D. Winkler

发表

Robust QSAR models using Bayesian regularized neural networks.

F. Burden, D. Winkler, David A. Winkler, 1999, Journal of medicinal chemistry.

Discovery and Optimization of Materials Using Evolutionary Approaches.

D. Winkler, T. Le, 2016, Chemical reviews.

Opening the black box of neural networks: methods for interpreting neural network models in clinical applications.

Bin Huang, David A Winkler, Zhongheng Zhang, 2018, Annals of translational medicine.

Using high throughput experimental data and in silico models to discover alternatives to toxic chromate corrosion inhibitors

D. Winkler, A. Hughes, I. Cole, 2016 .

Towards computational design of zeolite catalysts for CO2 reduction

Danielle F. Kennedy, Maciej Haranczyk, David A. Winkler, 2015 .

Discovery of (meth)acrylate polymers that resist colonization by fungi associated with pathogenesis and biodeterioration

M. Alexander, D. Winkler, R. Wildman, 2020, Science Advances.

Competitive Inhibition Mechanism of Acetylcholinesterase without Catalytic Active Site Interaction: Study on Functionalized C60 Nanoparticles via in Vitro and in Silico Assays.

Jianjie Fu, D. Winkler, A. Zhang, 2017, ACS applied materials & interfaces.

An Experimental and Computational Approach to the Development of ZnO Nanoparticles that are Safe by Design.

Lin Zhao, Hong Yin, Tu C Le, 2016, Small.

Quantitative structure-property relationship modeling of diverse materials properties.

Frank R Burden, Tu Le, David A Winkler, 2012, Chemical reviews.

Modeling biological activities of nanoparticles.

R. Weissleder, S. Shaw, F. Burden, 2012, Nano letters.

Aqueous solubility prediction: do crystal lattice interactions help?

Tu C Le, Maryam Salahinejad, David A Winkler, 2013, Molecular pharmaceutics.

Computational Modeling and Prediction of the Complex Time-Dependent Phase Behavior of Lyotropic Liquid Crystals under in Meso Crystallization Conditions

F. Burden, D. Winkler, T. Le, 2013 .

Predicting the Effect of Lipid Structure on Mesophase Formation during in Meso Crystallization

F. Burden, D. Winkler, T. Le, 2013 .

Quantitative design rules for protein-resistant surface coatings using machine learning

D. Winkler, T. Le, I. Yarovsky, 2019, Scientific Reports.

Applications in Materials Science

D. Winkler, T. Le, 2018 .

Broad-based quantitative structure-activity relationship modeling of potency and selectivity of farnesyltransferase inhibitors using a Bayesian regularized neural network.

Frank R Burden, David A Winkler, F. Burden, 2004, Journal of medicinal chemistry.

Sparse feature selection methods identify unexpected global cellular response to strontium-containing materials

Molly M. Stevens, Frank R. Burden, Eileen Gentleman, 2015, Proceedings of the National Academy of Sciences.

High Throughput Screening of Millions of van der Waals Heterostructures for Superlubricant Applications

Mutaz Abu Ghazaleh, O. Isayev, D. Winkler, 2020, Advanced Theory and Simulations.

Rapid evaluation of immobilized immunoglobulins using automated mass-segmented ToF-SIMS.

Robert M T Madiona, D. Winkler, P. Pigram, 2019, Biointerphases.

The diverse biological properties of the chemically inert noble gases.

D. Winkler, A. Thornton, I. Katz, 2016, Pharmacology & therapeutics.

Prediction of Broad-Spectrum Pathogen Attachment to Coating Materials for Biomedical Devices.

Andrew L. Hook, Daniel G. Anderson, R. Langer, 2018, ACS applied materials & interfaces.

Optimal machine learning models for robust materials classification using ToF-SIMS data

Robert M T Madiona, D. Winkler, P. Pigram, 2019, Applied Surface Science.

Antifibrotic strategies for medical devices.

D. Winkler, H. Thissen, N. Welch, 2020, Advanced drug delivery reviews.

Computational screening of repurposed drugs and natural products against SARS-Cov-2 main protease (Mpro) as potential COVID-19 therapies

N. Petrovsky, D. Winkler, P. Singh, 2020, 2009.00744.

Micro topographical instruction of bacterial attachment, biofilm formation and in vivo host response

M. Alexander, J. de Boer, A. Ghaemmaghami, 2022, bioRxiv.

Coatings releasing the relaxin peptide analogue B7-33 reduce fibrotic encapsulation.

J. Werkmeister, D. Winkler, J. Wade, 2019, ACS applied materials & interfaces.

Information content of ToF-SIMS data: Effect of spectral binning

Robert M T Madiona, D. Winkler, P. Pigram, 2019, Applied Surface Science.

In silico comparison of spike protein-ACE2 binding affinities across species; significance for the possible origin of the SARS-CoV-2 virus

N. Petrovsky, D. Winkler, P. Singh, 2020, 2005.06199.

Computationally repurposed drugs and natural products against RNA dependent RNA polymerase as potential COVID-19 therapies

N. Petrovsky, D. Winkler, P. Singh, 2020, Molecular Biomedicine.

Energy Interplay in Materials: Unlocking Next‐Generation Synchronous Multisource Energy Conversion with Layered 2D Crystals

Amanda V. Ellis, Nick A. Shepelin, D. Winkler, 2022, Advanced materials.

Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture

D. Winkler, T. Le, Dehong Chen, 2022, Advanced science.

Predicting the Performance of Organic Corrosion Inhibitors

D. Winkler, 2017 .

Identifying medically relevant xenon protein targets by in silico screening of the structural proteome

D. Winkler, Aaron Thornton, Géraldine Farjot, 2022, Medical gas research.

Can an InChI for Nano Address the Need for a Simplified Representation of Complex Nanomaterials across Experimental and Nanoinformatics Studies?

Haralambos Sarimveis, Philip Doganis, Egon Willighagen, 2020, Nanomaterials.

Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using Complementary DFT and Machine Learning Approaches

Joseph G. Shapter, David A. Winkler, Michael J. Ford, 2018, Advanced Theory and Simulations.

Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using DFT and Machine Learning

O. Isayev, C. Stampfl, D. Winkler, 2018 .

A renaissance of neural networks in drug discovery

Igor V Tetko, Igor I Baskin, David Winkler, 2016, Expert opinion on drug discovery.

Machine learning-assisted exploration of a versatile polymer platform with charge transfer-dependent full-color emission

D. Winkler, J. Leroux, S. Russo, 2023, Chem.

Modeling the Influence of Fatty Acid Incorporation on Mesophase Formation in Amphiphilic Therapeutic Delivery Systems.

D. Winkler, T. Le, X. Mulet, 2016, Molecular pharmaceutics.

Corrosion Inhibition, Inhibitor Environments, and the Role of Machine Learning

D. Winkler, M. Laleh, A. Hughes, 2022, Corrosion and Materials Degradation.

The non‐Fickian diffusion of deterrents into a nitrocellulose‐based propellant

D. Winkler, A. Starks, 1988 .

Exploring structure-property relationships in magnesium dissolution modulators

D. Winkler, S. Lamaka, M. Zheludkevich, 2021, npj Materials Degradation.

In silico screening of modulators of magnesium dissolution

D. Winkler, S. Lamaka, M. Zheludkevich, 2020, Corrosion Science.

Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery.

D. Winkler, T. Le, Dehong Chen, 2022, Chemical reviews.

A critical overview of computational approaches employed for COVID-19 drug discovery

Tudor I. Oprea, Rommie E. Amaro, G. Schneider, 2021, Chemical Society reviews.

Nanoparticle-protein corona complex: understanding multiple interactions between environmental factors, corona formation, and biological activity

D. Winkler, S. Çeşmeli, A. Tomak, 2021, Nanotoxicology.

Biomedical nanomaterials: applications, toxicological concerns, and regulatory needs

D. Winkler, Ceyda Oksel Karakus, E. Bilgi, 2020, Nanotoxicology.

Computational Modeling and Simulation of CO2 Capture by Aqueous Amines.

D. Winkler, Xin Yang, R. J. Rees, 2017, Chemical reviews.

Discovery of synergistic material-topography combinations to achieve immunomodulatory osteoinductive biomaterials using a novel in vitro screening method: The ChemoTopoChip.

I. Dryden, M. Alexander, J. de Boer, 2021, Biomaterials.

Synergistic Material-Topography Combinations to Achieve Immunomodulatory Osteogenic Biomaterials

I. Dryden, M. Alexander, J. de Boer, 2020, bioRxiv.

Role of Artificial Intelligence and Machine Learning in Nanosafety.

David A Winkler, D. Winkler, 2020, Small.

In silico comparison of SARS-CoV-2 spike protein-ACE2 binding affinities across species and implications for virus origin

N. Petrovsky, D. Winkler, P. Singh, 2021, Scientific Reports.

QSAR without borders.

Artem Cherkasov, Tudor I. Oprea, Jürgen Bajorath, 2020, Chemical Society reviews.

Toward a systematic exploration of nano-bio interactions.

Hao Zhu, Cong Li, Yin Liu, 2017, Toxicology and applied pharmacology.

Sparse Feature Selection Identifies H2A.Z as a Novel Pattern-Specific Biomarker for Asymmetrically Self-Renewing Distributed Stem Cells

Frank R Burden, Minsoo Noh, David A Winkler, 2015, Microscopy and Microanalysis.

Hydration and Dynamics of Ligands Determine the Antifouling Capacity of Functionalized Surfaces

D. Winkler, I. Yarovsky, Alexis Belessiotis-Richards, 2019 .

Use of Artificial Intelligence and Machine Learning for Discovery of Drugs for Neglected Tropical Diseases

D. Winkler, 2021, Frontiers in Chemistry.

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

David A. Winkler, Maryam Salahinejad, Tu C. Le, 2013, J. Chem. Inf. Model..

Identifying SARS-CoV-2 Drugs Binding to the Spike Fatty Acid Binding Pocket Using In Silico Docking and Molecular Dynamics

N. Petrovsky, D. Winkler, Puneet S. Singh, 2023, International journal of molecular sciences.

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

Frank R. Burden, Martyn G. Ford, David C. Whitley, 2000, J. Chem. Inf. Comput. Sci..

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

Frank R. Burden, David A. Winkler, F. Burden, 1999, J. Chem. Inf. Comput. Sci..

Immune Modulation by Design: Using Topography to Control Human Monocyte Attachment and Macrophage Differentiation

David J Scurr, Paul Williams, Aurélie Carlier, 2020, Advanced science.

Use of metamodels for rapid discovery of narrow bandgap oxide photocatalysts

K. Domen, D. Winkler, T. Le, 2021, iScience.

SOM-RPM for the accurate visualization of high-dimensional hyperspectral data.

D. Ballabio, D. Winkler, P. Pigram, 2020, Analytical chemistry.

Exploring the Relationship between Polymer Surface Chemistry and Bacterial Attachment Using ToF‐SIMS and Self‐Organizing maps

D. Ballabio, M. Alexander, D. Winkler, 2023, Advanced Materials Interfaces.

Self-Organizing Map and Relational Perspective Mapping for the Accurate Visualization of High-Dimensional Hyperspectral Data

D. Ballabio, D. Winkler, P. Pigram, 2020 .

Effect of mass segment size on polymer ToF-SIMS multivariate analysis using a universal data matrix

Paul J. Pigram, Robert M.T. Madiona, Benjamin W. Muir, 2019, Applied Surface Science.

Distinguishing Chemically Similar Polyamide Materials with ToF-SIMS Using Self-Organizing Maps and a Universal Data Matrix.

Robert M T Madiona, Benjamin W Muir, Paul J Pigram, 2018, Analytical chemistry.

Multivariate analysis of ToF‐SIMS data using mass segmented peak lists

David A. Winkler, Paul J. Pigram, Robert M.T. Madiona, 2018 .

Two-Dimensional and Three-Dimensional Time-of-Flight Secondary Ion Mass Spectrometry Image Feature Extraction Using a Spatially Aware Convolutional Autoencoder.

D. Winkler, P. Pigram, G. Pietersz, 2022, Analytical chemistry.

Analyzing 3D hyperspectral TOF-SIMS depth profile data using self-organizing map-relational perspective mapping.

D. Ballabio, D. Winkler, P. Pigram, 2020, Biointerphases.

Fusing ToF‐SIMS Images for Spatial‐Spectral Resolution Enhancement using a Convolutional Neural Network

D. Winkler, P. Pigram, S. Cutts, 2022, Advanced Materials Interfaces.

A bright future for engineering piezoelectric 2D crystals.

Amanda V. Ellis, D. Winkler, P. Sherrell, 2021, Chemical Society reviews.

In silico comparison of spike protein-ACE2 binding affinities across species; significance for the possible origin of the SARS-CoV-2 virus

N. Petrovsky, D. Winkler, P. Singh, 2020, 2005.06199.

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

Aaron Elbourne, Nastaran Meftahi, Andrew J. Christofferson, 2021, J. Chem. Inf. Model..

Machine learning property prediction for organic photovoltaic devices

David A. Winkler, Udo Bach, Salvy P. Russo, 2020, npj Computational Materials.

Recent advances, and unresolved issues, in the application of computational modelling to the prediction of the biological effects of nanomaterials.

David A Winkler, D. Winkler, 2016, Toxicology and applied pharmacology.

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

David A. Winkler, Maryam Salahinejad, Tu C. Le, 2013, J. Chem. Inf. Model..

Immune-Instructive Polymers Control Macrophage Phenotype and Modulate the Foreign Body Response In Vivo

Andrew L. Hook, M. Alexander, A. Ghaemmaghami, 2020, Matter.

Linear Binary Classifier to Predict Bacterial Biofilm Formation on Polyacrylates

Andrew L. Hook, M. Alexander, D. Winkler, 2023, ACS applied materials & interfaces.

Quantitative design rules for protein-resistant surface coatings using machine learning

D. Winkler, T. Le, I. Yarovsky, 2019, Scientific Reports.

An Optimal Self‐Pruning Neural Network and Nonlinear Descriptor Selection in QSAR

Frank R. Burden, David A. Winkler, F. Burden, 2009 .

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

Joseph G. Shapter, Olexandr Isayev, Michael J. Ford, 2021, Adv. Intell. Syst..

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

Grazziela P. Figueredo, Divish Rengasamy, Benjamin Rothwell, 2021, ArXiv.

Decoding the Rich Biological Properties of Noble Gases: How Well Can We Predict Noble Gas Binding to Diverse Proteins?

D. Winkler, A. Thornton, A. Warden, 2018, ChemMedChem.

The impact of machine learning on future tuberculosis drug discovery

D. Winkler, 2022, Expert opinion on drug discovery.

Correction: QSAR without borders.

Tudor I. Oprea, I. Tetko, D. Agrafiotis, 2020, Chemical Society reviews.

Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation.

D. Winkler, Guibin Jiang, Yongguang Yin, 2023, Chemical reviews.

Applying quantitative structure-activity relationship approaches to nanotoxicology: current status and future potential.

Andrew Worth, David A Winkler, Enrico Mombelli, 2013, Toxicology.

Sparse QSAR modelling methods for therapeutic and regenerative medicine

David A. Winkler, D. Winkler, 2018, Journal of Computer-Aided Molecular Design.

Topographical biomaterials instruct bacterial surface attachment and the in vivo host-pathogen response

Andrew L. Hook, M. Alexander, J. de Boer, 2020 .

Overcoming roadblocks in computational roadmaps to the future for safe nanotechnology

Ceyda Oksel Karakus, David A Winkler, D. Winkler, 2021 .

Single-Cell Tracking on Polymer Microarrays Reveals the Impact of Surface Chemistry on Pseudomonas aeruginosa Twitching Speed and Biofilm Development

Andrew L. Hook, M. Alexander, D. Winkler, 2020, ACS applied bio materials.

High-Throughput Assessment and Modeling of a Polymer Library Regulating Human Dental Pulp-Derived Stem Cell Behavior.

Nicolas H Voelcker, Andrew L Hook, Morgan R Alexander, 2018, ACS applied materials & interfaces.

Optimal Sparse Descriptor Selection for QSAR Using Bayesian Methods

Frank R. Burden, David A. Winkler, F. Burden, 2009 .

Predictive Bayesian neural network models of MHC class II peptide binding.

David A Winkler, Frank R Burden, F. Burden, 2005, Journal of molecular graphics & modelling.

Predictive Human Intestinal Absorption QSAR Models Using Bayesian Regularized Neural Networks

Frank R. Burden, Mitchell J. Polley, David A. Winkler, 2005 .

Predicting the complex phase behavior of self-assembling drug delivery nanoparticles.

Frank R Burden, Tu C Le, Xavier Mulet, 2013, Molecular pharmaceutics.

NanoSolveIT integration of tools for assessment of human and environmental exposure to nanomaterials

Haralambos Sarimveis, Philip Doganis, Andreas Tsoumanis, 2021, Handbook of Functionalized Nanomaterials.

Computational Modeling and Simulation of CO2 Capture by Aqueous Amines.

D. Winkler, R. J. Rees, G. Puxty, 2017, Chemical reviews.

Deep and Shallow Neural Networks

David A. Winkler, D. Winkler, 2018, Chemoinformatics.

Predicting Thermal Properties of Crystals Using Machine Learning

Olexandr Isayev, David A. Winkler, Michelle J. S. Spencer, 2019, Advanced Theory and Simulations.

Sparse feature selection methods identify unexpected global cellular response to strontium-containing materials

F. Burden, M. Stevens, D. Winkler, 2015, Proceedings of the National Academy of Sciences.