Winkler, D*
Winkler, D*
CSIRO or Monash or La Trobe or Defence or Nottingham or Pharmacy
Verified email at - Homepage
Cited by
Cited by
Bayesian regularization of neural networks
F Burden, D Winkler
Artificial neural networks, 23-42, 2008
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
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
Quantitative structure–property relationship modeling of diverse materials properties - ISI Highly Cited paper
T Le, VC Epa, FR Burden, DA Winkler
Chemical reviews 112 (5), 2889-2919, 2012
Robust QSAR models using Bayesian regularized neural networks
FR Burden, DA Winkler
Journal of medicinal chemistry 42 (16), 3183-3187, 1999
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
A renaissance of neural networks in drug discovery
II Baskin, D Winkler, IV Tetko
Expert opinion on drug discovery 11 (8), 785-795, 2016
Consistent concepts of self‐organization and self‐assembly
JD Halley, DA Winkler
Complexity 14 (2), 10-17, 2008
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
The role of quantitative structure-activity relationships (QSAR) in biomolecular discovery
DA Winkler
Briefings in bioinformatics 3 (1), 73-86, 2002
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
Modeling biological activities of nanoparticles
VC Epa, FR Burden, C Tassa, R Weissleder, S Shaw, DA Winkler
Nano letters 12 (11), 5808-5812, 2012
Discovery and optimization of materials using evolutionary approaches
TC Le, DA Winkler
Chemical reviews 116 (10), 6107-6132, 2016
Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery – ISI Highly Cited paper
H Mai, TC Le, D Chen, DA Winkler, RA Caruso
Chemical Reviews 122 (16), 13478-13515, 2022
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
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
Computational Modelling and Simulation of CO2 Capture by Aqueous Amines
X Yang, Q Yang, G Puxty, R Rees, DA Winkler
Chemical Reviews 117 (14), 9524–9593, 2017
Design of potential anti-HIV agents. 1. Mannosidase inhibitors
DA Winkler, G Holan
Journal of medicinal chemistry 32 (9), 2084-2089, 1989
Understanding the roles of the “two QSARs”
T Fujita, DA Winkler
Journal of chemical information and modeling 56 (2), 269-274, 2016
Towards chromate-free corrosion inhibitors: structure–property models for organic alternatives
DA Winkler, M Breedon, AE Hughes, FR Burden, AS Barnard, TG Harvey, ...
Green Chemistry 16 (6), 3349-3357, 2014
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