Literature DB >> 16426057

ErG: 2D pharmacophore descriptions for scaffold hopping.

Nikolaus Stiefl1, Ian A Watson, Knut Baumann, Andrea Zaliani.   

Abstract

An extended reduced graph approach (ErG) is presented that uses pharmacophore-type node descriptions to encode the relevant molecular properties. The basic idea of the method can be described as a hybrid approach of reduced graphs (Gillet et al. J. Chem. Inf. Comput. Sci. 2003, 43, 338-345) and binding property pairs (Kearsley et al. J. Chem. Inf. Comput. Sci. 1996, 36, 118-127). However, specific extension modifications to correctly describe the pharmacophoric properties, size, and shape of the molecules under study result in a very stable and good performance as compared to DAYLIGHT fingerprints (DFP). This is exemplified for 11 activity classes of the MDL Drug Data Report database, for which ErG performs as well or better than DFP in 10 cases. On the basis of the example data sets, the ability of ErG to switch from one chemotype to another (often referred to as "scaffold hopping") is highlighted. Additionally, possible pitfalls of reduced graph approaches as well as suitable solutions are discussed with the help of example structures. Overall, it is shown that ErG is a widely applicable method capable of identifying structurally diverse actives for a given active search query. This diversity is achieved by a high degree of molecular abstraction, which in turn results in a low dimensional descriptor vector that allows very low computation times for similarity searches.

Year:  2006        PMID: 16426057     DOI: 10.1021/ci050457y

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  21 in total

1.  Indirect similarity based methods for effective scaffold-hopping in chemical compounds.

Authors:  Nikil Wale; Ian A Watson; George Karypis
Journal:  J Chem Inf Model       Date:  2008-04-11       Impact factor: 4.956

2.  Analysis and use of fragment-occurrence data in similarity-based virtual screening.

Authors:  Shereena M Arif; John D Holliday; Peter Willett
Journal:  J Comput Aided Mol Des       Date:  2009-06-18       Impact factor: 3.686

3.  Molecular Scaffold Hopping via Holistic Molecular Representation.

Authors:  Francesca Grisoni; Gisbert Schneider
Journal:  Methods Mol Biol       Date:  2021

Review 4.  Classification of scaffold-hopping approaches.

Authors:  Hongmao Sun; Gregory Tawa; Anders Wallqvist
Journal:  Drug Discov Today       Date:  2011-10-26       Impact factor: 7.851

Review 5.  A review of mathematical representations of biomolecular data.

Authors:  Duc Duy Nguyen; Zixuan Cang; Guo-Wei Wei
Journal:  Phys Chem Chem Phys       Date:  2020-02-26       Impact factor: 3.676

6.  FTree query construction for virtual screening: a statistical analysis.

Authors:  Christof Gerlach; Howard Broughton; Andrea Zaliani
Journal:  J Comput Aided Mol Des       Date:  2008-01-24       Impact factor: 3.686

7.  Are 2D fingerprints still valuable for drug discovery?

Authors:  Kaifu Gao; Duc Duy Nguyen; Vishnu Sresht; Alan M Mathiowetz; Meihua Tu; Guo-Wei Wei
Journal:  Phys Chem Chem Phys       Date:  2020-04-29       Impact factor: 3.676

Review 8.  Rapid analysis of pharmacology for infectious diseases.

Authors:  Andrew L Hopkins; G Richard Bickerton; Ian M Carruthers; Stephen K Boyer; Harvey Rubin; John P Overington
Journal:  Curr Top Med Chem       Date:  2011       Impact factor: 3.295

9.  Applying machine learning techniques to predict the properties of energetic materials.

Authors:  Daniel C Elton; Zois Boukouvalas; Mark S Butrico; Mark D Fuge; Peter W Chung
Journal:  Sci Rep       Date:  2018-06-13       Impact factor: 4.379

10.  Exploring protein hotspots by optimized fragment pharmacophores.

Authors:  Dávid Bajusz; Warren S Wade; Grzegorz Satała; Andrzej J Bojarski; Janez Ilaš; Jessica Ebner; Florian Grebien; Henrietta Papp; Ferenc Jakab; Alice Douangamath; Daren Fearon; Frank von Delft; Marion Schuller; Ivan Ahel; Amanda Wakefield; Sándor Vajda; János Gerencsér; Péter Pallai; György M Keserű
Journal:  Nat Commun       Date:  2021-05-27       Impact factor: 14.919

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