Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/168978
Title: Forman persistent Ricci curvature (FPRC)-based machine learning models for protein-ligand binding affinity prediction
Authors: Wee, Junjie
Xia, Kelin
Keywords: Science::Mathematics
Issue Date: 2021
Source: Wee, J. & Xia, K. (2021). Forman persistent Ricci curvature (FPRC)-based machine learning models for protein-ligand binding affinity prediction. Briefings in Bioinformatics, 22(6), bbab136-. https://dx.doi.org/10.1093/bib/bbab136
Project: M4081842.110 
RG109/19
MOE2018-T2-1-033
Journal: Briefings in Bioinformatics
Abstract: Artificial intelligence (AI) techniques have already been gradually applied to the entire drug design process, from target discovery, lead discovery, lead optimization and preclinical development to the final three phases of clinical trials. Currently, one of the central challenges for AI-based drug design is molecular featurization, which is to identify or design appropriate molecular descriptors or fingerprints. Efficient and transferable molecular descriptors are key to the success of all AI-based drug design models. Here we propose Forman persistent Ricci curvature (FPRC)-based molecular featurization and feature engineering, for the first time. Molecular structures and interactions are modeled as simplicial complexes, which are generalization of graphs to their higher dimensional counterparts. Further, a multiscale representation is achieved through a filtration process, during which a series of nested simplicial complexes at different scales are generated. Forman Ricci curvatures (FRCs) are calculated on the series of simplicial complexes, and the persistence and variation of FRCs during the filtration process is defined as FPRC. Moreover, persistent attributes, which are FPRC-based functions and properties, are employed as molecular descriptors, and combined with machine learning models, in particular, gradient boosting tree (GBT). Our FPRC-GBT models are extensively trained and tested on three most commonly-used datasets, including PDBbind-2007, PDBbind-2013 and PDBbind-2016. It has been found that our results are better than the ones from machine learning models with traditional molecular descriptors.
URI: https://hdl.handle.net/10356/168978
ISSN: 1467-5463
DOI: 10.1093/bib/bbab136
DOI (Related Dataset): 10.21979/N9/ZTA5MN
Schools: School of Physical and Mathematical Sciences 
Rights: © 2021 The Author(s). Published by Oxford University Press. All rights reserved
Fulltext Permission: none
Fulltext Availability: No Fulltext
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