Author

D. W. H. Swenson

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Biophysics· 2 Cancer Biology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 3 Approach:statistical model· 3 Research Focus:benchmarking· 2 Research Focus:software / tool· 2 Research Focus:drug response / drug discovery· 1 Research Focus:protein folding & dynamics· 1 Assay:protein structure· 1

Frequent Co-Authors

P. G. Bolhuis· 3 shared F. Noe· 2 shared J.-H. Prinz· 2 shared F. Hooft· 1 shared J. Chodera· 1 shared J. D. Chodera· 1 shared J. Vreede· 1 shared S. Roet· 1 shared

Activity by Year

2018-2020 · peak 2/yr

Co-Author Collaboration Network

8 top collaborators · 15 mutual co-author links

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3 shared papersBiophysics

P. G. Bolhuis

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Author profile →

3 papers

Recent·Most cited

Cancer BiologybioRxivPreprintFeb 28, 2020

Path sampling simulations reveal how the Q61L mutation restricts the dynamics of KRas

S. Roet, F. Hooft, P. G. Bolhuis…·J. Vreede

The paper uses path sampling simulations to show that the Q61L mutation in KRas restricts its dynamic transitions, favoring rigid states over flexible ones, which may inform therapeutic strategies.

DOI 10.1101/2020.02.28.969451

protein structuredeep learning

𝕏 PostBluesky

BiophysicsbioRxivJun 20, 2018

OpenPathSampling: A Python framework for path sampling simulations. II. Building and customizing path ensembles and sample schemes

D. W. H. Swenson, J.-H. Prinz, F. Noe…·P. G. Bolhuis

This paper introduces theoretical developments in the OpenPathSampling framework for path sampling simulations, focusing on path ensemble construction and Monte Carlo procedure customization.

DOI 10.1021/acs.jctc.8b00627

deep learningstatistical model

𝕏 PostBluesky

BiophysicsbioRxivJun 20, 2018

OpenPathSampling: A Python framework for path sampling simulations. I. Basics

D. W. H. Swenson, J.-H. Prinz, F. Noe…·P. G. Bolhuis

The paper introduces OpenPathSampling, a Python framework for path sampling simulations, providing tools for various transition path sampling methods and demonstrating their application to benchmark problems.

DOI 10.1021/acs.jctc.8b00626

deep learningstatistical model

𝕏 PostBluesky