Combining pharmacophore fingerprints and PLS-discriminant analysis for virtual screening and SAR elucidation

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Sune Askjær, Morten Langgård

The criterion of success for the initial stages of a ligand-based drug-discovery project is dual. First, a set of suitable lead compounds has to be identified. Second, a level of a preliminary structure-activity relationship (SAR) of the identified ligands has to be established in order to guide the lead optimization toward a final drug candidate. This paper presents a combined approach to solving these two problems of ligand-based virtual screening and elucidation of SAR based on interplay between pharmacophore fingerprints and interpretation of PLS-discriminant analysis (PLS-DA) models. The virtual screening capability of the PLS-DA method is compared to group fusion maximum similarity searching in a test using four graph-based pharmacophore fingerprints over a range of 10 diverse targets. The PLS-DA method was generally found to do better than the Smax method. The GpiDAPH3 and PCH fingerprints proved superior to the TGT and TGD fingerprints. Examples of SAR elucidation based on PLS-DA model interpretation of model coefficients using a reversible pharmacophore fingerprint are given. In addition, we tested the hypothesis that feature combinations coming from the analysis of two-dimensional (2D) pharmacophore fingerprints could be used to elucidate a three-dimensional pharmacophore (Williams, C. Mol. Diversity 2006, 10 (3), 311-332). This test was performed by mapping of pharmacophore triplets found by the PLS-DA model to be important for activity onto relevant ligands aligned by the protein-binding site known from X-ray complexes. The result of this analysis assists in explaining the efficiency of 2D pharmacophore fingerprints as descriptors in virtual screening.
Original languageEnglish
JournalJournal of Chemical Information and Modeling
Volume48
Issue number3
Pages (from-to)476-488
ISSN1549-9596
DOIs
Publication statusPublished - 2008

Bibliographical note

Keywords: Discriminant Analysis; Models, Molecular; Pharmaceutical Preparations; Structure-Activity Relationship

ID: 10157677