Literature DB >> 27565795

mRAISE: an alternative algorithmic approach to ligand-based virtual screening.

Mathias M von Behren1, Stefan Bietz1, Eva Nittinger1, Matthias Rarey2.   

Abstract

Ligand-based virtual screening is a well established method to find new lead molecules in todays drug discovery process. In order to be applicable in day to day practice, such methods have to face multiple challenges. The most important part is the reliability of the results, which can be shown and compared in retrospective studies. Furthermore, in the case of 3D methods, they need to provide biologically relevant molecular alignments of the ligands, that can be further investigated by a medicinal chemist. Last but not least, they have to be able to screen large databases in reasonable time. Many algorithms for ligand-based virtual screening have been proposed in the past, most of them based on pairwise comparisons. Here, a new method is introduced called mRAISE. Based on structural alignments, it uses a descriptor-based bitmap search engine (RAISE) to achieve efficiency. Alignments created on the fly by the search engine get evaluated with an independent shape-based scoring function also used for ranking of compounds. The correct ranking as well as the alignment quality of the method are evaluated and compared to other state of the art methods. On the commonly used Directory of Useful Decoys dataset mRAISE achieves an average area under the ROC curve of 0.76, an average enrichment factor at 1 % of 20.2 and an average hit rate at 1 % of 55.5. With these results, mRAISE is always among the top performing methods with available data for comparison. To access the quality of the alignments calculated by ligand-based virtual screening methods, we introduce a new dataset containing 180 prealigned ligands for 11 diverse targets. Within the top ten ranked conformations, the alignment closest to X-ray structure calculated with mRAISE has a root-mean-square deviation of less than 2.0 Å for 80.8 % of alignment pairs and achieves a median of less than 2.0 Å for eight of the 11 cases. The dataset used to rate the quality of the calculated alignments is freely available at http://www.zbh.uni-hamburg.de/mraise-dataset.html . The table of all PDB codes contained in the ensembles can be found in the supplementary material. The software tool mRAISE is freely available for evaluation purposes and academic use (see http://www.zbh.uni-hamburg.de/raise ).

Keywords:  3D similarity searching; Lead discovery; Ligand-based; Molecular similarity; Structural alignment; Virtual screening

Mesh:

Substances:

Year:  2016        PMID: 27565795     DOI: 10.1007/s10822-016-9940-1

Source DB:  PubMed          Journal:  J Comput Aided Mol Des        ISSN: 0920-654X            Impact factor:   3.686


  21 in total

1.  Ligand-based structural hypotheses for virtual screening.

Authors:  Ajay N Jain
Journal:  J Med Chem       Date:  2004-02-12       Impact factor: 7.446

2.  Comparative evaluation of 3D virtual ligand screening methods: impact of the molecular alignment on enrichment.

Authors:  David Giganti; Hélène Guillemain; Jean-Louis Spadoni; Michael Nilges; Jean-François Zagury; Matthieu Montes
Journal:  J Chem Inf Model       Date:  2010-06-28       Impact factor: 4.956

3.  Atomic property fields: generalized 3D pharmacophoric potential for automated ligand superposition, pharmacophore elucidation and 3D QSAR.

Authors:  Maxim Totrov
Journal:  Chem Biol Drug Des       Date:  2007-12-07       Impact factor: 2.817

4.  How to optimize shape-based virtual screening: choosing the right query and including chemical information.

Authors:  Johannes Kirchmair; Simona Distinto; Patrick Markt; Daniela Schuster; Gudrun M Spitzer; Klaus R Liedl; Gerhard Wolber
Journal:  J Chem Inf Model       Date:  2009-03       Impact factor: 4.956

5.  ShaEP: molecular overlay based on shape and electrostatic potential.

Authors:  Mikko J Vainio; J Santeri Puranen; Mark S Johnson
Journal:  J Chem Inf Model       Date:  2009-02       Impact factor: 4.956

6.  CONFECT: conformations from an expert collection of torsion patterns.

Authors:  Christin Schärfer; Tanja Schulz-Gasch; Jérôme Hert; Lennart Heinzerling; Benjamin Schulz; Therese Inhester; Martin Stahl; Matthias Rarey
Journal:  ChemMedChem       Date:  2013-08-08       Impact factor: 3.466

7.  Pharao: pharmacophore alignment and optimization.

Authors:  Jonatan Taminau; Gert Thijs; Hans De Winter
Journal:  J Mol Graph Model       Date:  2008-04-11       Impact factor: 2.518

8.  FLEXS: a method for fast flexible ligand superposition.

Authors:  C Lemmen; T Lengauer; G Klebe
Journal:  J Med Chem       Date:  1998-11-05       Impact factor: 7.446

9.  Fast protein binding site comparison via an index-based screening technology.

Authors:  Mathias M von Behren; Andrea Volkamer; Angela M Henzler; Karen T Schomburg; Sascha Urbaczek; Matthias Rarey
Journal:  J Chem Inf Model       Date:  2013-02-07       Impact factor: 4.956

10.  Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking.

Authors:  Michael M Mysinger; Michael Carchia; John J Irwin; Brian K Shoichet
Journal:  J Med Chem       Date:  2012-07-05       Impact factor: 7.446

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  2 in total

1.  Ligand-based virtual screening under partial shape constraints.

Authors:  Mathias M von Behren; Matthias Rarey
Journal:  J Comput Aided Mol Des       Date:  2017-03-18       Impact factor: 3.686

2.  On the Value of Using 3D Shape and Electrostatic Similarities in Deep Generative Methods.

Authors:  Giovanni Bolcato; Esther Heid; Jonas Boström
Journal:  J Chem Inf Model       Date:  2022-03-10       Impact factor: 4.956

  2 in total

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