Predicting patterns of biological performance using chemical substructure features Diego Borges-Rivera 08/04/08

Introduction  cheminformatics – allow us to computationally describe similarity  synthetic chemists – describe through visual inspection  we will describe compounds by the presence of chemical substructures  we will attempt to identify sets of substructures that predict biological performance

Previous work  Clemons/Kahne/Wagner et al. -- disaccharide profiling in multiple cell states  found sets of substructures relevant to biological activity patterns  substructures highly specific to disaccharides substructures

Biological performance profile  400 compounds, 8 assays in duplicate  tested for cell proliferation in 8 different cell lines  class labels are active (A) or inactive (I) active compound

What are fingerprints?  compound collection fed into commercial software  each substructure = 1 bit  the fingerprint shows which substructures are present substructure #1725 substructure #886 substructure #7017

Overview of cheminformatic methods  produced fingerprints  7700 total substructures  filtered set  left 2166 substructures

feature (substructure) selection to find predictive subsets evaluate methods for predictive value Overview of computational methods  two steps independent of each other

ReliefF: substructure selection weights Top 5 Bottom 5

K nearest neighbors (knn): predictive accuracy  Examples: k = 2, 5 compound being classified = ?

Similarity between compounds  similarity between two fingerprints  Tanimoto coefficient  this is used twice: (1) in ReliefF (2) in knn Example: Compound a: Compound b: Tanimoto coefficient = 1 / 2 =.5

Cross-validation: predictive accuracy  10 subsets  test set: one of the subsets  training set: the remaining subsets test set training set

Picking parameters for methods  which parameters produce the best predictive accuracies number of neighbors used in ReliefF {1, 2, 4, etc} number of neighbors used in knn {1, 2, 4, etc} number of ReliefF substructures used to predict classes in knn {1, 20, 100, etc}

Picking number of substructures predictive accuracy all number of substructures used to predict

Group of substructures best able to predict

Future work  multi-class  different feature selection

Acknowledgements Computational Chemical Biology Joshua Gilbert Paul Clemons Hyman Carrinski Summer Research Program in Genomics Shawna Young Lucia Vielma Maura Silverstein