Abstract:
:Scoring functions (SFs) based on complex machine learning (ML) algorithms have gradually emerged as a promising alternative to overcome the weaknesses of classical SFs. However, extensive efforts have been devoted to the development of SFs based on new protein-ligand interaction representations and advanced alternative ML algorithms instead of the energy components obtained by the decomposition of existing SFs. Here, we propose a new method named energy auxiliary terms learning (EATL), in which the scoring components are extracted and used as the input for the development of three levels of ML SFs including EATL SFs, docking-EATL SFs and comprehensive SFs with ascending VS performance. The EATL approach not only outperforms classical SFs for the absolute performance (ROC) and initial enrichment (BEDROC) but also yields comparable performance compared with other advanced ML-based methods on the diverse subset of Directory of Useful Decoys: Enhanced (DUD-E). The test on the relatively unbiased actives as decoys (AD) dataset also proved the effectiveness of EATL. Furthermore, the idea of learning from SF components to yield improved screening power can also be extended to other docking programs and SFs available.
journal_name
Brief Bioinformjournal_title
Briefings in bioinformaticsauthors
Xiong GL,Ye WL,Shen C,Lu AP,Hou TJ,Cao DSdoi
10.1093/bib/bbaa094subject
Has Abstractpub_date
2020-06-04 00:00:00eissn
1467-5463issn
1477-4054pii
5851268pub_type
杂志文章abstract::We discuss and review different ways to map cellular components and their temporal interaction with other such components to different non-spatially explicit mathematical models. The essential choices made in the literature are between discrete and continuous state spaces, between rule and event-based state updates an...
journal_title:Briefings in bioinformatics
pub_type: 杂志文章,评审
doi:10.1093/bib/bbp062
更新日期:2010-01-01 00:00:00
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journal_title:Briefings in bioinformatics
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journal_title:Briefings in bioinformatics
pub_type: 杂志文章
doi:10.1093/bib/bbz094
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pub_type: 杂志文章
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journal_title:Briefings in bioinformatics
pub_type: 杂志文章
doi:10.1093/bib/5.3.237
更新日期:2004-09-01 00:00:00
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journal_title:Briefings in bioinformatics
pub_type: 杂志文章
doi:10.1093/bib/bbz151
更新日期:2021-01-18 00:00:00
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doi:10.1093/bib/bbz028
更新日期:2020-05-21 00:00:00
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pub_type: 杂志文章
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更新日期:2019-07-19 00:00:00
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更新日期:2020-04-06 00:00:00
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doi:10.1093/bib/bbx148
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更新日期:2018-07-20 00:00:00
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