Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/151043
Title: Persistent spectral-based machine learning (PerSpect ML) for protein-ligand binding affinity prediction
Authors: Meng, Zhenyu
Xia, Kelin
Keywords: Science::Biological sciences
Issue Date: 2021
Source: Meng, Z. & Xia, K. (2021). Persistent spectral-based machine learning (PerSpect ML) for protein-ligand binding affinity prediction. Science Advances, 7(19), eabc5329-. https://dx.doi.org/10.1126/sciadv.abc5329
Project: M4081842.110
RG109/19
MOE2018-T2-1-033
Journal: Science Advances 
Abstract: Molecular descriptors are essential to not only quantitative structure-activity relationship (QSAR) models but also machine learning–based material, chemical, and biological data analysis. Here, we propose persistent spectral–based machine learning (PerSpect ML) models for drug design. Different from all previous spectral models, a filtration process is introduced to generate a sequence of spectral models at various different scales. PerSpect attributes are defined as the function of spectral variables over the filtration value. Molecular descriptors obtained from PerSpect attributes are combined with machine learning models for protein-ligand binding affinity prediction. Our results, for the three most commonly used databases including PDBbind-2007, PDBbind-2013, and PDBbind-2016, are better than all existing models, as far as we know. The proposed PerSpect theory provides a powerful feature engineering framework. PerSpect ML models demonstrate great potential to significantly improve the performance of learning models in molecular data analysis.
URI: https://hdl.handle.net/10356/151043
ISSN: 2375-2548
DOI: 10.1126/sciadv.abc5329
Rights: © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Journal Articles

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