Alexander Tropsha
Zitiert von
Zitiert von
Beware of q2!
A Golbraikh, A Tropsha
Journal of molecular graphics and modelling 20 (4), 269-276, 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
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
Best practices for QSAR model development, validation, and exploitation
A Tropsha
Molecular informatics 29 (6‐7), 476-488, 2010
Deep reinforcement learning for de novo drug design
M Popova, O Isayev, A Tropsha
Science advances 4 (7), eaap7885, 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
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
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
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
QSAR without borders
EN Muratov, J Bajorath, RP Sheridan, IV Tetko, D Filimonov, V Poroikov, ...
Chemical Society Reviews 49 (11), 3525-3564, 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
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
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
Predictive QSAR modeling workflow, model applicability domains, and virtual screening
A Tropsha, A Golbraikh
Current pharmaceutical design 13 (34), 3494-3504, 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
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
Quantitative nanostructure− activity relationship modeling
D Fourches, D Pu, C Tassa, R Weissleder, SY Shaw, RJ Mumper, ...
ACS nano 4 (10), 5703-5712, 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
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
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
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