Literature DB >> 23557664

Matched molecular pair analysis in drug discovery.

Alexander G Dossetter1, Edward J Griffen, Andrew G Leach.   

Abstract

Multiple parameter optimisation in drug discovery is difficult, but Matched Molecular Pair Analysis (MMPA) can help. Computer algorithms can process data in an unbiased way to yield design rules and suggest better molecules, cutting the number of design cycles. The approach often makes more suggestions than can be processed manually and methods to deal with this are proposed. However, there is a paucity of contextually specific design rules, which would truly make the technique powerful. By combining extracted information from multiple sources there is an opportunity to solve this problem and advance medicinal chemistry in a matter of months rather than years.
Copyright © 2013 Elsevier Ltd. All rights reserved.

Mesh:

Year:  2013        PMID: 23557664     DOI: 10.1016/j.drudis.2013.03.003

Source DB:  PubMed          Journal:  Drug Discov Today        ISSN: 1359-6446            Impact factor:   7.851


  24 in total

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5.  Blowing a breath of fresh share on data.

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6.  LogD Contributions of Substituents Commonly Used in Medicinal Chemistry.

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8.  Controlled Molecule Generator for Optimizing Multiple Chemical Properties.

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9.  Structure-based predictions of activity cliffs.

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Journal:  J Chem Inf Model       Date:  2015-05-11       Impact factor: 4.956

10.  Rapid scanning structure-activity relationships in combinatorial data sets: identification of activity switches.

Authors:  José L Medina-Franco; Bruce S Edwards; Clemencia Pinilla; Jon R Appel; Marc A Giulianotti; Radleigh G Santos; Austin B Yongye; Larry A Sklar; Richard A Houghten
Journal:  J Chem Inf Model       Date:  2013-06-07       Impact factor: 4.956

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