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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Meng, Zhenyu | en_US |
dc.contributor.author | Xia, Kelin | en_US |
dc.date.accessioned | 2021-06-25T06:29:40Z | - |
dc.date.available | 2021-06-25T06:29:40Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | 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 | en_US |
dc.identifier.issn | 2375-2548 | en_US |
dc.identifier.uri | https://hdl.handle.net/10356/151043 | - |
dc.description.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. | en_US |
dc.description.sponsorship | Ministry of Education (MOE) | en_US |
dc.description.sponsorship | Nanyang Technological University | en_US |
dc.language.iso | en | en_US |
dc.relation | M4081842.110 | en_US |
dc.relation | RG109/19 | en_US |
dc.relation | MOE2018-T2-1-033 | en_US |
dc.relation.ispartof | Science Advances | en_US |
dc.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). | en_US |
dc.subject | Science::Biological sciences | en_US |
dc.title | Persistent spectral-based machine learning (PerSpect ML) for protein-ligand binding affinity prediction | en_US |
dc.type | Journal Article | en |
dc.contributor.school | School of Physical and Mathematical Sciences | en_US |
dc.identifier.doi | 10.1126/sciadv.abc5329 | - |
dc.description.version | Published version | en_US |
dc.identifier.pmid | 33962954 | - |
dc.identifier.scopus | 2-s2.0-85105653469 | - |
dc.identifier.issue | 19 | en_US |
dc.identifier.volume | 7 | en_US |
dc.identifier.spage | eabc5329 | en_US |
dc.subject.keywords | Binding Energy | en_US |
dc.subject.keywords | Chemical Analysis | en_US |
dc.description.acknowledgement | This work was supported, in part, by Nanyang Technological University Startup Grant M4081842.110 and Singapore Ministry of Education Academic Research Fund Tier 1 RG109/19 and Tier 2 MOE2018-T2-1-033. | en_US |
item.fulltext | With Fulltext | - |
item.grantfulltext | open | - |
Appears in Collections: | SPMS Journal Articles |
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