期刊
JOURNAL OF CHEMICAL INFORMATION AND MODELING
卷 57, 期 7, 页码 1579-1590出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.jcim.7b00153
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资金
- NIH/NCI [P30 CA014520]
- NIH/NCATS [UL1TR000427]
- NIH [1U54AI117924]
- NSF [1321762, 1148698]
- UW-Madison Office of the Vice Chancellor for Research and Graduate Education
- Wisconsin Alumni Research Foundation
- Division Of Physics
- Direct For Mathematical & Physical Scien [1148698] Funding Source: National Science Foundation
- Office of Advanced Cyberinfrastructure (OAC)
- Direct For Computer & Info Scie & Enginr [1321762] Funding Source: National Science Foundation
In structure-based virtual screening, compound ranking through a consensus of scores from a variety of docking programs or scoring functions, rather than ranking by scores from a single program, provides better predictive performance and reduces target performance variability. Here we compare traditional consensus scoring methods with a novel, unsupervised gradient boosting approach. We also, observed increased score variation among active ligands and developed a statistical mixture model consensus score based On combining score means and variances. To evaluate performance, we used the common performance metrics ROCAUC and EF1 on 21 benchmark targets from DUD-E. Traditional consensus methods, such as taking the mean of quantile normalized docking stores, outperformed individual docking methods and are more robust to target variation. The mixture model and gradient boosting provided further improvements over the traditional consensus methods. These methods are readily applicable to new targets in academic research and overcome the potentially poor performance of using a single docking method on a new target.
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