A fully differentiable ligand pose optimization framework guided by deep learning and traditional sc...
A fully differentiable ligand pose optimization framework guided by deep learning and traditional scoring functions
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Publisher
Ithaca: Cornell University Library, arXiv.org
Journal title
Language
English
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Publisher
Ithaca: Cornell University Library, arXiv.org
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Contents
The machine learning (ML) and deep learning (DL) techniques are widely recognized to be powerful tools for virtual drug screening. The recently reported ML- or DL-based scoring functions have shown exciting performance in predicting protein-ligand binding affinities with fruitful application prospects. However, the differentiation between highly si...
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Full title
A fully differentiable ligand pose optimization framework guided by deep learning and traditional scoring functions
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TN_cdi_proquest_journals_2681638523
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2681638523
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E-ISSN
2331-8422
DOI
10.48550/arxiv.2206.13345
