← Leaderboards
Yingkai Zhang
New York University
$7,536,680
Attributed
$8,337,831
Total exposure
4
Grants
3
Lead (contact PI)
Attributed= this PI's even-split share of every grant they're on (the fair, additive number). Exposure = full size of all those grants.
Funding over time
peak $786.3K · FY2007–25$1M$750K$500K$250K$0
'07
'08
'09
'10
'11
'12
'13
'14
'15
'16
'17
'18
'19
'20
'21
'22
'23
'24
'25
Funding mix
By agency
NIH$8,337,831 · 4
By mechanism
R01$3,972,575 · 2
R35$3,969,939 · 1
R21$395,317 · 1
Top collaborators
- Paramjit S Arora5 shared
Most similar at New York University
Same institution · by research overlap
- Shohei Koide$16,409,478
- Nathaniel J. Traaseth$7,360,173
- Da-Neng Wang$18,349,403
- Joseph W Meisel$168,029
- Paramjit S Arora$12,984,973
Others in their field
Top investigators on “Design”
- Lawrence Corey · Fred Hutchinson Cancer Center$736,542,541
- Sonia M Thomas · Research Triangle Institute$700,865,642
- Tracy L Nolen · Research Triangle Institute$474,487,152
- David Heimbrook · Leidos Biomedical Research, Inc.$460,267,569
- Jeffrey P Krischer · University Of South Florida$427,700,530
- David R. Weir · University Of Michigan At Ann Arbor$385,316,144
Research focus
DesignProteinsMalignant NeoplasmsComputing MethodologiesBiologicalDrug TargetingBaseEnzymesDockingSpecificityAreaMolecular DynamicsGene Expression RegulationFree EnergyInsightLeadComputer StudiesInnovationBindingDrug DevelopmentDrug DiscoveryAffinityLengthComputerized Tools
Grant awards (24)
Computational modulator design and machine learning to target protein-protein interactions$589,588
R35 · FY2025 · GM · contact PI
Computational modulator design and machine learning to target protein-protein interactions$590,705
R35 · FY2024 · GM · contact PI
Computational modulator design and machine learning to target protein-protein interactions$588,935
R35 · FY2023 · GM · contact PI
Computational modulator design and machine learning to target protein-protein interactions$556,806
R35 · FY2022 · GM · contact PI
Computational modulator design and machine learning to target protein-protein interactions$556,806
R35 · FY2021 · GM · contact PI
Computational modulator design and machine learning to target protein-protein interactions$491,993
R35 · FY2020 · GM · contact PI
Computational inhibitor design to target protein-protein interactions$371,180
R01 · FY2019 · GM
Computational modulator design and machine learning to target protein-protein interactions$297,553
R35 · FY2019 · GM · contact PI
Computational inhibitor design to target protein-protein interactions$445,443
R01 · FY2018 · GM
Computational modulator design and machine learning to target protein-protein interactions$297,553
R35 · FY2018 · GM · contact PI
Computational inhibitor design to target protein-protein interactions$43,320
R01 · FY2018 · GM
Computational inhibitor design to target protein-protein interactions$371,180
R01 · FY2017 · GM
Computational inhibitor design to target protein-protein interactions$371,180
R01 · FY2016 · GM
Computational Studies of Histone Modifications$269,579
R01 · FY2016 · GM · contact PI
Computational Studies of Histone Modifications$270,231
R01 · FY2015 · GM · contact PI
Computational Studies of Histone Modifications$284,379
R01 · FY2014 · GM · contact PI
Computational Studies of Histone Modifications$293,166
R01 · FY2013 · GM · contact PI
Force field development for zinc metalloproteins$186,391
R21 · FY2012 · GM · contact PI
Computational Studies of Histone Modifications$246,848
R01 · FY2011 · GM · contact PI
Force field development for zinc metalloproteins$208,926
R21 · FY2011 · GM · contact PI
Computational Studies of Histone Modifications$249,629
R01 · FY2010 · GM · contact PI
Computational Studies of Histone Modifications$252,431
R01 · FY2009 · GM · contact PI
Computational Studies of Histone Modifications$252,257
R01 · FY2008 · GM · contact PI
Computational Studies of Histone Modifications$251,752
R01 · FY2007 · GM · contact PI