Benchmarking Study of Parameter Variation When Using Signature Fingerprints Together with Support Vector Machines
Alvarsson J, Eklund M, Andersson C, Carlsson L, Spjuth O, Wikberg JES.
Benchmarking Study of Parameter Variation When Using Signature Fingerprints Together with Support Vector Machines.
Journal of Chemical Information and Modeling. 54, 11, 3211--3217. (2014). DOI: 10.1021/ci500344v
QSAR modeling using molecular signatures and support vector machines with a radial basis function is increasingly used for virtual screening in the drug discovery field. This method has three free parameters: C, γ, and signature height. C is a penalty parameter that limits overfitting, γ controls the width of the radial basis function kernel, and the signature height determines how much of the molecule is described by each atom signature. Determination of optimal values for these parameters is time-consuming. Good default values could therefore save considerable computational cost. The goal of this project was to investigate whether such default values could be found by using seven public QSAR data sets spanning a wide range of end points and using both a bit version and a count version of the molecular signatures. On the basis of the experiments performed, we recommend a parameter set of heights 0 to 2 for the count version of the signature fingerprints and heights 0 to 3 for the bit version. These are in combination with a support vector machine using C in the range of 1 to 100 and γ in the range of 0.001 to 0.1. When data sets are small or longer run times are not a problem, then there is reason to consider the addition of height 3 to the count fingerprint and a wider grid search. However, marked improvements should not be expected.