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Title: Ollivier persistent Ricci curvature-based machine learning for the protein-ligand binding affinity prediction
Authors: Wee, Junjie
Xia, Kelin
Keywords: Science::Mathematics
Issue Date: 2021
Source: Wee, J. & Xia, K. (2021). Ollivier persistent Ricci curvature-based machine learning for the protein-ligand binding affinity prediction. Journal of Chemical Information and Modeling, 61(4), 1617-1626.
Project: M4081842.110
Journal: Journal of Chemical Information and Modeling
Abstract: Efficient molecular featurization is one of the major issues for machine learning models in drug design. Here, we propose a persistent Ricci curvature (PRC), in particular, Ollivier PRC (OPRC), for the molecular featurization and feature engineering, for the first time. The filtration process proposed in the persistent homology is employed to generate a series of nested molecular graphs. Persistence and variation of Ollivier Ricci curvatures on these nested graphs are defined as OPRC. Moreover, persistent attributes, which are statistical and combinatorial properties of OPRCs during the filtration process, are used as molecular descriptors and further combined with machine learning models, in particular, gradient boosting tree (GBT). Our OPRC-GBT model is used in the prediction of the protein-ligand binding affinity, which is one of the key steps in drug design. Based on three of the most commonly used data sets from the well-established protein-ligand binding databank, that is, PDBbind, we intensively test our model and compare with existing models. It has been found that our model can achieve the state-of-the-art results and has advantages over traditional molecular descriptors.
ISSN: 1549-9596
DOI: 10.1021/acs.jcim.0c01415
Schools: School of Physical and Mathematical Sciences 
Rights: © 2021 American Chemical Society. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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