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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
1123*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
7002015
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
6862020
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
5572012
Robust QSAR models using Bayesian regularized neural networks
FR Burden, DA Winkler
Journal of Medicinal Chemistry 42 (16), 3183-3187, 1999
2991999
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
2792018
A renaissance of neural networks in drug discovery
II Baskin, D Winkler, IV Tetko
Expert opinion on drug discovery 11 (8), 785-795, 2016
2672016
Consistent concepts of self‐organization and self‐assembly
JD Halley, DA Winkler
Complexity 14 (2), 10-17, 2008
2402008
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
2232017
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
2162022
The role of quantitative structure-activity relationships (QSAR) in biomolecular discovery
DA Winkler
Briefings in bioinformatics 3 (1), 73-86, 2002
2122002
Modeling biological activities of nanoparticles
VC Epa, FR Burden, C Tassa, R Weissleder, S Shaw, DA Winkler
Nano letters 12 (11), 5808-5812, 2012
2082012
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
2052013
Discovery and optimization of materials using evolutionary approaches
TC Le, DA Winkler
Chemical reviews 116 (10), 6107-6132, 2016
2032016
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
1862017
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
1832014
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
1802000
Design of potential anti-HIV agents. 1. Mannosidase inhibitors
DA Winkler, G Holan
Journal of medicinal chemistry 32 (9), 2084-2089, 1989
1781989
Validating Eaton's hypothesis: Cubane as a benzene bioisostere
BA Chalmers, H Xing, S Houston, C Clark, S Ghassabian, A Kuo, B Cao, ...
Angewandte Chemie International Edition 55 (11), 3580-3585, 2016
176*2016
A critical overview of computational approaches employed for COVID-19 drug discovery – ISI Highly Cited paper
E Muratov, N Brown, D Fourches, D Kozakov, JL Medina-Franco, K Merz, ...
Chemical Society Reviews 50 (21 August), 9121-9151, 2021
1722021
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Articles 1–20