Deterministic pharmacophore detection via multiple flexible alignment of drug-like molecules

Dina Schneidman-Duhovny, Oranit Dror, Yuval Inbar, Ruth Nussinov, Haim J. Wolfson

Research output: Contribution to journalArticlepeer-review

Abstract

We present a novel highly efficient method for the detection of a pharmacophore from a set of drug-like ligands that interact with a target receptor. A pharmacophore is a spatial arrangement of physico-chemical features in a ligand that is essential for the interaction with a specific receptor. In the absence of a known three-dimensional (3D) receptor structure, a pharmacophore can be identified from a multiple structural alignment of ligand molecules. The key advantages of the presented algorithm are: (a) its ability to multiply align flexible ligands in a deterministic manner, (b) its ability to focus on subsets of the input ligands, which may share a large common substructure, resulting in the detection of both outlier molecules and alternative binding modes, and (c) its computational efficiency, which allows to detect pharmacophores shared by a large number of molecules on a standard PC. The algorithm was extensively tested on a dataset of almost 80 ligands acting on 12 different receptors. The results, which were achieved using a set of standard default parameters, were consistent with reference pharmacophores that were derived from the bound ligand-receptor complexes. The pharmacophores detected by the algorithm are expected to be a key component in the discovery of new leads by screening large databases of drug-like molecules. A user-friendly web interface is available at http://bioinfo3d.cs.tau.ac.il/pharma. Supplementary material can be found at http://bioinfo3d.cs.tau.ac.il/pharma/ reduction/.

Original languageEnglish
Pages (from-to)737-754
Number of pages18
JournalJournal of Computational Biology
Volume15
Issue number7
DOIs
StatePublished - 1 Sep 2008

Keywords

  • 3D molecular similarity
  • 3D molecular superposition
  • Computer-aided drug design (CADD)
  • Rational drug discovery

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