Wavelet Approximation of GRID Fields: Application to Quantitative Structure-Activity Relationships

Richard L Martin; Eleanor Gardiner; Valerie J Gillet; Jordi Muñoz-Muriedas; Stefan Senger

doi:10.1002/minf.201000066

Wavelet Approximation of GRID Fields: Application to Quantitative Structure-Activity Relationships

Mol Inform. 2010 Sep 17;29(8-9):603-20. doi: 10.1002/minf.201000066. Epub 2010 Sep 23.

Authors

Richard L Martin¹, Eleanor Gardiner¹, Valerie J Gillet², Jordi Muñoz-Muriedas³, Stefan Senger³

Affiliations

¹ Information School, University of Sheffield, Regent Court, 211 Portobello Street, Sheffield S1 4DP, UK.
² Information School, University of Sheffield, Regent Court, 211 Portobello Street, Sheffield S1 4DP, UK. v.gillet@sheffield.ac.uk.
³ Computational and Structural Chemistry, GlaxoSmithKline Research & Development, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, UK.

PMID: 27463455
DOI: 10.1002/minf.201000066

Abstract

Molecular interaction fields such as those computed by the GRID program are widely used in applications such as virtual screening, molecular docking and 3D-QSAR modelling. They characterise molecules according to their favourable interaction sites and therefore enable predictions to be made on how molecules might interact. The fields are, however, comprised of a very large number of data points which presents difficulties for many applications. For example, there are likely to be high degrees of correlation between the variables which can lead to misleading results in 3D-QSAR. We describe the use of wavelet methods for approximating such data into a much smaller number of variables. We present a number of validation experiments, including use of the approximated GRIDs in 3D-QSAR, and demonstrate that wavelet approximation at high levels of data compression preserves the information content in GRID fields while significantly reducing computational requirements.

Keywords: Chemoinformatics; GRID fields; Molecular similarity; Structure-activity relationships; Wavelets.