MOLER: Incorporate Molecule-Level Reward to Enhance Deep Generative Model for Molecule Optimization is a research paper published in IEEE Transactions on Knowledge and Data Engineering (2021). On theSindex it has a DataRank of 0.406. It has been cited 14 times.
Scored on demand from live citation data
DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?
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Base Score Contribution
0.406
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
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Grant: SCH-2014438
NSF
Grant: PPoSS 2028839
NSF
Grant: IIS-1838042
NIH
Grant: R01 1R01NS107291-01
NINDS NIH HHS
Grant: R01 NS107291
National Science Foundation
Grant: 1838042
BigData:IA:Collaborative Research: TIMES: A tensor factorization platform for spatio-temporal data
National Institutes of Health
Grant: 1R01NS107291-01
Big Data and Deep Learning for the Interictal-Ictal-Injury Continuum
National Science Foundation
Grant: 2014438
SCH:INT: Collaborative Research: Deep Sense: Interpretable Deep Learning for Zero-effort Phenotype Sensing and Its Application to Sleep Medicine
National Science Foundation
Grant: 2028839
Collaborative Research: PPoSS: Planning: Integrated Scalable Platform for Privacy-aware Collaborative Learning and Inference
OSF Healthcare
FWCI
1.45
Citation Percentile
0.8%
Citation Trend
Fields of Study
Keywords