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2020,7,17]],"date-time":"2020-07-17T00:00:00Z","timestamp":1594944000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-045"}],"funder":[{"DOI":"10.13039\/100006108","name":"National Center for Advancing Translational Sciences","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","award":["1U01CA207160"],"award-info":[{"award-number":["1U01CA207160"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,8,24]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Small, colloidally aggregating molecules (SCAMs) are the most common source of false positives in high-throughput screening (HTS) campaigns. Although SCAMs can be experimentally detected and suppressed by the addition of detergent in the assay buffer, detergent sensitivity is not routinely monitored in HTS. Computational methods are thus needed to flag potential SCAMs during HTS triage. In this study, we have developed and rigorously validated quantitative structure-interference relationship (QSIR) models of detergent-sensitive aggregation in several HTS campaigns under various assay conditions and screening concentrations. In particular, we have modeled detergent-sensitive aggregation in an AmpC \u03b2-lactamase assay, the preferred HTS counter-screen for aggregation, as well as in another assay that measures cruzain inhibition. Our models increase the accuracy of aggregation prediction by \u223c53% in the \u03b2-lactamase assay and by \u223c46% in the cruzain assay compared to previously published methods. We also discuss the importance of both assay conditions and screening concentrations in the development of QSIR models for various interference mechanisms besides aggregation. The models developed in this study are publicly available for fast prediction within the SCAM detective web application (https:\/\/scamdetective.mml.unc.edu\/).<\/jats:p>","DOI":"10.1021\/acs.jcim.0c00415","type":"journal-article","created":{"date-parts":[[2020,7,17]],"date-time":"2020-07-17T16:40:34Z","timestamp":1595004034000},"page":"4056-4063","source":"Crossref","is-referenced-by-count":37,"title":["SCAM Detective: Accurate Predictor of Small, Colloidally\nAggregating Molecules"],"prefix":"10.1021","volume":"60","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6182-1748","authenticated-orcid":true,"given":"Vinicius\nM.","family":"Alves","sequence":"first","affiliation":[{"name":"University of North Carolina , , , ,","place":["Chapel Hill, North Carolina, United States, 27599"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0001-8211","authenticated-orcid":true,"given":"Stephen J.","family":"Capuzzi","sequence":"additional","affiliation":[{"name":"University of North Carolina , , , ,","place":["Chapel Hill, North Carolina, United States, 27599"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rodolpho C.","family":"Braga","sequence":"additional","affiliation":[{"name":"InsilicAll , , ,","place":["Sa\u0303o Paulo, Sa\u0303o\nPaulo, Brazil, 04363-090"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Korn","sequence":"additional","affiliation":[{"name":"University of North Carolina , , , ,","place":["Chapel Hill, North Carolina, United States, 27599"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joshua E.","family":"Hochuli","sequence":"additional","affiliation":[{"name":"University of North Carolina , , , ,","place":["Chapel Hill, North Carolina, United States, 27599"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyle H.","family":"Bowler","sequence":"additional","affiliation":[{"name":"University of North Carolina , , , ,","place":["Chapel Hill, North Carolina, United States, 27599"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7350-1402","authenticated-orcid":true,"given":"Adam","family":"Yasgar","sequence":"additional","affiliation":[{"name":"National Institutes of Health , , 9800 Medical Center Drive , , ,","place":["Rockville, Maryland, United States, 20850"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9763-9641","authenticated-orcid":true,"given":"Ganesha","family":"Rai","sequence":"additional","affiliation":[{"name":"National Institutes of Health , , 9800 Medical Center Drive , , ,","place":["Rockville, Maryland, United States, 20850"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anton","family":"Simeonov","sequence":"additional","affiliation":[{"name":"National Institutes of Health , , 9800 Medical Center Drive , , ,","place":["Rockville, Maryland, United States, 20850"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4616-7036","authenticated-orcid":true,"given":"Eugene N.","family":"Muratov","sequence":"additional","affiliation":[{"name":"University of North Carolina , , , ,","place":["Chapel Hill, North Carolina, United States, 27599"]},{"name":"Federal University of Paraiba , , , ,","place":["Joa\u0303o Pessoa, Parai\u0301ba, Brazil, 58059"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2466-1711","authenticated-orcid":true,"given":"Alexey V.","family":"Zakharov","sequence":"additional","affiliation":[{"name":"National Institutes of Health , , 9800 Medical Center Drive , , ,","place":["Rockville, Maryland, United States, 20850"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3802-8896","authenticated-orcid":true,"given":"Alexander","family":"Tropsha","sequence":"additional","affiliation":[{"name":"University of North Carolina , , , ,","place":["Chapel Hill, North Carolina, United States, 27599"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2020,7,17]]},"reference":[{"issue":"1","key":"2026081805100183000_cit1","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1021\/acschembio.6b00791","article-title":"Internal\nStructure and Preferential Protein Binding of Colloidal Aggregates","volume":"12","author":"Duan","year":"2017","journal-title":"ACS Chem. Biol."},{"issue":"3","key":"2026081805100183000_cit2","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1016\/j.cbpa.2010.03.020","article-title":"Apparent Activity\nin High-Throughput\nScreening: Origins of Compound-Dependent Assay Interference","volume":"14","author":"Thorne","year":"2010","journal-title":"Curr. Opin. Chem. Biol."},{"issue":"20","key":"2026081805100183000_cit3","doi-asserted-by":"publisher","first-page":"4265","DOI":"10.1021\/jm030266r","article-title":"A Specific Mechanism\nof Nonspecific Inhibition","volume":"46","author":"McGovern","year":"2003","journal-title":"J. Med. Chem."},{"issue":"5","key":"2026081805100183000_cit4","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1038\/s41557-019-0234-9","article-title":"Computational Advances in Combating\nColloidal Aggregation in Drug Discovery","volume":"11","author":"Reker","year":"2019","journal-title":"Nat.\nChem."},{"issue":"1","key":"2026081805100183000_cit5","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1021\/jm010934d","article-title":"Development of a Virtual\nScreening Method for\nIdentification of \u201cFrequent Hitters\u201d in Compound Libraries","volume":"45","author":"Roche","year":"2002","journal-title":"J. Med. Chem."},{"issue":"7","key":"2026081805100183000_cit6","doi-asserted-by":"publisher","first-page":"2719","DOI":"10.1021\/jm901137j","article-title":"New Substructure\nFilters for Removal of Pan Assay Interference\nCompounds (PAINS) from Screening Libraries and for Their Exclusion\nin Bioassays","volume":"53","author":"Baell","year":"2010","journal-title":"J. Med. Chem."},{"issue":"6","key":"2026081805100183000_cit7","doi-asserted-by":"publisher","first-page":"564","DOI":"10.1002\/cmdc.201700673","article-title":"Hit Dexter:\nA Machine-Learning Model\nfor the Prediction of Frequent Hitters","volume":"13","author":"Stork","year":"2018","journal-title":"ChemMedChem"},{"issue":"3","key":"2026081805100183000_cit8","doi-asserted-by":"publisher","first-page":"1030","DOI":"10.1021\/acs.jcim.8b00677","article-title":"Hit Dexter\n2.0: Machine-Learning Models for the Prediction of Frequent Hitters","volume":"59","author":"Stork","year":"2019","journal-title":"J. Chem. Inf. Model."},{"key":"2026081805100183000_cit9","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1186\/s13321-016-0137-3","article-title":"Badapple: Promiscuity\nPatterns from Noisy Evidence","volume":"8","author":"Yang","year":"2016","journal-title":"J. Cheminf."},{"issue":"3","key":"2026081805100183000_cit10","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1038\/nchembio718","article-title":"High-Throughput\nAssays for Promiscuous Inhibitors","volume":"1","author":"Feng","year":"2005","journal-title":"Nat. Chem.\nBiol."},{"issue":"21","key":"2026081805100183000_cit11","doi-asserted-by":"publisher","first-page":"4477","DOI":"10.1021\/jm030191r","article-title":"Identification and\nPrediction of Promiscuous Aggregating Inhibitors among Known Drugs","volume":"46","author":"Seidler","year":"2003","journal-title":"J. Med. Chem."},{"key":"2026081805100183000_cit12","doi-asserted-by":"publisher","first-page":"7076","DOI":"10.1021\/acs.jmedchem.5b01105","article-title":"An Aggregation Advisor\nfor Ligand Discovery","volume":"58","author":"Irwin","year":"2015","journal-title":"J. Med. Chem."},{"issue":"4","key":"2026081805100183000_cit13","doi-asserted-by":"publisher","first-page":"752","DOI":"10.1002\/jcc.21347","article-title":"Identification\nof Small Molecule Aggregators from Large\nCompound Libraries by Support Vector Machines","volume":"31","author":"Rao","year":"2009","journal-title":"J. Comput. Chem."},{"issue":"6\u20137","key":"2026081805100183000_cit14","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1002\/minf.201000061","article-title":"Best Practices\nfor QSAR Model Development, Validation, and Exploitation","volume":"29","author":"Tropsha","year":"2010","journal-title":"Mol. Inf."},{"issue":"10","key":"2026081805100183000_cit15","doi-asserted-by":"publisher","first-page":"2385","DOI":"10.1021\/jm061317y","article-title":"A High-Throughput\nScreen for Aggregation-Based\nInhibition in a Large Compound Library","volume":"50","author":"Feng","year":"2007","journal-title":"J. Med.\nChem."},{"issue":"9","key":"2026081805100183000_cit16","doi-asserted-by":"publisher","first-page":"3714","DOI":"10.1021\/acs.jcim.9b00541","article-title":"Structural Analysis and Identification of Colloidal\nAggregators in Drug Discovery","volume":"59","author":"Yang","year":"2019","journal-title":"J. Chem. Inf.\nModel."},{"issue":"1","key":"2026081805100183000_cit17","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1021\/jm901070c","article-title":"Quantitative Analyses of Aggregation,\nAutofluorescence,\nand Reactivity Artifacts in a Screen for Inhibitors of a Thiol Protease","volume":"53","author":"Jadhav","year":"2010","journal-title":"J. Med. Chem."},{"issue":"D1","key":"2026081805100183000_cit18","doi-asserted-by":"publisher","first-page":"D1202","DOI":"10.1093\/nar\/gkv951","article-title":"PubChem Substance and Compound Databases","volume":"44","author":"Kim","year":"2016","journal-title":"Nucleic\nAcids Res."},{"key":"2026081805100183000_cit19","article-title":"Assay Interference\nby Aggregation","volume-title":"Assay Guidance Manual [Internet]","author":"Auld","year":"2017"},{"issue":"1","key":"2026081805100183000_cit20","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1021\/ja0455547","article-title":"ALARM NMR: A Rapid\nand Robust Experimental Method to Detect Reactive False Positives\nin Biochemical Screens","volume":"127","author":"Huth","year":"2005","journal-title":"J. Am. Chem. Soc."},{"issue":"1","key":"2026081805100183000_cit21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-60747-3","article-title":"High-Throughput\nScreening to Predict Chemical-Assay Interference","volume":"10","author":"Borrel","year":"2020","journal-title":"Sci. Rep."},{"key":"2026081805100183000_cit22","doi-asserted-by":"publisher","first-page":"W586","DOI":"10.1093\/nar\/gkaa378","article-title":"Inter Pred:\nA Webtool to Predict\nChemical Autofluorescence and Luminescence Interference","volume":"48","author":"Borrel","year":"2020","journal-title":"Nucleic Acids Res."},{"issue":"1","key":"2026081805100183000_cit23","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1021\/jm901069a","article-title":"Identification and Optimization of Inhibitors\nof Trypanosomal Cysteine Proteases: Cruzain, Rhodesain, and TbCatB","volume":"53","author":"Mott","year":"2010","journal-title":"J. Med. Chem."},{"issue":"25","key":"2026081805100183000_cit24","doi-asserted-by":"publisher","first-page":"7274","DOI":"10.1021\/jm061103g","article-title":"Interpreting\nSteep Dose-Response Curves in Early Inhibitor Discovery","volume":"49","author":"Shoichet","year":"2006","journal-title":"J. Med. Chem."},{"key":"2026081805100183000_cit25","first-page":"442","article-title":"Enabling the Large-Scale\nAnalysis\nof Quantitative High-Throughput","volume-title":"Handbook\nof Drug Screening","author":"Southall","year":"2009","edition":"2"},{"key":"2026081805100183000_cit26","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1007\/s12551-016-0218-6","article-title":"Dynamic Light Scattering: A Practical\nGuide and Applications in Biomedical Sciences","author":"Stetefeld","year":"2016","journal-title":"Biophysical Reviews"},{"issue":"8","key":"2026081805100183000_cit27","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1038\/nchembio.1881","article-title":"Curation of Chemogenomics Data","volume":"11","author":"Fourches","year":"2015","journal-title":"Nat. Chem. Biol."},{"issue":"7","key":"2026081805100183000_cit28","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1021\/ci100176x","article-title":"Trust, but\nVerify: On the Importance\nof Chemical Structure Curation in Cheminformatics and QSAR Modeling\nResearch","volume":"50","author":"Fourches","year":"2010","journal-title":"J. Chem. Inf. Model."},{"issue":"7","key":"2026081805100183000_cit29","doi-asserted-by":"publisher","first-page":"1243","DOI":"10.1021\/acs.jcim.6b00129","article-title":"Trust, but\nVerify II: A Practical\nGuide to Chemogenomics Data Curation","volume":"56","author":"Fourches","year":"2016","journal-title":"J. Chem.\nInf. Model."},{"key":"2026081805100183000_cit30","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1007\/978-1-4614-7138-7","article-title":"Random Forests","volume-title":"An Introduction to Statistical Learning","author":"James","year":"2013"},{"issue":"1","key":"2026081805100183000_cit31","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random\nForests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"issue":"6","key":"2026081805100183000_cit32","doi-asserted-by":"publisher","first-page":"1241","DOI":"10.1016\/j.drudis.2018.01.039","article-title":"The Rise of Deep Learning in Drug\nDiscovery","volume":"23","author":"Chen","year":"2018","journal-title":"Drug Discovery Today"},{"issue":"1","key":"2026081805100183000_cit33","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1002\/qsar.200390007","article-title":"The Importance of Being Earnest:\nValidation Is the Absolute Essential for Successful Application and\nInterpretation of QSPR Models","volume":"22","author":"Tropsha","year":"2003","journal-title":"QSAR Comb. Sci."},{"issue":"2\u20134","key":"2026081805100183000_cit34","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1023\/A:1025386326946","article-title":"Rational Selection of Training and Test Sets for the\nDevelopment of Validated QSAR Models","volume":"17","author":"Golbraikh","year":"2003","journal-title":"J. Comput.-Aided\nMol. 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Drug Discovery"},{"issue":"20","key":"2026081805100183000_cit41","doi-asserted-by":"publisher","first-page":"1795","DOI":"10.1002\/cmdc.201900395","article-title":"Identification\nof Compounds That Interfere with High-Throughput Screening Assay Technologies","volume":"14","author":"David","year":"2019","journal-title":"ChemMedChem"}],"container-title":["Journal of Chemical Information and Modeling"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acs.jcim.0c00415","content-type":"application\/pdf","content-version":"vor","intended-application":"unspecified"},{"URL":"https:\/\/pubs.acs.org\/jcisd8\/article-pdf\/60\/8\/4056\/9376091\/ci0c00415.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/pubs.acs.org\/jcisd8\/article-pdf\/60\/8\/4056\/9376091\/ci0c00415.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T10:22:25Z","timestamp":1787048545000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.acs.org\/jcisd8\/article\/60\/8\/4056\/874305\/SCAM-Detective-Accurate-Predictor-of-Small"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,17]]},"references-count":41,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2020,7,17]]},"published-print":{"date-parts":[[2020,8,24]]}},"URL":"https:\/\/doi.org\/10.1021\/acs.jcim.0c00415","relation":{},"ISSN":["1549-9596","1549-960X"],"issn-type":[{"value":"1549-9596","type":"print"},{"value":"1549-960X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,17]]}}}