David A. Winkler

According to our database, David A. Winkler authored at least 29 papers between 1999 and 2022.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2022

Potent antimalarial drugs with validated activities.

David A. Winkler

Nat. Mach. Intell., 2022

European Registry of Materials: global, unique identifiers for (undisclosed) nanomaterials.

Jeaphianne P. M. van Rijn, Antreas Afantitis, Mustafa Culha, Maria Dusinska, Thomas E. Exner, Nina Jeliazkova, Eleonora Marta Longhin, Iseult Lynch, Georgia Melagraki, Penny Nymark, Anastasios G. Papadiamantis, David A. Winkler, Hulya Yilmaz, Egon L. Willighagen

J. Cheminformatics, 2022

Feature importance in machine learning models: A fuzzy information fusion approach.

Divish Rengasamy, Jimiama Mafeni Mase, Aayush Kumar, Benjamin Rothwell, Mercedes Torres Torres, Morgan R. Alexander, David A. Winkler, Grazziela P. Figueredo

Neurocomputing, 2022

EFI: A Toolbox for Feature Importance Fusion and Interpretation in Python.

Aayush Kumar, Jimiama Mafeni Mase, Divish Rengasamy, Benjamin Rothwell, Mercedes Torres Torres, David A. Winkler, Grazziela P. Figueredo

Proceedings of the Machine Learning, Optimization, and Data Science, 2022

2021

Systematic Comparison of the Structural and Dynamic Properties of Commonly Used Water Models for Molecular Dynamics Simulations.

Sachini P. Kadaoluwa Pathirannahalage, Nastaran Meftahi, Aaron Elbourne, Alessia C. G. Weiss, Chris F. McConville, Agilio Padua, David A. Winkler, Margarida Costa Gomes, Tamar L. Greaves, Tu C. Le, Quinn A. Besford, Andrew J. Christofferson

J. Chem. Inf. Model., 2021

Mechanistic Interpretation of Machine Learning Inference: A Fuzzy Feature Importance Fusion Approach.

Divish Rengasamy, Jimiama Mafeni Mase, Mercedes Torres Torres, Benjamin Rothwell, David A. Winkler, Grazziela P. Figueredo

CoRR, 2021

Active Learning in Bayesian Neural Networks for Bandgap Predictions of Novel Van der Waals Heterostructures.

Marco Fronzi, Olexandr Isayev, David A. Winkler, Joseph George Shapter, Amanda V. Ellis, Peter C. Sherrell, Nick A. Shepelin, Alexander Corletto, Michael J. Ford

Adv. Intell. Syst., 2021

2020

Ligand Entropy Is Hard but Should Not Be Ignored.

David A. Winkler

J. Chem. Inf. Model., 2020

2019

Evolving embodied intelligence from materials to machines.

David Howard, Ágoston E. Eiben, Danielle Frances Kennedy, Jean-Baptiste Mouret, Philip Valencia, David A. Winkler

Nat. Mach. Intell., 2019

Massive in Silico Study of Noble Gas Binding to the Structural Proteome.

David A. Winkler, A. C. Warden, Thierry Prangé, N. Colloc'h, A. W. Thornton, J.-F. Ramirez-Gil, G. Farjot, I. Katz

J. Chem. Inf. Model., 2019

Impressive computational acceleration by using machine learning for 2-dimensional super-lubricant materials discovery.

Marco Fronzi, Mutaz Abu Ghazaleh, Olexandr Isayev, David A. Winkler, Joe Shapter, Michael J. Ford

CoRR, 2019

Toward Interpretable Machine Learning Models for Materials Discovery.

Paulius Mikulskis, Morgan R. Alexander, David A. Winkler

Adv. Intell. Syst., 2019

2018

Sparse QSAR modelling methods for therapeutic and regenerative medicine.

David A. Winkler

J. Comput. Aided Mol. Des., 2018

2016

Understanding the Roles of the "Two QSARs".

Toshio Fujita, David A. Winkler

J. Chem. Inf. Model., 2016

2015

Relevance Vector Machines: Sparse Classification Methods for QSAR.

Frank R. Burden, David A. Winkler

J. Chem. Inf. Model., 2015

Beware of <i>R</i>²: Simple, Unambiguous Assessment of the Prediction Accuracy of QSAR and QSPR Models.

David L. J. Alexander, Alexander Tropsha, David A. Winkler

J. Chem. Inf. Model., 2015

2013

Correction to Capturing the Crystal: Prediction of Enthalpy of Sublimation, Crystal Lattice Energy, and Melting Points of Organic Compounds.

Maryam Salahinejad, Tu C. Le, David A. Winkler

J. Chem. Inf. Model., 2013

Capturing the Crystal: Prediction of Enthalpy of Sublimation, Crystal Lattice Energy, and Melting Points of Organic Compounds.

Maryam Salahinejad, Tu C. Le, David A. Winkler

J. Chem. Inf. Model., 2013

2009

Toward Novel Universal Descriptors: Charge Fingerprints.

Frank R. Burden, Mitchell J. Polley, David A. Winkler

J. Chem. Inf. Model., 2009

Predictive Mesoscale Network Model of Cell Fate Decisions during <i>C. elegans</i> Embryogenesis.

David A. Winkler, Frank R. Burden, Julianne D. Halley

Artif. Life, 2009

Bayesian Regularization of Neural Networks.

Frank R. Burden, David A. Winkler

Proceedings of the Artificial Neural Networks, 2009

2008

Consistent concepts of self-organization and self-assembly.

Julianne D. Halley, David A. Winkler

Complex., 2008

Classification of emergence and its relation to self-organization.

Julianne D. Halley, David A. Winkler

Complex., 2008

Critical-like self-organization and natural selection: Two facets of a single evolutionary process?

Julianne D. Halley, David A. Winkler

Biosyst., 2008

2003

Comparison of Linear and Nonlinear Classification Algorithms for the Prediction of Drug and Chemical Metabolism by Human UDP-Glucuronosyltransferase Isoforms.

Michael J. Sorich, John O. Miners, Ross A. McKinnon, David A. Winkler, Frank R. Burden, Paul A. Smith

J. Chem. Inf. Comput. Sci., 2003

2002

The role of quantitative structure-activity relationships (QSAR) in biomolecular discovery.

David A. Winkler

Briefings Bioinform., 2002

2001

A high-resolution electron momentum spectroscopy and density functional theory study into the complete valence electronic structure of allene.

F. Wang, H. Mackenzie-Ross, David A. Winkler, Ian E. McCarthy, Laurence Campbell, Michael J. Brunger

J. Comput. Chem., 2001

2000

Use of Automatic Relevance Determination in QSAR Studies Using Bayesian Neural Networks.

Frank R. Burden, Martyn G. Ford, David C. Whitley, David A. Winkler

J. Chem. Inf. Comput. Sci., 2000

1999

New QSAR Methods Applied to Structure-Activity Mapping and Combinatorial Chemistry.

Frank R. Burden, David A. Winkler

J. Chem. Inf. Comput. Sci., 1999