| Year |
Citation |
Score |
| 2024 |
Melo-Filho CC, Su G, Liu K, Muratov EN, Tropsha A, Liu J. Modeling interactions between Heparan Sulfate and proteins based on the Heparan Sulfate microarray analysis. Glycobiology. PMID 38836441 DOI: 10.1093/glycob/cwae039 |
0.31 |
|
| 2024 |
Rath M, Wellnitz J, Martin HJ, Melo-Filho C, Hochuli JE, Silva GM, Beasley JM, Travis M, Sessions ZL, Popov KI, Zakharov AV, Cherkasov A, Alves V, Muratov EN, Tropsha A. Pharmacokinetics Profiler (PhaKinPro): Model Development, Validation, and Implementation as a Web Tool for Triaging Compounds with Undesired Pharmacokinetics Profiles. Journal of Medicinal Chemistry. PMID 38568752 DOI: 10.1021/acs.jmedchem.3c02446 |
0.327 |
|
| 2023 |
Brocidiacono M, Francoeur P, Aggarwal R, Popov KI, Koes DR, Tropsha A. BigBind: Learning from Nonstructural Data for Structure-Based Virtual Screening. Journal of Chemical Information and Modeling. PMID 38113513 DOI: 10.1021/acs.jcim.3c01211 |
0.314 |
|
| 2023 |
Hajjo R, Momani E, Sabbah DA, Baker N, Tropsha A. Identifying a causal link between prolactin signaling pathways and COVID-19 vaccine-induced menstrual changes. Npj Vaccines. 8: 129. PMID 37658087 DOI: 10.1038/s41541-023-00719-6 |
0.668 |
|
| 2022 |
Hajjo R, Sabbah DA, Tropsha A. Analyzing the Systems Biology Effects of COVID-19 mRNA Vaccines to Assess Their Safety and Putative Side Effects. Pathogens (Basel, Switzerland). 11. PMID 35889989 DOI: 10.3390/pathogens11070743 |
0.669 |
|
| 2022 |
Khashan R, Tropsha A, Zheng W. Data Mining Meets Machine Learning: A Novel ANN-based Multi-body Interaction Docking Scoring Function (MBI-score) based on Utilizing Frequent Geometric and Chemical Patterns of Interfacial Atoms in Native Protein-ligand Complexes. Molecular Informatics. e2100248. PMID 35142086 DOI: 10.1002/minf.202100248 |
0.78 |
|
| 2021 |
Hajjo R, Sabbah DA, Bardaweel SK, Tropsha A. Shedding the Light on Post-Vaccine Myocarditis and Pericarditis in COVID-19 and Non-COVID-19 Vaccine Recipients. Vaccines. 9. PMID 34696294 DOI: 10.3390/vaccines9101186 |
0.659 |
|
| 2021 |
Mansouri K, Karmaus AL, Fitzpatrick J, Patlewicz G, Pradeep P, Alberga D, Alepee N, Allen TEH, Allen D, Alves VM, Andrade CH, Auernhammer TR, Ballabio D, Bell S, Benfenati E, ... ... Tropsha A, et al. Erratum: CATMoS: Collaborative Acute Toxicity Modeling Suite. Environmental Health Perspectives. 129: 109001. PMID 34647794 DOI: 10.1289/EHP10369 |
0.636 |
|
| 2021 |
Mansouri K, Karmaus A, Fitzpatrick J, Patlewicz G, Pradeep P, Alberga D, Alepee N, Allen TEH, Allen D, Alves VM, Andrade CH, Auernhammer TR, Ballabio D, Bell S, Benfenati E, ... ... Tropsha A, et al. Erratum: CATMoS: Collaborative Acute Toxicity Modeling Suite. Environmental Health Perspectives. 129: 79001. PMID 34242083 DOI: 10.1289/EHP9883 |
0.751 |
|
| 2021 |
Mansouri K, Karmaus AL, Fitzpatrick J, Patlewicz G, Pradeep P, Alberga D, Alepee N, Allen TEH, Allen D, Alves VM, Andrade CH, Auernhammer TR, Ballabio D, Bell S, Benfenati E, ... ... Tropsha A, et al. CATMoS: Collaborative Acute Toxicity Modeling Suite. Environmental Health Perspectives. 129: 47013. PMID 33929906 DOI: 10.1289/EHP8495 |
0.656 |
|
| 2021 |
Hajjo R, Sabbah DA, Bardaweel SK, Tropsha A. Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML). Diagnostics (Basel, Switzerland). 11. PMID 33919342 DOI: 10.3390/diagnostics11050742 |
0.667 |
|
| 2021 |
Jain S, Siramshetty VB, Alves VM, Muratov EN, Kleinstreuer N, Tropsha A, Nicklaus MC, Simeonov A, Zakharov AV. Large-Scale Modeling of Multispecies Acute Toxicity End Points Using Consensus of Multitask Deep Learning Methods. Journal of Chemical Information and Modeling. PMID 33533614 DOI: 10.1021/acs.jcim.0c01164 |
0.315 |
|
| 2021 |
Alves VM, Bobrowski T, Melo-Filho CC, Korn D, Auerbach S, Schmitt C, Muratov EN, Tropsha A. QSAR Modeling of SARS-CoV M Inhibitors Identifies Sufugolix, Cenicriviroc, Proglumetacin, and other Drugs as Candidates for Repurposing against SARS-CoV-2. Molecular Informatics. 40: e2000113. PMID 33405340 DOI: 10.1002/Minf.202000113 |
0.406 |
|
| 2020 |
Hajjo R, Tropsha A. A Systems Biology Workflow for Drug and Vaccine Repurposing: Identifying Small-Molecule BCG Mimics to Reduce or Prevent COVID-19 Mortality. Pharmaceutical Research. 37: 212. PMID 33025261 DOI: 10.1007/s11095-020-02930-9 |
0.667 |
|
| 2020 |
Alves VM, Capuzzi SJ, Braga R, Korn D, Hochuli J, Bowler K, Yasgar A, Rai G, Simeonov A, Muratov EN, Zakharov AV, Tropsha A. SCAM Detective: Accurate Predictor of Small, Colloidally-Aggregating Molecules. Journal of Chemical Information and Modeling. PMID 32678597 DOI: 10.1021/Acs.Jcim.0C00415 |
0.315 |
|
| 2020 |
Borba J, Braga RC, Alves VM, Muratov EN, Kleinstreuer NC, Tropsha A, Andrade CH. Pred-Skin: A web portal for accurate prediction of human skin sensitizers. Chemical Research in Toxicology. PMID 32673477 DOI: 10.1021/Acs.Chemrestox.0C00186 |
0.362 |
|
| 2020 |
Baker N, Williams AJ, Tropsha A, Ekins S. Repurposing Quaternary Ammonium Compounds as Potential Treatments for COVID-19. Pharmaceutical Research. 37: 104. PMID 32451736 DOI: 10.1007/S11095-020-02842-8 |
0.331 |
|
| 2020 |
Muratov EN, Bajorath J, Sheridan RP, Tetko IV, Filimonov D, Poroikov V, Oprea TI, Baskin II, Varnek A, Roitberg A, Isayev O, Curtalolo S, Fourches D, Cohen Y, Aspuru-Guzik A, ... ... Tropsha A, et al. QSAR without borders. Chemical Society Reviews. PMID 32356548 DOI: 10.1039/D0Cs00098A |
0.363 |
|
| 2020 |
Afantitis A, Melagraki G, Isigonis P, Tsoumanis A, Varsou DD, Valsami-Jones E, Papadiamantis A, Ellis LA, Sarimveis H, Doganis P, Karatzas P, Tsiros P, Liampa I, Lobaskin V, Greco D, ... ... Tropsha A, et al. NanoSolveIT Project: Driving nanoinformatics research to develop innovative and integrated tools for nanosafety assessment. Computational and Structural Biotechnology Journal. 18: 583-602. PMID 32226594 DOI: 10.1016/J.Csbj.2020.02.023 |
0.312 |
|
| 2020 |
Mansouri K, Kleinstreuer N, Abdelaziz AM, Alberga D, Alves VM, Andersson PL, Andrade CH, Bai F, Balabin I, Ballabio D, Benfenati E, Bhhatarai B, Boyer S, Chen J, Consonni V, ... ... Tropsha A, et al. CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity. Environmental Health Perspectives. 128: 27002. PMID 32074470 DOI: 10.1289/Ehp5580 |
0.682 |
|
| 2019 |
Alves VM, Hwang D, Muratov E, Sokolsky-Papkov M, Varlamova E, Vinod N, Lim C, Andrade CH, Tropsha A, Kabanov A. Cheminformatics-driven discovery of polymeric micelle formulations for poorly soluble drugs. Science Advances. 5: eaav9784. PMID 31249867 DOI: 10.1126/Sciadv.Aav9784 |
0.316 |
|
| 2019 |
Fernandez M, Ban F, Woo G, Isayev O, Perez C, Fokin VV, Tropsha A, Cherkasov A. Quantitative Structure-Price Relationships (QS$R) Modeling and the Development of Economically Feasible Drug Discovery Projects. Journal of Chemical Information and Modeling. PMID 30767528 DOI: 10.1021/Acs.Jcim.8B00747 |
0.394 |
|
| 2018 |
Low Y, Alves VM, Fourches D, Sedykh A, Andrade CH, Muratov EN, Rusyn I, Tropsha A. Chemistry-Wide Association Studies (CWAS): A Novel Framework for Identifying and Interpreting Structure-Activity Relationships. Journal of Chemical Information and Modeling. PMID 30376324 DOI: 10.1021/Acs.Jcim.8B00450 |
0.425 |
|
| 2018 |
Alves VM, Golbraikh A, Capuzzi SJ, Liu K, Lam WI, Korn D, Pozefsky D, Andrade CH, Muratov EN, Tropsha A. Multi-Descriptor Read Across (MuDRA): a simple and transparent approach for developing accurate QSAR models. Journal of Chemical Information and Modeling. PMID 29809005 DOI: 10.1021/Acs.Jcim.8B00124 |
0.416 |
|
| 2018 |
Capuzzi SJ, Sun W, Muratov EN, Martinez-Romero C, He S, Zhu W, Li H, Tawa GJ, Fisher EG, Xu M, Shinn P, Qiu X, García-Sastre A, Zheng W, Tropsha A. Computer-Aided Discovery and Characterization of Novel Ebola Virus Inhibitors. Journal of Medicinal Chemistry. PMID 29624387 DOI: 10.1021/Acs.Jmedchem.8B00035 |
0.351 |
|
| 2018 |
Shen M, Asawa R, Zhang YQ, Cunningham E, Sun H, Tropsha A, Janzen WP, Muratov EN, Capuzzi SJ, Farag S, Jadhav A, Blatt J, Simeonov A, Martinez NJ. Quantitative high-throughput phenotypic screening of pediatric cancer cell lines identifies multiple opportunities for drug repurposing. Oncotarget. 9: 4758-4772. PMID 29435139 DOI: 10.18632/Oncotarget.23462 |
0.335 |
|
| 2018 |
Alves VM, Capuzzi SJ, Braga RC, Borba JVB, Silva AC, Luechtefeld T, Hartung T, Andrade CH, Muratov EN, Tropsha A. A Perspective and a New Integrated Computational Strategy for Skin Sensitization Assessment Acs Sustainable Chemistry & Engineering. 6: 2845-2859. DOI: 10.1021/Acssuschemeng.7B04220 |
0.334 |
|
| 2018 |
Fourches D, Williams AJ, Patlewicz G, Shah I, Grulke C, Wambaugh J, Richard A, Tropsha A. Computational Tools for ADMET Profiling Computational Toxicology. 211-244. DOI: 10.1002/9781119282594.Ch8 |
0.638 |
|
| 2017 |
Arnold KM, Capuzzi SJ, Xu Y, Muratov EN, Carrick K, Szajek AY, Tropsha A, Liu J. Modernization of Enoxaparin Molecular Weight Determination Using Homogeneous Standards. Pharmaceuticals (Basel, Switzerland). 10. PMID 28737679 DOI: 10.3390/Ph10030066 |
0.321 |
|
| 2017 |
Muratov E, Lewis M, Fourches D, Tropsha A, Cox WC. Computer-Assisted Decision Support for Student Admissions Based on Their Predicted Academic Performance. American Journal of Pharmaceutical Education. 81: 46. PMID 28496266 DOI: 10.5688/Ajpe81346 |
0.351 |
|
| 2017 |
Alves VM, Muratov EN, Zakharov A, Muratov NN, Andrade CH, Tropsha A. Chemical toxicity prediction for major classes of industrial chemicals: Is it possible to develop universal models covering cosmetics, drugs, and pesticides? Food and Chemical Toxicology : An International Journal Published For the British Industrial Biological Research Association. PMID 28412406 DOI: 10.1016/J.Fct.2017.04.008 |
0.404 |
|
| 2017 |
Cern A, Marcus D, Tropsha A, Barenholz Y, Goldblum A. New drug candidates for liposomal delivery identified by computer modeling of liposomes' remote loading and leakage. Journal of Controlled Release : Official Journal of the Controlled Release Society. PMID 28215669 DOI: 10.1016/J.Jconrel.2017.02.015 |
0.328 |
|
| 2017 |
Capuzzi SJ, Kim IS, Lam WI, Thornton TE, Muratov EN, Pozefsky D, Tropsha A. Chembench: A Publicly-Accessible, Integrated Cheminformatics Portal. Journal of Chemical Information and Modeling. PMID 28045544 DOI: 10.1021/Acs.Jcim.6B00462 |
0.314 |
|
| 2016 |
Alves VM, Capuzzi SJ, Muratov E, Braga RC, Thornton T, Fourches D, Strickland J, Kleinstreuer N, Andrade CH, Tropsha A. QSAR models of human data can enrich or replace LLNA testing for human skin sensitization. Green Chemistry : An International Journal and Green Chemistry Resource : Gc. 18: 6501-6515. PMID 28630595 DOI: 10.1039/C6Gc01836J |
0.369 |
|
| 2016 |
Alves V, Muratov E, Capuzzi S, Politi R, Low Y, Braga R, Zakharov AV, Sedykh A, Mokshyna E, Farag S, Andrade C, Kuz'min V, Fourches D, Tropsha A. Alarms about structural alerts. Green Chemistry : An International Journal and Green Chemistry Resource : Gc. 18: 4348-4360. PMID 28503093 DOI: 10.1039/C6Gc01492E |
0.42 |
|
| 2016 |
Tetko IV, Maran U, Tropsha A. Public (Q)SAR Services, Integrated Modeling Environments, and Model Repositories on the Web: State of the Art and Perspectives for Future Development. Molecular Informatics. PMID 27778468 DOI: 10.1002/Minf.201600082 |
0.339 |
|
| 2016 |
Luo M, Wang XS, Tropsha A. Comparative Analysis of QSAR-based vs. Chemical Similarity Based Predictors of GPCRs Binding Affinity. Molecular Informatics. 35: 36-41. PMID 27491652 DOI: 10.1002/Minf.201500038 |
0.61 |
|
| 2016 |
Fourches D, Muratov EN, Tropsha A. Trust, But Verify II: A Practical Guide to Chemogenomics Data Curation. Journal of Chemical Information and Modeling. PMID 27280890 DOI: 10.1021/Acs.Jcim.6B00129 |
0.304 |
|
| 2016 |
Mansouri K, Abdelaziz A, Rybacka A, Roncaglioni A, Tropsha A, Varnek A, Zakharov A, Worth A, Richard AM, Grulke CM, Trisciuzzi D, Fourches D, Horvath D, Benfenati E, Muratov E, et al. CERAPP: Collaborative Estrogen Receptor Activity Prediction Project. Environmental Health Perspectives. PMID 26908244 DOI: 10.1289/Ehp.1510267 |
0.673 |
|
| 2016 |
Zakharov AV, Varlamova EV, Lagunin AA, Dmitriev AV, Muratov EN, Fourches D, Kuz'min VE, Poroikov VV, Tropsha A, Nicklaus MC. QSAR Modeling and Prediction of Drug-Drug Interactions. Molecular Pharmaceutics. PMID 26669717 DOI: 10.1021/Acs.Molpharmaceut.5B00762 |
0.374 |
|
| 2016 |
Capuzzi SJ, Politi R, Isayev O, Farag S, Tropsha A. QSAR Modeling of Tox21 Challenge Stress Response and Nuclear Receptor Signaling Toxicity Assays Frontiers in Environmental Science. 4. DOI: 10.3389/Fenvs.2016.00003 |
0.389 |
|
| 2016 |
Luo M, Wang XS, Tropsha A. Comparative Analysis of QSAR-based vs. Chemical Similarity Based Predictors of GPCRs Binding Affinity Molecular Informatics. 35: 36-41. DOI: 10.1002/minf.201500038 |
0.431 |
|
| 2016 |
Borysov P, Hannig J, Marron JS, Muratov E, Fourches D, Tropsha A. Activity prediction and identification of mis-annotated chemical compounds using extreme descriptors Journal of Chemometrics. 30: 99-108. DOI: 10.1002/Cem.2776 |
0.411 |
|
| 2015 |
Braga RC, Alves VM, Silva MF, Muratov E, Fourches D, Lião LM, Tropsha A, Andrade CH. Pred-hERG: A Novel web-Accessible Computational Tool for Predicting Cardiac Toxicity. Molecular Informatics. 34: 698-701. PMID 27490970 DOI: 10.1002/Minf.201500040 |
0.371 |
|
| 2015 |
Baker NC, Fourches D, Tropsha A. Drug Side Effect Profiles as Molecular Descriptors for Predictive Modeling of Target Bioactivity. Molecular Informatics. 34: 160-70. PMID 27490038 DOI: 10.1002/Minf.201400134 |
0.374 |
|
| 2015 |
Fourches D, Pu D, Li L, Zhou H, Mu Q, Su G, Yan B, Tropsha A. Computer-aided design of carbon nanotubes with the desired bioactivity and safety profiles. Nanotoxicology. 1-10. PMID 26525350 DOI: 10.3109/17435390.2015.1073397 |
0.392 |
|
| 2015 |
Low YS, Caster O, Bergvall T, Fourches D, Zang X, Norén GN, Rusyn I, Edwards R, Tropsha A. Cheminformatics-aided pharmacovigilance: application to Stevens-Johnson Syndrome. Journal of the American Medical Informatics Association : Jamia. PMID 26499102 DOI: 10.1093/Jamia/Ocv127 |
0.371 |
|
| 2015 |
Alexander DL, Tropsha A, Winkler DA. Beware of R(2): Simple, Unambiguous Assessment of the Prediction Accuracy of QSAR and QSPR Models. Journal of Chemical Information and Modeling. 55: 1316-22. PMID 26099013 DOI: 10.1021/Acs.Jcim.5B00206 |
0.366 |
|
| 2015 |
Alves VM, Muratov E, Fourches D, Strickland J, Kleinstreuer N, Andrade CH, Tropsha A. Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds. Toxicology and Applied Pharmacology. 284: 262-72. PMID 25560674 DOI: 10.1016/J.Taap.2014.12.014 |
0.383 |
|
| 2015 |
Marlowe T, Figel S, Lenzo F, Golubovskaya V, Kurenova E, Tropsha A, Cance W. Abstract A64: Human epidermal growth factor receptor 2 (HER2) directly binds and activates focal adhesion kinase (FAK) to promote oncogenesis Molecular Cancer Therapeutics. 14. DOI: 10.1158/1535-7163.Targ-15-A64 |
0.305 |
|
| 2014 |
Khashan R, Zheng W, Tropsha A. The Development of Novel Chemical Fragment-Based Descriptors Using Frequent Common Subgraph Mining Approach and Their Application in QSAR Modeling. Molecular Informatics. 33: 201-15. PMID 27485689 DOI: 10.1002/Minf.201300165 |
0.8 |
|
| 2014 |
Politi R, Rusyn I, Tropsha A. Prediction of binding affinity and efficacy of thyroid hormone receptor ligands using QSAR and structure-based modeling methods. Toxicology and Applied Pharmacology. 280: 177-89. PMID 25058446 DOI: 10.1016/J.Taap.2014.07.009 |
0.399 |
|
| 2014 |
Low YS, Sedykh AY, Rusyn I, Tropsha A. Integrative approaches for predicting in vivo effects of chemicals from their structural descriptors and the results of short-term biological assays. Current Topics in Medicinal Chemistry. 14: 1356-64. PMID 24805064 DOI: 10.2174/1568026614666140506121116 |
0.428 |
|
| 2014 |
Braga RC, Alves VM, Silva MF, Muratov E, Fourches D, Tropsha A, Andrade CH. Tuning HERG out: antitarget QSAR models for drug development. Current Topics in Medicinal Chemistry. 14: 1399-415. PMID 24805060 DOI: 10.2174/1568026614666140506124442 |
0.403 |
|
| 2014 |
Thompson CG, Sedykh A, Nicol MR, Muratov E, Fourches D, Tropsha A, Kashuba AD. Short communication: cheminformatics analysis to identify predictors of antiviral drug penetration into the female genital tract. Aids Research and Human Retroviruses. 30: 1058-64. PMID 24512359 DOI: 10.1089/Aid.2013.0254 |
0.389 |
|
| 2014 |
Luo M, Wang XS, Roth BL, Golbraikh A, Tropsha A. Application of quantitative structure-activity relationship models of 5-HT1A receptor binding to virtual screening identifies novel and potent 5-HT1A ligands. Journal of Chemical Information and Modeling. 54: 634-47. PMID 24410373 DOI: 10.1021/Ci400460Q |
0.578 |
|
| 2014 |
Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M, Dearden J, Gramatica P, Martin YC, Todeschini R, Consonni V, Kuz'min VE, Cramer R, Benigni R, Yang C, ... ... Tropsha A, et al. QSAR modeling: where have you been? Where are you going to? Journal of Medicinal Chemistry. 57: 4977-5010. PMID 24351051 DOI: 10.1021/Jm4004285 |
0.386 |
|
| 2014 |
Golbraikh A, Muratov E, Fourches D, Tropsha A. Data set modelability by QSAR. Journal of Chemical Information and Modeling. 54: 1-4. PMID 24251851 DOI: 10.1021/Ci400572X |
0.38 |
|
| 2014 |
Cern A, Barenholz Y, Tropsha A, Goldblum A. Computer-aided design of liposomal drugs: In silico prediction and experimental validation of drug candidates for liposomal remote loading. Journal of Controlled Release : Official Journal of the Controlled Release Society. 173: 125-31. PMID 24184343 DOI: 10.1016/J.Jconrel.2013.10.029 |
0.396 |
|
| 2014 |
Low Y, Sedykh A, Fourches D, Tropsha A. Chemistry-wide association studies (CWAS) to determine joint toxicity effects of co-occurring chemical features Journal of Cheminformatics. 6. DOI: 10.1186/1758-2946-6-S1-P15 |
0.362 |
|
| 2014 |
Fourches D, Tropsha A. Fishing out the signal in polypharmacological high-throughput screening data using novel navigator cheminformatics software Journal of Cheminformatics. 6. DOI: 10.1186/1758-2946-6-S1-P14 |
0.379 |
|
| 2014 |
Khashan R, Zheng W, Tropsha A. The development of novel chemical fragment-based descriptors using frequent common subgraph mining approach and their application in QSAR modeling Molecular Informatics. 33: 201-215. DOI: 10.1002/minf.201300165 |
0.802 |
|
| 2013 |
Low Y, Sedykh A, Fourches D, Golbraikh A, Whelan M, Rusyn I, Tropsha A. Integrative chemical-biological read-across approach for chemical hazard classification. Chemical Research in Toxicology. 26: 1199-208. PMID 23848138 DOI: 10.1021/Tx400110F |
0.403 |
|
| 2013 |
Fourches D, Muratov E, Ding F, Dokholyan NV, Tropsha A. Predicting binding affinity of CSAR ligands using both structure-based and ligand-based approaches. Journal of Chemical Information and Modeling. 53: 1915-22. PMID 23809015 DOI: 10.1021/Ci400216Q |
0.412 |
|
| 2013 |
Zhang L, Sedykh A, Tripathi A, Zhu H, Afantitis A, Mouchlis VD, Melagraki G, Rusyn I, Tropsha A. Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches. Toxicology and Applied Pharmacology. 272: 67-76. PMID 23707773 DOI: 10.1016/J.Taap.2013.04.032 |
0.365 |
|
| 2013 |
Zhu XW, Sedykh A, Zhu H, Liu SS, Tropsha A. The use of pseudo-equilibrium constant affords improved QSAR models of human plasma protein binding. Pharmaceutical Research. 30: 1790-8. PMID 23568522 DOI: 10.1007/S11095-013-1023-6 |
0.435 |
|
| 2013 |
Sedykh A, Fourches D, Duan J, Hucke O, Garneau M, Zhu H, Bonneau P, Tropsha A. Human intestinal transporter database: QSAR modeling and virtual profiling of drug uptake, efflux and interactions. Pharmaceutical Research. 30: 996-1007. PMID 23269503 DOI: 10.1007/S11095-012-0935-X |
0.371 |
|
| 2013 |
Zhang L, Fourches D, Sedykh A, Zhu H, Golbraikh A, Ekins S, Clark J, Connelly MC, Sigal M, Hodges D, Guiguemde A, Guy RK, Tropsha A. Discovery of novel antimalarial compounds enabled by QSAR-based virtual screening. Journal of Chemical Information and Modeling. 53: 475-92. PMID 23252936 DOI: 10.1021/Ci300421N |
0.422 |
|
| 2013 |
Tropsha A. Potential of short-term biological assays to quantitatively predict chronic toxicity Toxicology Letters. 221: S52-S53. DOI: 10.1016/J.Toxlet.2013.06.226 |
0.302 |
|
| 2012 |
Martin TM, Harten P, Young DM, Muratov EN, Golbraikh A, Zhu H, Tropsha A. Does rational selection of training and test sets improve the outcome of QSAR modeling? Journal of Chemical Information and Modeling. 52: 2570-8. PMID 23030316 DOI: 10.1021/Ci300338W |
0.372 |
|
| 2012 |
Tropsha A. Recent trends in statistical QSAR modeling of environmental chemical toxicity. Exs. 101: 381-411. PMID 22945576 DOI: 10.1007/978-3-7643-8340-4_13 |
0.428 |
|
| 2012 |
Khashan R, Zheng W, Tropsha A. Scoring protein interaction decoys using exposed residues (SPIDER): a novel multibody interaction scoring function based on frequent geometric patterns of interfacial residues. Proteins. 80: 2207-17. PMID 22581643 DOI: 10.1002/Prot.24110 |
0.781 |
|
| 2012 |
Hajjo R, Setola V, Roth BL, Tropsha A. Chemocentric informatics approach to drug discovery: identification and experimental validation of selective estrogen receptor modulators as ligands of 5-hydroxytryptamine-6 receptors and as potential cognition enhancers. Journal of Medicinal Chemistry. 55: 5704-19. PMID 22537153 DOI: 10.1021/Jm2011657 |
0.724 |
|
| 2012 |
Rusyn I, Sedykh A, Low Y, Guyton KZ, Tropsha A. Predictive modeling of chemical hazard by integrating numerical descriptors of chemical structures and short-term toxicity assay data. Toxicological Sciences : An Official Journal of the Society of Toxicology. 127: 1-9. PMID 22387746 DOI: 10.1093/Toxsci/Kfs095 |
0.457 |
|
| 2012 |
Tang H, Wang XS, Hsieh JH, Tropsha A. Do crystal structures obviate the need for theoretical models of GPCRs for structure-based virtual screening? Proteins. 80: 1503-21. PMID 22275072 DOI: 10.1002/Prot.24035 |
0.599 |
|
| 2012 |
Lock EF, Abdo N, Huang R, Xia M, Kosyk O, O'Shea SH, Zhou YH, Sedykh A, Tropsha A, Austin CP, Tice RR, Wright FA, Rusyn I. Quantitative high-throughput screening for chemical toxicity in a population-based in vitro model. Toxicological Sciences : An Official Journal of the Society of Toxicology. 126: 578-88. PMID 22268004 DOI: 10.1093/Toxsci/Kfs023 |
0.371 |
|
| 2012 |
Proctor EA, Yin S, Tropsha A, Dokholyan NV. Discrete molecular dynamics distinguishes nativelike binding poses from decoys in difficult targets. Biophysical Journal. 102: 144-51. PMID 22225808 DOI: 10.1016/J.Bpj.2011.11.4008 |
0.385 |
|
| 2012 |
Cern A, Golbraikh A, Sedykh A, Tropsha A, Barenholz Y, Goldblum A. Quantitative structure-property relationship modeling of remote liposome loading of drugs. Journal of Controlled Release : Official Journal of the Controlled Release Society. 160: 147-57. PMID 22154932 DOI: 10.1016/J.Jconrel.2011.11.029 |
0.35 |
|
| 2012 |
Hsieh JH, Yin S, Wang XS, Liu S, Dokholyan NV, Tropsha A. Cheminformatics meets molecular mechanics: a combined application of knowledge-based pose scoring and physical force field-based hit scoring functions improves the accuracy of structure-based virtual screening. Journal of Chemical Information and Modeling. 52: 16-28. PMID 22017385 DOI: 10.1021/Ci2002507 |
0.566 |
|
| 2011 |
Samarov D, Marron JS, Liu Y, Grulke C, Tropsha A. LOCAL KERNEL CANONICAL CORRELATION ANALYSIS WITH APPLICATION TO VIRTUAL DRUG SCREENING. The Annals of Applied Statistics. 5: 2169-2196. PMID 22121408 DOI: 10.1214/11-Aoas472 |
0.696 |
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| 2011 |
Fleishman SJ, Whitehead TA, Strauch EM, Corn JE, Qin S, Zhou HX, Mitchell JC, Demerdash ON, Takeda-Shitaka M, Terashi G, Moal IH, Li X, Bates PA, Zacharias M, Park H, ... ... Tropsha A, et al. Community-wide assessment of protein-interface modeling suggests improvements to design methodology. Journal of Molecular Biology. 414: 289-302. PMID 22001016 DOI: 10.1016/J.Jmb.2011.09.031 |
0.771 |
|
| 2011 |
Hsieh JH, Yin S, Liu S, Sedykh A, Dokholyan NV, Tropsha A. Combined application of cheminformatics- and physical force field-based scoring functions improves binding affinity prediction for CSAR data sets. Journal of Chemical Information and Modeling. 51: 2027-35. PMID 21780807 DOI: 10.1021/Ci200146E |
0.612 |
|
| 2011 |
Artemenko AG, Muratov EN, Kuz’min VE, Muratov NN, Varlamova EV, Kuz’mina AV, Gorb LG, Golius A, Hill FC, Leszczynski J, Tropsha A. QSAR analysis of the toxicity of nitroaromatics in Tetrahymena pyriformis: structural factors and possible modes of action. Sar and Qsar in Environmental Research. 22: 575-601. PMID 21714735 DOI: 10.1080/1062936X.2011.569950 |
0.405 |
|
| 2011 |
Low Y, Uehara T, Minowa Y, Yamada H, Ohno Y, Urushidani T, Sedykh A, Muratov E, Kuz'min V, Fourches D, Zhu H, Rusyn I, Tropsha A. Predicting drug-induced hepatotoxicity using QSAR and toxicogenomics approaches. Chemical Research in Toxicology. 24: 1251-62. PMID 21699217 DOI: 10.1021/Tx200148A |
0.423 |
|
| 2011 |
Fourches D, Pu D, Tropsha A. Exploring quantitative nanostructure-activity relationships (QNAR) modeling as a tool for predicting biological effects of manufactured nanoparticles. Combinatorial Chemistry & High Throughput Screening. 14: 217-25. PMID 21275889 DOI: 10.2174/138620711794728743 |
0.416 |
|
| 2011 |
Ebalunode JO, Zheng W, Tropsha A. Application of QSAR and shape pharmacophore modeling approaches for targeted chemical library design. Methods in Molecular Biology (Clifton, N.J.). 685: 111-33. PMID 20981521 DOI: 10.1007/978-1-60761-931-4_6 |
0.413 |
|
| 2011 |
Sedykh A, Zhu H, Tang H, Zhang L, Richard A, Rusyn I, Tropsha A. Use of in vitro HTS-derived concentration-response data as biological descriptors improves the accuracy of QSAR models of in vivo toxicity. Environmental Health Perspectives. 119: 364-70. PMID 20980217 DOI: 10.1289/Ehp.1002476 |
0.421 |
|
| 2011 |
Oprea TI, May EE, Leitão A, Tropsha A. Computational systems chemical biology. Methods in Molecular Biology (Clifton, N.J.). 672: 459-88. PMID 20838980 DOI: 10.1007/978-1-60761-839-3_18 |
0.336 |
|
| 2011 |
Tropsha A, Golbraikh A, Cho WJ. Development of kNN QSAR models for 3-arylisoquinoline antitumor agents Bulletin of the Korean Chemical Society. 32: 2397-2404. DOI: 10.5012/Bkcs.2011.32.7.2397 |
0.386 |
|
| 2010 |
Tropsha A. Best Practices for QSAR Model Development, Validation, and Exploitation. Molecular Informatics. 29: 476-88. PMID 27463326 DOI: 10.1002/Minf.201000061 |
0.432 |
|
| 2010 |
Sushko I, Novotarskyi S, Körner R, Pandey AK, Cherkasov A, Li J, Gramatica P, Hansen K, Schroeter T, Müller KR, Xi L, Liu H, Yao X, Öberg T, Hormozdiari F, ... ... Tropsha A, et al. Applicability domains for classification problems: Benchmarking of distance to models for Ames mutagenicity set. Journal of Chemical Information and Modeling. 50: 2094-111. PMID 21033656 DOI: 10.1021/Ci100253R |
0.43 |
|
| 2010 |
Hajjo R, Grulke CM, Golbraikh A, Setola V, Huang XP, Roth BL, Tropsha A. Development, validation, and use of quantitative structure-activity relationship models of 5-hydroxytryptamine (2B) receptor ligands to identify novel receptor binders and putative valvulopathic compounds among common drugs. Journal of Medicinal Chemistry. 53: 7573-86. PMID 20958049 DOI: 10.1021/Jm100600Y |
0.784 |
|
| 2010 |
Walker T, Grulke CM, Pozefsky D, Tropsha A. Chembench: a cheminformatics workbench. Bioinformatics (Oxford, England). 26: 3000-1. PMID 20889496 DOI: 10.1093/Bioinformatics/Btq556 |
0.697 |
|
| 2010 |
Fourches D, Pu D, Tassa C, Weissleder R, Shaw SY, Mumper RJ, Tropsha A. Quantitative nanostructure-activity relationship modeling. Acs Nano. 4: 5703-12. PMID 20857979 DOI: 10.1021/Nn1013484 |
0.423 |
|
| 2010 |
Fourches D, Muratov E, Tropsha A. Trust, but verify: on the importance of chemical structure curation in cheminformatics and QSAR modeling research. Journal of Chemical Information and Modeling. 50: 1189-204. PMID 20572635 DOI: 10.1021/Ci100176X |
0.42 |
|
| 2010 |
Rodgers AD, Zhu H, Fourches D, Rusyn I, Tropsha A. Modeling liver-related adverse effects of drugs using knearest neighbor quantitative structure-activity relationship method. Chemical Research in Toxicology. 23: 724-32. PMID 20192250 DOI: 10.1021/Tx900451R |
0.367 |
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| 2010 |
Fourches D, Barnes JC, Day NC, Bradley P, Reed JZ, Tropsha A. Cheminformatics analysis of assertions mined from literature that describe drug-induced liver injury in different species. Chemical Research in Toxicology. 23: 171-83. PMID 20014752 DOI: 10.1021/Tx900326K |
0.369 |
|
| 2010 |
Tropsha A. Recent Advances in Development, Validation, and Exploitation of QSAR Models Burger's Medicinal Chemistry and Drug Discovery. 505-534. DOI: 10.1002/0471266949.Bmc002.Pub2 |
0.406 |
|
| 2009 |
Zhu H, Martin TM, Ye L, Sedykh A, Young DM, Tropsha A. Quantitative structure-activity relationship modeling of rat acute toxicity by oral exposure. Chemical Research in Toxicology. 22: 1913-21. PMID 19845371 DOI: 10.1021/Tx900189P |
0.445 |
|
| 2009 |
Zhu H, Ye L, Richard A, Golbraikh A, Wright FA, Rusyn I, Tropsha A. A novel two-step hierarchical quantitative structure-activity relationship modeling work flow for predicting acute toxicity of chemicals in rodents. Environmental Health Perspectives. 117: 1257-64. PMID 19672406 DOI: 10.1289/Ehp.0800471 |
0.435 |
|
| 2009 |
Bandyopadhyay D, Huan J, Prins J, Snoeyink J, Wang W, Tropsha A. Identification of family-specific residue packing motifs and their use for structure-based protein function prediction: II. Case studies and applications. Journal of Computer-Aided Molecular Design. 23: 785-97. PMID 19548090 DOI: 10.1007/S10822-009-9277-0 |
0.355 |
|
| 2009 |
Peterson YK, Wang XS, Casey PJ, Tropsha A. Discovery of geranylgeranyltransferase-I inhibitors with novel scaffolds by the means of quantitative structure-activity relationship modeling, virtual screening, and experimental validation. Journal of Medicinal Chemistry. 52: 4210-20. PMID 19537691 DOI: 10.1021/Jm8013772 |
0.755 |
|
| 2009 |
Tang H, Wang XS, Huang XP, Roth BL, Butler KV, Kozikowski AP, Jung M, Tropsha A. Novel inhibitors of human histone deacetylase (HDAC) identified by QSAR modeling of known inhibitors, virtual screening, and experimental validation. Journal of Chemical Information and Modeling. 49: 461-76. PMID 19182860 DOI: 10.1021/Ci800366F |
0.442 |
|
| 2008 |
Tetko IV, Sushko I, Pandey AK, Zhu H, Tropsha A, Papa E, Oberg T, Todeschini R, Fourches D, Varnek A. Critical assessment of QSAR models of environmental toxicity against Tetrahymena pyriformis: focusing on applicability domain and overfitting by variable selection. Journal of Chemical Information and Modeling. 48: 1733-46. PMID 18729318 DOI: 10.1021/Ci800151M |
0.424 |
|
| 2008 |
Zhang L, Zhu H, Oprea TI, Golbraikh A, Tropsha A. QSAR modeling of the blood-brain barrier permeability for diverse organic compounds. Pharmaceutical Research. 25: 1902-14. PMID 18553217 DOI: 10.1007/S11095-008-9609-0 |
0.426 |
|
| 2008 |
Zhang S, Kaplan AH, Tropsha A. HIV-1 protease function and structure studies with the simplicial neighborhood analysis of protein packing method. Proteins. 73: 742-53. PMID 18498108 DOI: 10.1002/Prot.22094 |
0.557 |
|
| 2008 |
Wang XS, Tang H, Golbraikh A, Tropsha A. Combinatorial QSAR modeling of specificity and subtype selectivity of ligands binding to serotonin receptors 5HT1E and 5HT1F. Journal of Chemical Information and Modeling. 48: 997-1013. PMID 18470978 DOI: 10.1021/Ci700404C |
0.434 |
|
| 2008 |
Zhu H, Rusyn I, Richard A, Tropsha A. Use of cell viability assay data improves the prediction accuracy of conventional quantitative structure-activity relationship models of animal carcinogenicity. Environmental Health Perspectives. 116: 506-13. PMID 18414635 DOI: 10.1289/Ehp.10573 |
0.373 |
|
| 2008 |
Karthikeyan M, Krishnan S, Pandey AK, Bender A, Tropsha A. Distributed chemical computing using ChemStar: an open source java remote method invocation architecture applied to large scale molecular data from PubChem. Journal of Chemical Information and Modeling. 48: 691-703. PMID 18402434 DOI: 10.1021/Ci700334F |
0.307 |
|
| 2008 |
Hsieh JH, Wang XS, Teotico D, Golbraikh A, Tropsha A. Differentiation of AmpC beta-lactamase binders vs. decoys using classification kNN QSAR modeling and application of the QSAR classifier to virtual screening. Journal of Computer-Aided Molecular Design. 22: 593-609. PMID 18338225 DOI: 10.1007/S10822-008-9199-2 |
0.654 |
|
| 2008 |
Zhu H, Tropsha A, Fourches D, Varnek A, Papa E, Gramatica P, Oberg T, Dao P, Cherkasov A, Tetko IV. Combinatorial QSAR modeling of chemical toxicants tested against Tetrahymena pyriformis. Journal of Chemical Information and Modeling. 48: 766-84. PMID 18311912 DOI: 10.1021/Ci700443V |
0.451 |
|
| 2008 |
Tetko IV, Tropsha A, Zhu H, Papa E, Gramatica P, Öberg T, Fourches D, Varnek A. Comparison of applicability domains of QSAR models: application to the modelling of the environmental toxicity against Tetrahymena pyriformis Chemistry Central Journal. 2. DOI: 10.1186/1752-153X-2-S1-P14 |
0.315 |
|
| 2007 |
Tropsha A, Golbraikh A. Predictive QSAR modeling workflow, model applicability domains, and virtual screening. Current Pharmaceutical Design. 13: 3494-504. PMID 18220786 DOI: 10.2174/138161207782794257 |
0.346 |
|
| 2007 |
Oprea TI, Tropsha A, Faulon JL, Rintoul MD. Systems chemical biology. Nature Chemical Biology. 3: 447-50. PMID 17637771 DOI: 10.1038/Nchembio0807-447 |
0.326 |
|
| 2007 |
Zhang S, Wei L, Bastow K, Zheng W, Brossi A, Lee KH, Tropsha A. Antitumor agents 252. Application of validated QSAR models to database mining: discovery of novel tylophorine derivatives as potential anticancer agents. Journal of Computer-Aided Molecular Design. 21: 97-112. PMID 17340042 DOI: 10.1007/S10822-007-9102-6 |
0.625 |
|
| 2006 |
Tropsha A, Wang SX. QSAR modeling of GPCR ligands: methodologies and examples of applications Ernst Schering Foundation Symposium Proceedings. 49-73. PMID 17703577 |
0.323 |
|
| 2006 |
Votano JR, Parham M, Hall LM, Hall LH, Kier LB, Oloff S, Tropsha A. QSAR modeling of human serum protein binding with several modeling techniques utilizing structure-information representation. Journal of Medicinal Chemistry. 49: 7169-81. PMID 17125269 DOI: 10.1021/Jm051245V |
0.431 |
|
| 2006 |
Zhang S, Golbraikh A, Oloff S, Kohn H, Tropsha A. A novel automated lazy learning QSAR (ALL-QSAR) approach: method development, applications, and virtual screening of chemical databases using validated ALL-QSAR models. Journal of Chemical Information and Modeling. 46: 1984-95. PMID 16995729 DOI: 10.1021/Ci060132X |
0.615 |
|
| 2006 |
Ghoneim OM, Legere JA, Golbraikh A, Tropsha A, Booth RG. Novel ligands for the human histamine H1 receptor: synthesis, pharmacology, and comparative molecular field analysis studies of 2-dimethylamino-5-(6)-phenyl-1,2,3,4-tetrahydronaphthalenes. Bioorganic & Medicinal Chemistry. 14: 6640-58. PMID 16782354 DOI: 10.1016/J.Bmc.2006.05.077 |
0.314 |
|
| 2006 |
de Cerqueira Lima P, Golbraikh A, Oloff S, Xiao Y, Tropsha A. Combinatorial QSAR modeling of P-glycoprotein substrates. Journal of Chemical Information and Modeling. 46: 1245-54. PMID 16711744 DOI: 10.1021/Ci0504317 |
0.42 |
|
| 2006 |
Zhang S, Golbraikh A, Tropsha A. Development of quantitative structure-binding affinity relationship models based on novel geometrical chemical descriptors of the protein-ligand interfaces. Journal of Medicinal Chemistry. 49: 2713-24. PMID 16640331 DOI: 10.1021/Jm050260X |
0.584 |
|
| 2006 |
Oloff S, Zhang S, Sukumar N, Breneman C, Tropsha A. Chemometric analysis of ligand receptor complementarity: identifying Complementary Ligands Based on Receptor Information (CoLiBRI). Journal of Chemical Information and Modeling. 46: 844-51. PMID 16563016 DOI: 10.1021/Ci050065R |
0.565 |
|
| 2006 |
Tropsha A. Chapter 7 Variable Selection QSAR Modeling, Model Validation, and Virtual Screening Annual Reports in Computational Chemistry. 2: 113-126. DOI: 10.1016/S1574-1400(06)02007-X |
0.412 |
|
| 2006 |
Oprea TI, Tropsha A. Target, chemical and bioactivity databases - integration is key Drug Discovery Today: Technologies. 3: 357-365. DOI: 10.1016/J.Ddtec.2006.12.003 |
0.338 |
|
| 2005 |
Oloff S, Mailman RB, Tropsha A. Application of validated QSAR models of D1 dopaminergic antagonists for database mining. Journal of Medicinal Chemistry. 48: 7322-32. PMID 16279792 DOI: 10.1021/Jm049116M |
0.408 |
|
| 2005 |
Medina-Franco JL, Golbraikh A, Oloff S, Castillo R, Tropsha A. Quantitative structure-activity relationship analysis of pyridinone HIV-1 reverse transcriptase inhibitors using the k nearest neighbor method and QSAR-based database mining. Journal of Computer-Aided Molecular Design. 19: 229-42. PMID 16163450 DOI: 10.1007/S10822-005-4789-8 |
0.419 |
|
| 2005 |
Itskowitz P, Tropsha A. kappa Nearest neighbors QSAR modeling as a variational problem: theory and applications. Journal of Chemical Information and Modeling. 45: 777-85. PMID 15921467 DOI: 10.1021/Ci049628+ |
0.413 |
|
| 2005 |
Kovatcheva A, Golbraikh A, Oloff S, Feng J, Zheng W, Tropsha A. QSAR modeling of datasets with enantioselective compounds using chirality sensitive molecular descriptors. Sar and Qsar in Environmental Research. 16: 93-102. PMID 15844445 DOI: 10.1080/10629360412331319844 |
0.526 |
|
| 2004 |
Votano JR, Parham M, Hall LH, Kier LB, Oloff S, Tropsha A, Xie Q, Tong W. Three new consensus QSAR models for the prediction of Ames genotoxicity. Mutagenesis. 19: 365-77. PMID 15388809 DOI: 10.1093/Mutage/Geh043 |
0.314 |
|
| 2004 |
Xiao Z, Varma S, Xiao YD, Tropsha A. Modeling of p38 mitogen-activated protein kinase inhibitors using the Catalyst HypoGen and k-nearest neighbor QSAR methods. Journal of Molecular Graphics & Modelling. 23: 129-38. PMID 15363455 DOI: 10.1016/J.Jmgm.2004.05.001 |
0.41 |
|
| 2004 |
Ng C, Xiao Y, Putnam W, Lum B, Tropsha A. Quantitative structure-pharmacokinetic parameters relationships (QSPKR) analysis of antimicrobial agents in humans using simulated annealing k-nearest-neighbor and partial least-square analysis methods. Journal of Pharmaceutical Sciences. 93: 2535-44. PMID 15349962 DOI: 10.1002/Jps.20117 |
0.393 |
|
| 2004 |
Sherman DB, Zhang S, Pitner JB, Tropsha A. Evaluation of the relative stability of liganded versus ligand-free protein conformations using Simplicial Neighborhood Analysis of Protein Packing (SNAPP) method. Proteins. 56: 828-38. PMID 15281134 DOI: 10.1002/Prot.20131 |
0.547 |
|
| 2004 |
Shen M, Béguin C, Golbraikh A, Stables JP, Kohn H, Tropsha A. Application of predictive QSAR models to database mining: identification and experimental validation of novel anticonvulsant compounds. Journal of Medicinal Chemistry. 47: 2356-64. PMID 15084134 DOI: 10.1021/Jm030584Q |
0.457 |
|
| 2004 |
Kovatcheva A, Golbraikh A, Oloff S, Xiao YD, Zheng W, Wolschann P, Buchbauer G, Tropsha A. Combinatorial QSAR of ambergris fragrance compounds. Journal of Chemical Information and Computer Sciences. 44: 582-95. PMID 15032539 DOI: 10.1021/Ci034203T |
0.429 |
|
| 2003 |
Tropsha A, Carter CW, Cammer S, Vaisman II. Simplicial neighborhood analysis of protein packing (SNAPP): a computational geometry approach to studying proteins. Methods in Enzymology. 374: 509-44. PMID 14696387 DOI: 10.1016/S0076-6879(03)74022-1 |
0.774 |
|
| 2003 |
Golbraikh A, Shen M, Xiao Z, Xiao YD, Lee KH, Tropsha A. Rational selection of training and test sets for the development of validated QSAR models. Journal of Computer-Aided Molecular Design. 17: 241-53. PMID 13677490 DOI: 10.1023/A:1025386326946 |
0.318 |
|
| 2003 |
Shen M, Xiao Y, Golbraikh A, Gombar VK, Tropsha A. Development and validation of k-nearest-neighbor QSPR models of metabolic stability of drug candidates. Journal of Medicinal Chemistry. 46: 3013-20. PMID 12825940 DOI: 10.1021/Jm020491T |
0.424 |
|
| 2003 |
Tropsha A, Gramatica P, Gombar VK. The importance of being earnest: Validation is the absolute essential for successful application and interpretation of QSPR models Qsar and Combinatorial Science. 22: 69-77. DOI: 10.1002/Qsar.200390007 |
0.42 |
|
| 2003 |
Tropsha A. Recent Trends in Quantitative Structure‐Activity Relationships Burger's Medicinal Chemistry and Drug Discovery. 49-76. DOI: 10.1002/0471266949.Bmc002 |
0.423 |
|
| 2002 |
Golbraikh A, Tropsha A. Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection. Molecular Diversity. 5: 231-43. PMID 12549674 DOI: 10.1023/A:1021372108686 |
0.382 |
|
| 2002 |
Golbraikh A, Tropsha A. Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection. Journal of Computer-Aided Molecular Design. 16: 357-69. PMID 12489684 DOI: 10.1023/A:1020869118689 |
0.382 |
|
| 2002 |
Shen M, LeTiran A, Xiao Y, Golbraikh A, Kohn H, Tropsha A. Quantitative structure-activity relationship analysis of functionalized amino acid anticonvulsant agents using k nearest neighbor and simulated annealing PLS methods. Journal of Medicinal Chemistry. 45: 2811-23. PMID 12061883 DOI: 10.1021/Jm010488U |
0.427 |
|
| 2002 |
Xiao Z, Xiao YD, Feng J, Golbraikh A, Tropsha A, Lee KH. Antitumor agents. 213. Modeling of epipodophyllotoxin derivatives using variable selection k nearest neighbor QSAR method. Journal of Medicinal Chemistry. 45: 2294-309. PMID 12014968 DOI: 10.1021/Jm0105427 |
0.45 |
|
| 2002 |
Tropsha A, Zheng W. Rational principles of compound selection for combinatorial library design. Combinatorial Chemistry & High Throughput Screening. 5: 111-23. PMID 11966420 DOI: 10.2174/1386207024607400 |
0.327 |
|
| 2002 |
Golbraikh A, Tropsha A. Beware of q2! Journal of Molecular Graphics & Modelling. 20: 269-76. PMID 11858635 DOI: 10.1016/S1093-3263(01)00123-1 |
0.385 |
|
| 2001 |
Carter CW, LeFebvre BC, Cammer SA, Tropsha A, Edgell MH. Four-body potentials reveal protein-specific correlations to stability changes caused by hydrophobic core mutations. Journal of Molecular Biology. 311: 625-38. PMID 11518520 DOI: 10.1006/Jmbi.2001.4906 |
0.763 |
|
| 2001 |
Pilger C, Bartolucci C, Lamba D, Tropsha A, Fels G. Accurate prediction of the bound conformation of galanthamine in the active site of Torpedo californica acetylcholinesterase using molecular docking Journal of Molecular Graphics and Modelling. 19: 288-296. PMID 11449566 DOI: 10.1016/S1093-3263(00)00056-5 |
0.367 |
|
| 2001 |
Tropsha A, Zheng W. Identification of the descriptor pharmacophores using variable selection QSAR: Applications to database mining Current Pharmaceutical Design. 7: 599-612. PMID 11375770 DOI: 10.2174/1381612013397834 |
0.403 |
|
| 2001 |
Gan HH, Tropsha A, Schlick T. Lattice protein folding with two and four-body statistical potentials Proteins: Structure, Function and Genetics. 43: 161-174. PMID 11276086 DOI: 10.1002/1097-0134(20010501)43:2<161::Aid-Prot1028>3.0.Co;2-F |
0.323 |
|
| 2001 |
Golbraikh A, Bonchev D, Tropsha A. Novel Chirality Descriptors Derived from Molecular Topology Journal of Chemical Information and Computer Sciences. 41: 147-158. PMID 11206367 DOI: 10.1021/Ci000082A |
0.327 |
|
| 2001 |
Reynolds CH, Tropsha A, Pfahler LB, Druker R, Chakravorty S, Ethiraj G, Zheng W. Diversity and Coverage of Structural Sublibraries Selected Using the SAGE and SCA Algorithms Journal of Chemical Information and Computer Sciences. 41: 1470-1477. DOI: 10.1021/Ci010041U |
0.333 |
|
| 2000 |
Zhang SX, Feng J, Kuo SC, Brossi A, Hamel E, Tropsha A, Lee KH. Antitumor agents. 199. Three-dimensional quantitative structure-activity relationship study of the colchicine binding site ligands using comparative molecular field analysis. Journal of Medicinal Chemistry. 43: 167-76. PMID 10649972 DOI: 10.1021/Jm990333A |
0.637 |
|
| 2000 |
Zheng W, Tropsha A. Novel Variable Selection Quantitative Structure-Property Relationship Approach Based on the k-Nearest-Neighbor Principle Journal of Chemical Information and Computer Sciences. 40: 185-194. DOI: 10.1021/Ci980033M |
0.418 |
|
| 1999 |
O'Connell TM, Wang L, Tropsha A, Hermans J. The "random-coil" state of proteins: comparison of database statistics and molecular simulations. Proteins. 36: 407-18. PMID 10450082 DOI: 10.1002/(Sici)1097-0134(19990901)36:4<407::Aid-Prot4>3.0.Co;2-1 |
0.342 |
|
| 1999 |
Zheng W, Cho SJ, Waller CL, Tropsha A. Rational combinatorial library design. 3. Simulated annealing guided evaluation (SAGE) of molecular diversity: A novel computational tool for universal library design and database mining Journal of Chemical Information and Computer Sciences. 39: 738-746. PMID 10443027 DOI: 10.1021/Ci980103P |
0.311 |
|
| 1998 |
Cho SJ, Zheng W, Tropsha A. Rational combinatorial library design. 2. Rational design of targeted combinatorial peptide libraries using chemical similarity probe and the inverse QSAR approaches Journal of Chemical Information and Computer Sciences. 38: 259-268. PMID 9538521 DOI: 10.1021/Ci9700945 |
0.305 |
|
| 1998 |
Zheng W, Cho SJ, Tropsha A. Rational combinatorial library design. 1. Focus-2D: A new approach to the design of targeted combinatorial chemical libraries Journal of Chemical Information and Computer Sciences. 38: 251-258. PMID 9538520 DOI: 10.1021/Ci970095X |
0.368 |
|
| 1998 |
Tropsha A, Cho SJ. Cross-Validated R2 Guided Region Selection for CoMFA Studies Perspectives in Drug Discovery and Design. 12: 57-69. DOI: 10.1007/0-306-46858-1_4 |
0.392 |
|
| 1998 |
Zheng W, Cho SJ, Tropsha A. Rational design of a targeted combinatorial chemical library with opiatelike activity International Journal of Quantum Chemistry. 69: 65-75. DOI: 10.1002/(Sici)1097-461X(1998)69:1<65::Aid-Qua9>3.0.Co;2-V |
0.369 |
|
| 1996 |
Cho SJ, Garsia ML, Bier J, Tropsha A. Structure-based alignment and comparative molecular field analysis of acetylcholinesterase inhibitors. Journal of Medicinal Chemistry. 39: 5064-71. PMID 8978837 DOI: 10.1021/Jm950771R |
0.334 |
|
| 1996 |
Cho SJ, Tropsha A, Sufness M, Cheng YC, Lee KH. Antitumor agents. 163. Three-dimensional quantitative structure-activity relationship study of 4′-O-demethylepipodophyllotoxin analogs using the modified CoMFA/q2-GRS approach Journal of Medicinal Chemistry. 39: 1383-1395. PMID 8691468 DOI: 10.1021/Jm9503052 |
0.327 |
|
| 1995 |
Hoffman DL, Laiter S, Singh RK, Vaisman II, Tropsha A. Rapid protein structure classification using one-dimensional structure profiles on the BioSCAN parallel computer Bioinformatics. 11: 675-679. PMID 8808584 DOI: 10.1093/Bioinformatics/11.6.675 |
0.333 |
|
| 1995 |
Laiter S, Hoffman DL, Singh RK, Vaisman II, Tropsha A. Pseudotorsional OCCO backbone angle as a single descriptor of protein secondary structure Protein Science. 4: 1633-1643. PMID 8520489 DOI: 10.1002/Pro.5560040821 |
0.319 |
|
| 1995 |
Cho SJ, Tropsha A. Cross-validated R2-guided region selection for comparative molecular field analysis: A simple method to achieve consistent results Journal of Medicinal Chemistry. 38: 1060-1066. PMID 7707309 DOI: 10.1021/Jm00007A003 |
0.318 |
|
| 1994 |
Edgerton MD, Tropsha A, Jones AM. Modelling the auxin-binding site of auxin-binding protein 1 of maize Phytochemistry. 35: 1111-1123. DOI: 10.1016/S0031-9422(00)94807-6 |
0.3 |
|
| 1991 |
Tropsha A, Bowen JP, Brown FK, Kizer JS. Do interhelical side chain-backbone hydrogen bonds participate in formation of leucine zipper coiled coils? Proceedings of the National Academy of Sciences of the United States of America. 88: 9488-92. PMID 1946362 DOI: 10.1073/Pnas.88.21.9488 |
0.304 |
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