New QSAR Methods Applied to Structure-Activity Mapping and Combinatorial Chemistry. - Frank R. Burden; David A. Winkler (J. Chem. Inf. Comput. Sci., 1999)

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)

A high-resolution electron momentum spectroscopy and density functional theory study into the complete valence electronic structure of allene. - Feng Wang 005; H. Mackenzie-Ross; David A. Winkler; Ian E. McCarthy; Laurence Campbell; Michael J. Brunger (J. Comput. Chem., 2001)

The role of quantitative structure-activity relationships (QSAR) in biomolecular discovery. - David A. Winkler (Briefings Bioinform., 2002)

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)

Critical-like self-organization and natural selection: Two facets of a single evolutionary process? - Julianne D. Halley; David A. Winkler (Biosyst., 2008)

Classification of emergence and its relation to self-organization. - Julianne D. Halley; David A. Winkler (Complex., 2008)

Consistent concepts of self-organization and self-assembly. - Julianne D. Halley; David A. Winkler (Complex., 2008)

Bayesian Regularization of Neural Networks. - Frank R. Burden; David A. Winkler (Artificial Neural Networks, 2009)

Predictive Mesoscale Network Model of Cell Fate Decisions during C. elegans Embryogenesis. - David A. Winkler; Frank R. Burden; Julianne D. Halley (Artif. Life, 2009)

Toward Novel Universal Descriptors: Charge Fingerprints. - Frank R. Burden; Mitchell J. Polley; David A. Winkler (J. Chem. Inf. Model., 2009)

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)

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)

Beware of R2: 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)

Relevance Vector Machines: Sparse Classification Methods for QSAR. - Frank R. Burden; David A. Winkler (J. Chem. Inf. Model., 2015)

Understanding the Roles of the "Two QSARs". - Toshio Fujita; David A. Winkler (J. Chem. Inf. Model., 2016)

Sparse QSAR modelling methods for therapeutic and regenerative medicine. - David A. Winkler (J. Comput. Aided Mol. Des., 2018)

Toward Interpretable Machine Learning Models for Materials Discovery. - Paulius Mikulskis; Morgan R. Alexander; David A. Winkler (Adv. Intell. Syst., 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)

Massive in Silico Study of Noble Gas Binding to the Structural Proteome. - David A. Winkler; Andrew C. Warden; Thierry Prangé; N. Colloc'h; A. W. Thornton; J.-F. Ramirez-Gil; G. Farjot; I. Katz (J. Chem. Inf. Model., 2019)

Evolving embodied intelligence from materials to machines. - David Howard 001; Ágoston E. Eiben; Danielle Frances Kennedy; Jean-Baptiste Mouret; Philip Valencia; David A. Winkler (Nat. Mach. Intell., 2019)

Ligand Entropy Is Hard but Should Not Be Ignored. - David A. Winkler (J. Chem. Inf. Model., 2020)

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)

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)

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)

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 (LOD (2), 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)

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; et al. (J. Cheminformatics, 2022)

Potent antimalarial drugs with validated activities. - David A. Winkler (Nat. Mach. Intell., 2022)

Helix 1.0: An Open-Source Framework for Reproducible and Interpretable Machine Learning on Tabular Scientific Data. - Eduardo Aguilar-Bejarano; Daniel Lea; Karthikeyan Sivakumar; Jimiama Mafeni Mase; Reza Omidvar; Ruizhe Li 005; Troy Kettle; James Mitchell-White; Morgan R. Alexander; David A. Winkler; Grazziela Patrocinio Figueredo (CoRR, 2025)