Literature DB >> 23402361

Physicochemical and DMPK in silico models: facilitating their use by medicinal chemists.

Daniel F Ortwine1, Ignacio Aliagas.   

Abstract

It is known that the developability of drugs is related to their physicochemical and DMPK properties. Given the time and expense involved in discovering and developing new drugs, maximizing the chance of success by calculating properties ahead of chemical synthesis and testing, and only acting on those candidates whose properties fall into a desired range, would seem to make sense. This paper provides an overview of calculable physicochemical and DMPK properties, an assessment of their relative difficulty of their calculation and accuracy, and available software. Methods companies have employed to communicate results will be discussed, including the use of composite scoring functions and ranking schemes. Calculations do no good if chemists will not use them to prioritize synthesis decisions. Strategies are presented for facilitating model usage. An approach adopted at Genentech for presenting results that involves the close coupling of property calculations with 3D structure based drug design is described.

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Year:  2013        PMID: 23402361     DOI: 10.1021/mp3006193

Source DB:  PubMed          Journal:  Mol Pharm        ISSN: 1543-8384            Impact factor:   4.939


  4 in total

1.  An integrated suite of modeling tools that empower scientists in structure- and property-based drug design.

Authors:  Jianwen A Feng; Ignacio Aliagas; Philippe Bergeron; Jeff M Blaney; Erin K Bradley; Michael F T Koehler; Man-Ling Lee; Daniel F Ortwine; Vickie Tsui; Johnny Wu; Alberto Gobbi
Journal:  J Comput Aided Mol Des       Date:  2015-04-29       Impact factor: 3.686

2.  Enabling drug discovery project decisions with integrated computational chemistry and informatics.

Authors:  Vickie Tsui; Daniel F Ortwine; Jeffrey M Blaney
Journal:  J Comput Aided Mol Des       Date:  2016-10-31       Impact factor: 3.686

3.  Lipophilicity prediction of peptides and peptide derivatives by consensus machine learning.

Authors:  Jens-Alexander Fuchs; Francesca Grisoni; Michael Kossenjans; Jan A Hiss; Gisbert Schneider
Journal:  Medchemcomm       Date:  2018-08-22       Impact factor: 3.597

Review 4.  Molecular determinants of blood-brain barrier permeation.

Authors:  Werner J Geldenhuys; Afroz S Mohammad; Chris E Adkins; Paul R Lockman
Journal:  Ther Deliv       Date:  2015-08-25
  4 in total

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