Activity cliffs have a significant impact on a number of tasks relevant in medicinal chemistry and chemoinformatics such as lead optimization, development of methods to predict the biological activity and selection of queries for similarity searching. Therefore, the identification of compounds highly associated with activity cliffs in biological data sets i.e., 'activity cliff generators', is of prime significance [1]. The identification of activity cliff generators based on Structure-Activity Similarity (SAS) maps and frequency counts has been reported [1]. Herein we propose two complementary approaches to identify activity cliff generators based on the distribution of the Structure-Activity Landscape Index (SALI) values [2]. To illustrate the approach, we discuss the SAR and the activity cliff generators identified in screening data sets from our effort to identify inhibitors of the migration inhibitory factor MIF. [1] M�ndez-Lucio, O. et al. Mol. Inf., 2012, 31, 837. [2] Guha, R and Van Drie, JH. J. Chem. Inf. Model., 2008, 48, 646.

Uncovering activity cliff-forming compounds using SALI values

248th National Meeting (2014)

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