Literature DB >> 20426451

Extended-connectivity fingerprints.

David Rogers1, Mathew Hahn.   

Abstract

Extended-connectivity fingerprints (ECFPs) are a novel class of topological fingerprints for molecular characterization. Historically, topological fingerprints were developed for substructure and similarity searching. ECFPs were developed specifically for structure-activity modeling. ECFPs are circular fingerprints with a number of useful qualities: they can be very rapidly calculated; they are not predefined and can represent an essentially infinite number of different molecular features (including stereochemical information); their features represent the presence of particular substructures, allowing easier interpretation of analysis results; and the ECFP algorithm can be tailored to generate different types of circular fingerprints, optimized for different uses. While the use of ECFPs has been widely adopted and validated, a description of their implementation has not previously been presented in the literature.

Mesh:

Year:  2010        PMID: 20426451     DOI: 10.1021/ci100050t

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


  740 in total

1.  Development and implementation of (Q)SAR modeling within the CHARMMing web-user interface.

Authors:  Iwona E Weidlich; Yuri Pevzner; Benjamin T Miller; Igor V Filippov; H Lee Woodcock; Bernard R Brooks
Journal:  J Comput Chem       Date:  2014-11-03       Impact factor: 3.376

2.  Identifying mechanism-of-action targets for drugs and probes.

Authors:  Elisabet Gregori-Puigjané; Vincent Setola; Jérôme Hert; Brenda A Crews; John J Irwin; Eugen Lounkine; Lawrence Marnett; Bryan L Roth; Brian K Shoichet
Journal:  Proc Natl Acad Sci U S A       Date:  2012-06-18       Impact factor: 11.205

3.  Analysis of high-throughput screening assays using cluster enrichment.

Authors:  Minya Pu; Tomoko Hayashi; Howard Cottam; Joseph Mulvaney; Michelle Arkin; Maripat Corr; Dennis Carson; Karen Messer
Journal:  Stat Med       Date:  2012-07-05       Impact factor: 2.373

4.  Small molecules of different origins have distinct distributions of structural complexity that correlate with protein-binding profiles.

Authors:  Paul A Clemons; Nicole E Bodycombe; Hyman A Carrinski; J Anthony Wilson; Alykhan F Shamji; Bridget K Wagner; Angela N Koehler; Stuart L Schreiber
Journal:  Proc Natl Acad Sci U S A       Date:  2010-10-18       Impact factor: 11.205

5.  A lead discovery strategy driven by a comprehensive analysis of proteases in the peptide substrate space.

Authors:  Sai Chetan K Sukuru; Florian Nigsch; Jean Quancard; Martin Renatus; Rajiv Chopra; Natasja Brooijmans; Dmitri Mikhailov; Zhan Deng; Allen Cornett; Jeremy L Jenkins; Ulrich Hommel; John W Davies; Meir Glick
Journal:  Protein Sci       Date:  2010-11       Impact factor: 6.725

6.  Design of chemical space networks using a Tanimoto similarity variant based upon maximum common substructures.

Authors:  Bijun Zhang; Martin Vogt; Gerald M Maggiora; Jürgen Bajorath
Journal:  J Comput Aided Mol Des       Date:  2015-09-29       Impact factor: 3.686

7.  Dark chemical matter as a promising starting point for drug lead discovery.

Authors:  Anne Mai Wassermann; Eugen Lounkine; Dominic Hoepfner; Gaelle Le Goff; Frederick J King; Christian Studer; John M Peltier; Melissa L Grippo; Vivian Prindle; Jianshi Tao; Ansgar Schuffenhauer; Iain M Wallace; Shanni Chen; Philipp Krastel; Amanda Cobos-Correa; Christian N Parker; John W Davies; Meir Glick
Journal:  Nat Chem Biol       Date:  2015-10-19       Impact factor: 15.040

8.  Shallow Representation Learning via Kernel PCA Improves QSAR Modelability.

Authors:  Stefano E Rensi; Russ B Altman
Journal:  J Chem Inf Model       Date:  2017-08-07       Impact factor: 4.956

Review 9.  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

10.  Tuning HERG out: antitarget QSAR models for drug development.

Authors:  Rodolpho C Braga; Vinicius M Alves; Meryck F B Silva; Eugene Muratov; Denis Fourches; Alexander Tropsha; Carolina H Andrade
Journal:  Curr Top Med Chem       Date:  2014       Impact factor: 3.295

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.