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Journal article

Learning from the ligand: using ligand-based features to improve binding affinity prediction

Abstract:

Machine learning scoring functions for protein-ligand binding affinity prediction have been found to consistently outperform classical scoring functions. Structure-based scoring functions for universal affinity prediction typically use features describing interactions derived from the protein-ligand complex, with limited information about the chemical or topological properties of the ligand itself. We demonstrate that the performance of machine learning scoring functions are consistently impr...

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Publication status:
Not Published
Peer review status:
Not peer reviewed
Version:
Author's Original

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Publisher copy:
10.26434/chemrxiv.8174525.v1

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Institution:
University of Oxford
Division:
MPLS Division
Department:
Statistics
Oxford college:
Green Templeton College
Role:
Author
ORCID:
0000-0003-1731-8405
Publication date:
2019-05-23
DOI:
Pubs id:
pubs:1002923
URN:
uri:859e4ea6-731f-4af3-8cfb-a31adec9ca8a
UUID:
uuid:859e4ea6-731f-4af3-8cfb-a31adec9ca8a
Local pid:
pubs:1002923

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