Literature DB >> 15534697

Molecular similarity: a key technique in molecular informatics.

Andreas Bender1, Robert C Glen.   

Abstract

Molecular Informatics utilises many ideas and concepts to find relationships between molecules. The concept of similarity, where molecules may be grouped according to their biological effects or physicochemical properties has found extensive use in drug discovery. Some areas of particular interest have been in lead discovery and compound optimisation. For example, in designing libraries of compounds for lead generation, one approach is to design sets of compounds "similar" to known active compounds in the hope that alternative molecular structures are found that maintain the properties required while enhancing e.g. patentability, medicinal chemistry opportunities or even in achieving optimised pharmacokinetic profiles. Thus the practical importance of the concept of molecular similarity has grown dramatically in recent years. The predominant users are pharmaceutical companies, employing similarity methods in a wide range of applications e.g. virtual screening, estimation of absorption, distribution, metabolism, excretion and toxicity (ADME/Tox) and prediction of physicochemical properties (solubility, partitioning etc.). In this perspective, we discuss the representation of molecular structure (descriptors), methods of comparing structures and how these relate to measured properties. This leads to the concept of molecular similarity, its various definitions and uses and how these have evolved in recent years. Here, we wish to evaluate and in some cases challenge accepted views and uses of molecular similarity. Molecular similarity, as a paradigm, contains many implicit and explicit assumptions in particular with respect to the prediction of the binding and efficacy of molecules at biological receptors. The fundamental observation is that molecular similarity has a context which both defines and limits its use. The key issues of solvation effects, heterogeneity of binding sites and the fundamental problem of the form of similarity measure to use are addressed.

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Year:  2004        PMID: 15534697     DOI: 10.1039/B409813G

Source DB:  PubMed          Journal:  Org Biomol Chem        ISSN: 1477-0520            Impact factor:   3.876


  134 in total

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2.  Diversity-oriented synthesis as a tool for the discovery of novel biologically active small molecules.

Authors:  Warren R J D Galloway; Albert Isidro-Llobet; David R Spring
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Review 3.  Evaluation of machine-learning methods for ligand-based virtual screening.

Authors:  Beining Chen; Robert F Harrison; George Papadatos; Peter Willett; David J Wood; Xiao Qing Lewell; Paulette Greenidge; Nikolaus Stiefl
Journal:  J Comput Aided Mol Des       Date:  2007-01-05       Impact factor: 3.686

Review 4.  Cheminformatics analysis and learning in a data pipelining environment.

Authors:  Moises Hassan; Robert D Brown; Shikha Varma-O'brien; David Rogers
Journal:  Mol Divers       Date:  2006-09-22       Impact factor: 2.943

Review 5.  Chemogenomic approaches to rational drug design.

Authors:  D Rognan
Journal:  Br J Pharmacol       Date:  2007-05-29       Impact factor: 8.739

6.  Inhibitors of SARS-3CLpro: virtual screening, biological evaluation, and molecular dynamics simulation studies.

Authors:  Prasenjit Mukherjee; Falgun Shah; Prashant Desai; Mitchell Avery
Journal:  J Chem Inf Model       Date:  2011-05-23       Impact factor: 4.956

7.  TargetHunter: an in silico target identification tool for predicting therapeutic potential of small organic molecules based on chemogenomic database.

Authors:  Lirong Wang; Chao Ma; Peter Wipf; Haibin Liu; Weiwei Su; Xiang-Qun Xie
Journal:  AAPS J       Date:  2013-01-05       Impact factor: 4.009

Review 8.  Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review.

Authors:  Peter Csermely; Tamás Korcsmáros; Huba J M Kiss; Gábor London; Ruth Nussinov
Journal:  Pharmacol Ther       Date:  2013-02-04       Impact factor: 12.310

9.  Alpha shapes applied to molecular shape characterization exhibit novel properties compared to established shape descriptors.

Authors:  J Anthony Wilson; Andreas Bender; Taner Kaya; Paul A Clemons
Journal:  J Chem Inf Model       Date:  2009-10       Impact factor: 4.956

10.  Application of 3D Zernike descriptors to shape-based ligand similarity searching.

Authors:  Vishwesh Venkatraman; Padmasini Ramji Chakravarthy; Daisuke Kihara
Journal:  J Cheminform       Date:  2009-12-17       Impact factor: 5.514

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