Literature DB >> 12470280

Predicting drug-likeness: why and how?

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Abstract

There exists a huge attrition rate of molecules in clinical trials. It was expected that high-throughput screening and combinatorial chemistry would make the task of producing drugs easier. However, the efforts of the past decade have not been an unvarnished success. As a result, a lot of experimental and computational efforts are currently being directed at determining the basic requirements for a molecule to become a drug. Here we will review the physiological, structural, and other requirements for obtaining a molecule that will be successful in the clinic. Following this we will provide a description, analysis, and commentary on the computational efforts in this direction. We will focus both on the traditional computational chemistry perspective of starting from the structure of the molecule as well as the traditional computational pharmaceutical scientist's perspective of physiologically based simulations. We end with a few comments about the future and some ideas on re-organizing the pharmaceutical enterprise.

Mesh:

Year:  2002        PMID: 12470280     DOI: 10.2174/1568026023392968

Source DB:  PubMed          Journal:  Curr Top Med Chem        ISSN: 1568-0266            Impact factor:   3.295


  3 in total

1.  Impact of descriptor vector scaling on the classification of drugs and nondrugs with artificial neural networks.

Authors:  Alireza Givehchi; Gisbert Schneider
Journal:  J Mol Model       Date:  2004-04-06       Impact factor: 1.810

2.  Combinatorial library-based design with Basis Products.

Authors:  Joe Zhongxiang Zhou; Shenghua Shi; Jim Na; Zhengwei Peng; Tom Thacher
Journal:  J Comput Aided Mol Des       Date:  2009-07-11       Impact factor: 3.686

3.  Natural Compounds and Their Analogues as Potent Antidotes against the Most Poisonous Bacterial Toxin.

Authors:  Kruti B Patel; Shuowei Cai; Michael Adler; Brajendra K Singh; Virinder S Parmar; Bal Ram Singh
Journal:  Appl Environ Microbiol       Date:  2018-11-30       Impact factor: 4.792

  3 in total

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