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Winkler, D*
Winkler, D*
CSIRO or Monash or La Trobe or Defence or Nottingham or Pharmacy
Verified email at latrobe.edu.au - Homepage
Title
Cited by
Cited by
Year
Bayesian regularization of neural networks
F Burden, D Winkler
Artificial neural networks, 23-42, 2008
5212008
Beware of R2: simple, unambiguous assessment of the prediction accuracy of QSAR and QSPR models – ISI Highly Cited paper
DLJ Alexander, A Tropsha, DA Winkler
Journal of chemical information and modeling 55 (7), 1316-1322, 2015
4682015
Quantitative structure–property relationship modeling of diverse materials properties
T Le, VC Epa, FR Burden, DA Winkler
Chemical reviews 112 (5), 2889-2919, 2012
3952012
QSAR without Borders – ISI Highly Cited Paper: ISI Hot Paper
EN Muratov, J Bajorath, RP Sheridan, I Tetko, D Filimonov, V Poroikov, ...
Chemical Society Reviews 49, 3525, 2020
2742020
Robust QSAR models using Bayesian regularized neural networks
FR Burden, DA Winkler
Journal of medicinal chemistry 42 (16), 3183-3187, 1999
2581999
Consistent concepts of self‐organization and self‐assembly
JD Halley, DA Winkler
Complexity 14 (2), 10-17, 2008
2052008
Artificial neural networks: methods and applications
DJ Livingstone
Humana Press, 2008
2052008
The role of quantitative structure-activity relationships (QSAR) in biomolecular discovery
DA Winkler
Briefings in bioinformatics 3 (1), 73-86, 2002
1902002
Modeling biological activities of nanoparticles
VC Epa, FR Burden, C Tassa, R Weissleder, S Shaw, DA Winkler
Nano letters 12 (11), 5808-5812, 2012
1802012
Applying quantitative structure–activity relationship approaches to nanotoxicology: current status and future potential
DA Winkler, E Mombelli, A Pietroiusti, L Tran, A Worth, B Fadeel, ...
Toxicology 313 (1), 15-23, 2013
1782013
A renaissance of neural networks in drug discovery
II Baskin, D Winkler, IV Tetko
Expert opinion on drug discovery 11 (8), 785-795, 2016
1752016
Design of potential anti-HIV agents. 1. Mannosidase inhibitors
DA Winkler, G Holan
Journal of medicinal chemistry 32 (9), 2084-2089, 1989
1741989
Use of automatic relevance determination in QSAR studies using Bayesian neural networks
FR Burden, MG Ford, DC Whitley, DA Winkler
Journal of Chemical Information and Computer Sciences 40 (6), 1423-1430, 2000
1652000
Opening the black box of neural networks: methods for interpreting neural network models in clinical applications.
Z Zhang, MW Beck, DA Winkler, B Huang, W Sibanda, H Goyal
Ann. Transl. Med. 6 (11), 216, 2018
1562018
Materials for stem cell factories of the future
AD Celiz, JGW Smith, R Langer, DG Anderson, DA Winkler, DA Barrett, ...
Nature materials 13 (6), 570-579, 2014
1562014
The Materials Genome in Action: Identifying the Performance Limits of Physical Hydrogen Storage
AW Thornton, CM Simon, J Kim, O Kwon, KS Deeg, K Konstas, SJ Pas, ...
Chemistry of Materials 29 (7), 2844-2854, 2017
1522017
Discovery and optimization of materials using evolutionary approaches
TC Le, DA Winkler
Chemical reviews 116 (10), 6107-6132, 2016
1432016
Classification of emergence and its relation to self‐organization
JD Halley, DA Winkler
Complexity 13 (5), 10-15, 2008
1382008
The X-ray Structure of a Hemipteran Ecdysone Receptor Ligand-binding Domain comparison with a Lepidopteran ecdysone receptor ligand-binding domain and implications for …
JA Carmichael, MC Lawrence, LD Graham, PA Pilling, VC Epa, L Noyce, ...
Journal of Biological Chemistry 280 (23), 22258-22269, 2005
1292005
Understanding the roles of the “two QSARs”
T Fujita, DA Winkler
Journal of chemical information and modeling 56 (2), 269-274, 2016
1282016
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