Beware of q2! A Golbraikh, A Tropsha Journal of molecular graphics and modelling 20 (4), 269-276, 2002 | 4267 | 2002 |
The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models A Tropsha, P Gramatica, VK Gombar QSAR & Combinatorial Science 22 (1), 69-77, 2003 | 2564 | 2003 |
QSAR modeling: where have you been? Where are you going to? A Cherkasov, EN Muratov, D Fourches, A Varnek, II Baskin, M Cronin, ... Journal of medicinal chemistry 57 (12), 4977-5010, 2014 | 1935 | 2014 |
Best practices for QSAR model development, validation, and exploitation A Tropsha Molecular informatics 29 (6‐7), 476-488, 2010 | 1867 | 2010 |
Deep reinforcement learning for de novo drug design M Popova, O Isayev, A Tropsha Science advances 4 (7), eaap7885, 2018 | 1147 | 2018 |
Trust, but verify: on the importance of chemical structure curation in cheminformatics and QSAR modeling research D Fourches, E Muratov, A Tropsha Journal of chemical information and modeling 50 (7), 1189, 2010 | 804 | 2010 |
Rational selection of training and test sets for the development of validated QSAR models A Golbraikh, M Shen, Z Xiao, YD Xiao, KH Lee, A Tropsha Journal of computer-aided molecular design 17, 241-253, 2003 | 802 | 2003 |
Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection A Golbraikh, A Tropsha Molecular diversity 5, 231-243, 2000 | 778 | 2000 |
Beware of R2: Simple, Unambiguous Assessment of the Prediction Accuracy of QSAR and QSPR Models DLJ Alexander, A Tropsha, DA Winkler Journal of chemical information and modeling 55 (7), 1316-1322, 2015 | 667 | 2015 |
QSAR without borders EN Muratov, J Bajorath, RP Sheridan, IV Tetko, D Filimonov, V Poroikov, ... Chemical Society Reviews 49 (11), 3525-3564, 2020 | 631 | 2020 |
Universal fragment descriptors for predicting properties of inorganic crystals O Isayev, C Oses, C Toher, E Gossett, S Curtarolo, A Tropsha Nature communications 8 (1), 15679, 2017 | 609 | 2017 |
Chemical basis of interactions between engineered nanoparticles and biological systems Q Mu, G Jiang, L Chen, H Zhou, D Fourches, A Tropsha, B Yan Chemical reviews 114 (15), 7740-7781, 2014 | 594 | 2014 |
Novel Variable Selection Quantitative Structure−Property Relationship Approach Based on the k-Nearest-Neighbor Principle W Zheng, A Tropsha Journal of chemical information and computer sciences 40 (1), 185-194, 2000 | 567 | 2000 |
Predictive QSAR modeling workflow, model applicability domains, and virtual screening A Tropsha, A Golbraikh Current pharmaceutical design 13 (34), 3494-3504, 2007 | 505 | 2007 |
Autoimmunity is triggered by cPR-3 (105–201), a protein complementary to human autoantigen proteinase-3 WF Pendergraft III, GA Preston, RR Shah, A Tropsha, CW Carter Jr, ... Nature medicine 10 (1), 72-79, 2004 | 466 | 2004 |
Critical Assessment of QSAR Models of Environmental Toxicity against Tetrahymena pyriformis: Focusing on Applicability Domain and Overfitting by Variable … IV Tetko, I Sushko, AK Pandey, H Zhu, A Tropsha, E Papa, T Oberg, ... Journal of chemical information and modeling 48 (9), 1733-1746, 2008 | 445 | 2008 |
Quantitative nanostructure− activity relationship modeling D Fourches, D Pu, C Tassa, R Weissleder, SY Shaw, RJ Mumper, ... ACS nano 4 (10), 5703-5712, 2010 | 401 | 2010 |
Cross-validated R2-guided region selection for comparative molecular field analysis: a simple method to achieve consistent results SJ Cho, A Tropsha Journal of medicinal chemistry 38 (7), 1060-1066, 1995 | 364 | 1995 |
Comprehensive characterization of the published kinase inhibitor set JM Elkins, V Fedele, M Szklarz, KR Abdul Azeez, E Salah, J Mikolajczyk, ... Nature biotechnology 34 (1), 95-103, 2016 | 343 | 2016 |
Combinatorial QSAR modeling of chemical toxicants tested against Tetrahymena pyriformis H Zhu, A Tropsha, D Fourches, A Varnek, E Papa, P Gramatica, T Oberg, ... Journal of chemical information and modeling 48 (4), 766-784, 2008 | 338 | 2008 |