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Role of Artificial Intelligence and Machine Learning in Nanosafety

Small (Weinheim an der Bergstrasse, Germany), 2020-09, Vol.16 (36), p.e2001883-n/a

Winkler, David A.

2020

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Discovery and Optimization of Materials Using Evolutionary Approaches

Chemical reviews, 2016-05, Vol.116 (10), p.6107-6132

2016

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Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery

Chemical reviews, 2022-08, Vol.122 (16), p.13478-13515

2022

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Understanding the Roles of the “Two QSARs”

Journal of chemical information and modeling, 2016-02, Vol.56 (2), p.269-274

2016

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

Toxicology and applied pharmacology, 2016-05, Vol.299, p.96-100

Winkler, David A.

2016

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Ligand Entropy Is Hard but Should Not Be Ignored

Journal of chemical information and modeling, 2020-10, Vol.60 (10), p.4421-4423

Winkler, David A

2020

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Aquatic insects rich in omega‐3 fatty acids drive breeding success in a widespread bird

Ecology letters, 2018-12, Vol.21 (12), p.1812-1820

2018

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

Scientific reports, 2021-06, Vol.11 (1), p.13063-13063, Article 13063

2021

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Beware of R(2): Simple, Unambiguous Assessment of the Prediction Accuracy of QSAR and QSPR Models

Journal of chemical information and modeling, 2015-07, Vol.55 (7), p.1316-1322

2015

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QSAR without borders

Chemical Society reviews, 2020-06, Vol.49 (11), p.3525-3564

2020

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Beware of R 2: Simple, Unambiguous Assessment of the Prediction Accuracy of QSAR and QSPR Models

Journal of chemical information and modeling, 2015-07, Vol.55 (7), p.1316-1322

2015

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Predicting the Performance of Organic Corrosion Inhibitors

Metals (Basel ), 2017-12, Vol.7 (12), p.553

Winkler, David

2017

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Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation

Chemical reviews, 2023-07, Vol.123 (13), p.8575-8637

2023

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Computational Modeling and Simulation of CO2 Capture by Aqueous Amines

Chemical reviews, 2017-07, Vol.117 (14), p.9524-9593

2017

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Computational Modelling of Magnetic Nanoparticle Properties and In Vivo Responses

Current medicinal chemistry, 2017-01, Vol.24 (5), p.483-496

Winkler, David A

2017

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Quantitative Structure–Property Relationship Modeling of Diverse Materials Properties

Chemical reviews, 2012-05, Vol.112 (5), p.2889-2919

2012

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

Frontiers in chemistry, 2021-03, Vol.9, p.614073-614073

Winkler, David A

2021

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Antifibrotic strategies for medical devices

Advanced drug delivery reviews, 2020-12, Vol.167, p.109-120

2020

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Computational repurposing of drugs for viral diseases and current and future pandemics

Journal of mathematical chemistry, 2024, Vol.62 (10), p.2844-2879

Winkler, David A.

2024

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

Journal of chemical information and modeling, 2021-09, Vol.61 (9), p.4521-4536

2021

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Brain homogenates from human tauopathies induce tau inclusions in mouse brain

Proceedings of the National Academy of Sciences - PNAS, 2013-06, Vol.110 (23), p.9535-9540

2013

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The impact of machine learning on future tuberculosis drug discovery

Expert opinion on drug discovery, 2022-09, Vol.17 (9), p.925-927

Winkler, David A.

2022

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

International journal of molecular sciences, 2023-02, Vol.24 (4), p.4192

2023

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Probing the properties of molecules and complex materials using machine learning

Australian journal of chemistry, 2022-01, Vol.75 (11), p.906-922

2022

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Stimulation of autophagy reduces neurodegeneration in a mouse model of human tauopathy

Brain (London, England : 1878), 2012-07, Vol.135 (Pt 7), p.2169-2177

2012

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Role of Artificial Intelligence and Machine Learning in Nanosafety

Discovery and Optimization of Materials Using Evolutionary Approaches

Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery

Understanding the Roles of the “Two QSARs”

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

Ligand Entropy Is Hard but Should Not Be Ignored

Aquatic insects rich in omega‐3 fatty acids drive breeding success in a widespread bird

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

Beware of R(2): Simple, Unambiguous Assessment of the Prediction Accuracy of QSAR and QSPR Models

QSAR without borders

Beware of R 2: Simple, Unambiguous Assessment of the Prediction Accuracy of QSAR and QSPR Models

Predicting the Performance of Organic Corrosion Inhibitors

Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation

Computational Modeling and Simulation of CO2 Capture by Aqueous Amines

Computational Modelling of Magnetic Nanoparticle Properties and In Vivo Responses

Quantitative Structure–Property Relationship Modeling of Diverse Materials Properties

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

Antifibrotic strategies for medical devices

Computational repurposing of drugs for viral diseases and current and future pandemics

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

Brain homogenates from human tauopathies induce tau inclusions in mouse brain

The impact of machine learning on future tuberculosis drug discovery

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

Probing the properties of molecules and complex materials using machine learning

Stimulation of autophagy reduces neurodegeneration in a mouse model of human tauopathy

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