Identification of Diverse Database Subsets using Property‐Based and Fragment‐Based Molecular Descriptions
Mark R. Ashton, John M. Barnard, Florence Casset, Michael H. Charlton, Geoff M. Downs, Alain‐Dominique Gorse, John D. Holliday, Roger Lahana, Peter Willett · Quantitative Structure-Activity Relationships · 2002
Abstract This paper reports a comparison of calculated molecular properties and of 2D fragment bit‐strings when used for the selection of structurally diverse subsets of a file of 44295 compounds. MaxMin dissimilarity‐based selection and k ‐means cluster‐based selection are used to select subsets containing between 1% and 20% of the file. Investigation of the numbers of bioactive molecules in the selected subsets suggest: that the MaxMin subsets are noticeably superior to the k ‐means subsets; that the property‐based descriptors are marginally superior to the fragment‐based descriptors; and that both approaches are noticeably superior to random selection.