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A novel hybrid ultrafast shape descriptor method for use in virtual screening.

Cannon EO, Nigsch F, Mitchell JB - Chem Cent J (2008)

Bottom Line: In a 10-fold Monte Carlo cross-validation this dataset was partitioned into three distinct parts for training, optimisation of an internal threshold that we introduced, and validation of the resulting model.The MACCS key descriptor was not combined with UF5, due to similar performance of UF5 and UF4.These figures of merit include recall in the top 1% and top 5% of the ranked validation sets, precision, F-measure, area under the Receiver Operating Characteristic curve and Matthews Correlation Coefficient.

View Article: PubMed Central - HTML - PubMed

Affiliation: Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK. eoc21@cam.ac.uk

ABSTRACT

Background: We have introduced a new Hybrid descriptor composed of the MACCS key descriptor encoding topological information and Ballester and Richards' Ultrafast Shape Recognition (USR) descriptor. The latter one is calculated from the moments of the distribution of the interatomic distances, and in this work we also included higher moments than in the original implementation.

Results: The performance of this Hybrid descriptor is assessed using Random Forest and a dataset of 116,476 molecules. Our dataset includes 5,245 molecules in ten classes from the 2005 World Anti-Doping Agency (WADA) dataset and 111,231 molecules from the National Cancer Institute (NCI) database. In a 10-fold Monte Carlo cross-validation this dataset was partitioned into three distinct parts for training, optimisation of an internal threshold that we introduced, and validation of the resulting model. The standard errors obtained were used to assess statistical significance of observed improvements in performance of our new descriptor.

Conclusion: The Hybrid descriptor was compared to the MACCS key descriptor, USR with the first three (USR), four (UF4) and five (UF5) moments, and a combination of MACCS with USR (three moments). The MACCS key descriptor was not combined with UF5, due to similar performance of UF5 and UF4. Superior performance in terms of all figures of merit was found for the MACCS/UF4 Hybrid descriptor with respect to all other descriptors examined. These figures of merit include recall in the top 1% and top 5% of the ranked validation sets, precision, F-measure, area under the Receiver Operating Characteristic curve and Matthews Correlation Coefficient.

No MeSH data available.


Related in: MedlinePlus

Mesokurtic, leptokurtic and platykurtic.
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Figure 1: Mesokurtic, leptokurtic and platykurtic.

Mentions: The kurtosis K is a measure of the peakedness of the distribution (see Figure 1).


A novel hybrid ultrafast shape descriptor method for use in virtual screening.

Cannon EO, Nigsch F, Mitchell JB - Chem Cent J (2008)

Mesokurtic, leptokurtic and platykurtic.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2275267&req=5

Figure 1: Mesokurtic, leptokurtic and platykurtic.
Mentions: The kurtosis K is a measure of the peakedness of the distribution (see Figure 1).

Bottom Line: In a 10-fold Monte Carlo cross-validation this dataset was partitioned into three distinct parts for training, optimisation of an internal threshold that we introduced, and validation of the resulting model.The MACCS key descriptor was not combined with UF5, due to similar performance of UF5 and UF4.These figures of merit include recall in the top 1% and top 5% of the ranked validation sets, precision, F-measure, area under the Receiver Operating Characteristic curve and Matthews Correlation Coefficient.

View Article: PubMed Central - HTML - PubMed

Affiliation: Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK. eoc21@cam.ac.uk

ABSTRACT

Background: We have introduced a new Hybrid descriptor composed of the MACCS key descriptor encoding topological information and Ballester and Richards' Ultrafast Shape Recognition (USR) descriptor. The latter one is calculated from the moments of the distribution of the interatomic distances, and in this work we also included higher moments than in the original implementation.

Results: The performance of this Hybrid descriptor is assessed using Random Forest and a dataset of 116,476 molecules. Our dataset includes 5,245 molecules in ten classes from the 2005 World Anti-Doping Agency (WADA) dataset and 111,231 molecules from the National Cancer Institute (NCI) database. In a 10-fold Monte Carlo cross-validation this dataset was partitioned into three distinct parts for training, optimisation of an internal threshold that we introduced, and validation of the resulting model. The standard errors obtained were used to assess statistical significance of observed improvements in performance of our new descriptor.

Conclusion: The Hybrid descriptor was compared to the MACCS key descriptor, USR with the first three (USR), four (UF4) and five (UF5) moments, and a combination of MACCS with USR (three moments). The MACCS key descriptor was not combined with UF5, due to similar performance of UF5 and UF4. Superior performance in terms of all figures of merit was found for the MACCS/UF4 Hybrid descriptor with respect to all other descriptors examined. These figures of merit include recall in the top 1% and top 5% of the ranked validation sets, precision, F-measure, area under the Receiver Operating Characteristic curve and Matthews Correlation Coefficient.

No MeSH data available.


Related in: MedlinePlus