[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119997-en":3,"doc-seo-119997-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119997,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Predicted Binders for MadR from Machine Learning Applied to Affinity Screening Data","Machine learning models are trained using datasets from an affinity screening experiment. The trained models are then applied to rank compounds drawn from commercially available compound collections. Based on the resulting predictions, the article compiles lists of compounds identified as predicted binders for MadR, presenting them in structured tables that include chemical structures, SMILES representations, and associated vendor information for each entry.","Technical Disclosure Commons  \nDefensive Publications Series  \nDecember 2023  \nPredicted Binders for MadR from Machine Learning Applied to Affinity Screening Data  \nAnonymous  \nFollow this and additional works at: [https://www.tdcommons.org/dpubs_series](https://www.tdcommons.org/dpubs_series)  \nRecommended Citation  \nAnonymous, \"Predicted Binders for MadR from Machine Learning Applied to Affinity Screening Data\", Technical Disclosure Commons,(December 14, 2023)  \n[https://www.tdcommons.org/dpubs_series/6495](https://www.tdcommons.org/dpubs_series/6495)  \nThis work is licensed under a Creative Commons Attribution 4.0 License.  \nThis Article is brought to you for free and open access by Technical Disclosure Commons. It has been accepted for inclusion in Defensive Publications Series by an authorized administrator of Technical Disclosure Commons.  \nPredicted Binders for MadR from Machine Learning Applied  \nto A􀀎nity Screening Data  \nAnonymous  \nDecember 12, 2023  \nAbstract  \nWe used datasets from a􀀎nity screening to train machine learning models. The models were then used to rank compounds from commercially available compound collections to generate the following lists of predicted binders.  \n1 MadR closed con􀀌guration, Enamine In-Stock  \n\n| Chemical Structure | SMILES | Vendor |\n| --- | --- | --- |\n| \u003Cbr> | CC(=O)N(C)c1ccc(cc1) NS(=O)(=O)c2cccc3c2cccn3\u003Cbr>Cc1cc(sc1c2cccc(c2)C\u003Cbr>l)C(=O)N3CCC(CC3)N(C)C(=O)C\u003Cbr>COc1cccc(c1)N2CCC(C2 )CNC(=O)c3cc(c[nH]3)Cl\u003Cbr>Cc1ccc(cc1)Cn2cc(c(n 2)c3ccc(cc3)C)C[NH2+]C\u003Cbr>CC(c1nnc(o1)c2cccc(c\u003Cbr>2)Cl)[NH+]3CCN(CC3)c4ccccc4O\u003Cbr>c1ccc(cc1)C2=CCN(CC2 )C(=O)CN3CCSc4c3cccc4 | Enamine\u003Cbr>Z1022650560\u003Cbr>Enamine\u003Cbr>Z1023095262\u003Cbr>Enamine\u003Cbr>Z1024155598\u003Cbr>Enamine\u003Cbr>Z102767968\u003Cbr>Enamine\u003Cbr>Z104818742\u003Cbr>Enamine\u003Cbr>Z105335562 |\n\n1  \nPublished by Technical Disclosure Commons, 2023 2  \n\n| Chemical Structure | SMILES | Vendor |\n| --- | --- | --- |\n| \u003Cbr> | c1cc2c(cc1OC(F)F)CCC\u003Cbr>C2NC(=O)c3ccc(nc3)C(=O)[O-]\u003Cbr>c1cc(ccc1C2(CCOCC2)N\u003Cbr>C(=O)c3ccc(nc3)C(=O)[O-])Cl\u003Cbr>CC1(COC(CN1C(=O)c2ccc(nc2)C(=O)[O-])c3ccccc3)C\u003Cbr>C[NH+]1CCC(CC1)CN(C) S(=O)(=O)c2ccc(cc2)Br\u003Cbr>CCN(C1CCSc2c1cc(cc2)Br)C(=O)C\u003Cbr>c1ccc2c(c1)c(c([nH]2\u003Cbr>)C(=O)NCCCCNc3ccccn3)Cl\u003Cbr>c1ccc2c(c1)c(c[nH]2) C3=CCN(CC3)C(=O)CCCc 4ccc5c(c4)CC(=O)N5\u003Cbr>Cc1cccnc1CNC(=O)N2CC N(CC2)S(=O)(=O)C\u003Cbr>c1cc(cc(c1)OCc2ccc(cc2)Cl)N3CC[NH2+]CC3\u003Cbr>Cc1c(nc(s1)c2cccc(c2\u003Cbr>)O)c3ccc(cc3)NS(=O)(=O)C | Enamine\u003Cbr>Z1095734842\u003Cbr>Enamine\u003Cbr>Z1095756683\u003Cbr>Enamine\u003Cbr>Z1095779828\u003Cbr>Enamine\u003Cbr>Z1101450202\u003Cbr>Enamine\u003Cbr>Z1118909575\u003Cbr>Enamine\u003Cbr>Z1127008617\u003Cbr>Enamine\u003Cbr>Z1127851551\u003Cbr>Enamine\u003Cbr>Z1133443417\u003Cbr>Enamine\u003Cbr>Z1167043931\u003Cbr>Enamine\u003Cbr>Z1168422974 |\n\n2  \n[https://www.tdcommons.org/dpubs_series/6495](https://www.tdcommons.org/dpubs_series/6495) 3  \n\n| Chemical Structure | SMILES | Vendor |\n| --- | --- | --- |\n| \u003Cbr> | c1cc(cc(c1)C(F)(F)F)\u003Cbr>C[NH+]2CCN(CC2)S(=O)(=O)c3ccc(s3)CC(=O)[O-]\u003Cbr>CN(C)c1ccc(cc1)c2nc(\u003Cbr>cs2)c3ccc(cc3)NS(=O)(=O)C\u003Cbr>c1ccc2c(c1)c(ccn2)Oc 3ccc(cc3)NC(=O)c4ccc(nc4)C(=O)[O-]\u003Cbr>c1ccc(cc1)c2ccc(cc2)\u003Cbr>C(=O)Nc3ccc4c(c3)CC(=O)N4\u003Cbr>c1cc(cc(c1)OC(F)(F)F )N2CCC(C2)NC(=O)c3ccc(nc3)C(=O)[O-]\u003Cbr>c1cc2c(ccc(c2nc1)C(=\u003Cbr>O)NC3CCc4c(cn[nH]4)C3)Cl\u003Cbr>C[NH+]1CCCC(C1)CN(C) C(=O)c2ccc(s2)c3ccccc3\u003Cbr>CC(C1CC[NH+](CC1)C)NC(=O)c2cc3c(ccc(c3[nH]2)F)F\u003Cbr>COc1cccc(n1)CNC(=O)N 2CCC(=CC2)c3cccs3\u003Cbr>Cc1ccc(nn1)N2CCCC(C2 )NC(=O)c3c(c4ccccc4[nH]3)Cl | Enamine\u003Cbr>Z118429180\u003Cbr>Enamine\u003Cbr>Z118605278\u003Cbr>Enamine\u003Cbr>Z1192656646\u003Cbr>Enamine\u003Cbr>Z1198958800\u003Cbr>Enamine\u003Cbr>Z1204248476\u003Cbr>Enamine\u003Cbr>Z1204917877\u003Cbr>Enamine\u003Cbr>Z1212243747\u003Cbr>Enamine\u003Cbr>Z1222844917\u003Cbr>Enamine\u003Cbr>Z1227138044\u003Cbr>Enamine\u003Cbr>Z1232268580 |\n\n3  \nPublished by Technical Disclosure Commons, 2023 4  \n\n| Chemical Structure |  |  | SMILES | Vendor |\n| --- | --- | --- | --- | --- |\n| \u003Cbr> |  |  | CCOC1CC(C12CCC2)NC(= O)c3ccc(nc3)C(=O)[O-]\u003Cbr>CC(=O)N(Cc1ccccc1)C2 CCOc3c2cc(cc3)OC\u003Cbr>Cc1ccc(c(c1)O)C(=O)Nc2cccc(c2)c3nnc(o3)C\u003Cbr>Cc1ccccc1C2CN(C(CO2)\u003Cbr>C)C(=O)c3ccc(nc3)C(=O)[O-]\u003Cbr>c1ccc(cc1)CCCOC2CCN(\u003Cbr>C2)C(=O)c3ccc(nc3","cbCairi7TnE7eb6r","https://ap.wps.com/l/cbCairi7TnE7eb6r","pdf",3828459,1,202,"English","en",105,"# Abstract\n# Predicted Binders for MadR\n## MadR closed configuration, Enamine In-Stock\n## Additional predicted binder lists","[{\"question\":\"What data source is used to train the machine learning models?\",\"answer\":\"Datasets from an affinity screening are used to train the machine learning models.\"},{\"question\":\"How are the predicted binders generated from the models?\",\"answer\":\"The trained models rank compounds from commercially available compound collections, and the top-ranked results are compiled as predicted binders.\"},{\"question\":\"What information is provided for each predicted binder?\",\"answer\":\"Each entry is presented with chemical structure information, SMILES strings, and the vendor associated with the compound.\"}]","Predicted Binders for MadR from Machine Learning Applied to Affinity Screening Data | 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