Boosting Virtual Screening Enrichments with Data Fusion: Coalescing Hits from Two-Dimensional Fingerprints, Shape, and Docking

2013 ◽  
Vol 53 (7) ◽  
pp. 1531-1542 ◽  
Author(s):  
G. Madhavi Sastry ◽  
V. S. Sandeep Inakollu ◽  
Woody Sherman
Keyword(s):  
Virtual Screening ◽  
Data Fusion ◽  
Two Dimensional

Two Dimensional Multi-Frequency Eddy Current Data Fusion

1996 ◽  
pp. 2125-2132 ◽  
Author(s):  
M. Mina ◽  
J. Yim ◽  
S. S. Udpa ◽  
L. Udpa ◽  
W. Lord ◽  
...  
Keyword(s):  
Data Fusion ◽  
Eddy Current ◽  
Current Data ◽  
Two Dimensional

Analysis of Data Fusion Methods in Virtual Screening:  Similarity and Group Fusion

2006 ◽  
Vol 46 (6) ◽  
pp. 2206-2219 ◽  
Author(s):  
Martin Whittle ◽  
Valerie J. Gillet ◽  
Peter Willett ◽  
Jens Loesel
Keyword(s):  
Virtual Screening ◽  
Data Fusion ◽  
Fusion Methods ◽  
Group Fusion

Analysis of Data Fusion Methods in Virtual Screening:  Theoretical Model

2006 ◽  
Vol 46 (6) ◽  
pp. 2193-2205 ◽  
Author(s):  
Martin Whittle ◽  
Valerie J. Gillet ◽  
Peter Willett ◽  
Jens Loesel
Keyword(s):  
Theoretical Model ◽  
Virtual Screening ◽  
Data Fusion ◽  
Fusion Methods

Research on the Zero Drift of Sensor Based on Data Fusion Technology

2012 ◽  
Vol 546-547 ◽  
pp. 696-701
Author(s):  
Xiao Ming Li ◽  
Zhi Shu Feng ◽  
Zhong Chun Cai ◽  
Bin Xie ◽  
Chang Jie Liu
Keyword(s):  
Experimental Data ◽  
Mathematical Model ◽  
Regression Analysis ◽  
Data Fusion ◽  
Zero Drift ◽  
Two Dimensional ◽  
Fusion Technology ◽  
Input Output ◽  
Influence Of Temperature ◽  
Fusion Methods

The zero drift of sensor and its solution are presented in this paper. The basic principle of data fusion with two-dimensional regression analysis is expounded and the experimental data of any pressure sensor are fused with the two-dimensional regression analysis. Besides, the input-output mathematical model of sensor under the influence of temperature is established. At last, the linear, fitting and fusion methods are compared.

scholarly journals Analysis of the effects of related fingerprints on molecular similarity using an eigenvalue entropy approach

2019 ◽  
Author(s):  
Hiroyuki Kuwahara ◽  
Xin Gao
Keyword(s):  
Biological Activity ◽  
Virtual Screening ◽  
Molecular Similarity ◽  
Chemical Compounds ◽  
Structural Similarity ◽  
Optimal Selection ◽  
Two Dimensional ◽  
Chemical Fingerprints ◽  
Selection Of ◽  
Similarity Scores

AbstractTwo-dimensional (2D) chemical fingerprints are widely used as numerical features for the quantification of structural similarity of chemical compounds, which is an important step in similarity-based virtual screening (VS). Here, using an eigenvalue-based entropy approach, we sought to identify 2D fingerprints with little to no contribution to shaping the eigenvalue distribution of the feature matrix as related fingerprints and examined the degree to which these related 2D fingerprints influence molecular similarity scores via Tanimoto coefficient. We found that there are many related fingerprints in publicly available fingerprint schemes and that their presence in the feature set tends to decrease the similarity scores. Our results have implication in the optimal selection of 2D fingerprints and the identification of potential hits for compounds with target biological activity in VS.

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