Literature DB >> 15807484

Prospective exploration of synthetically feasible, medicinally relevant chemical space.

Stephan C Schürer1, Prashant Tyagi, Steven M Muskal.   

Abstract

We describe a novel approach to direct the exploration of chemical space in an effort to balance synthetic accessibility and medicinal relevancy prior to experimental work. Reaction transforms containing empirical reactivity and compatibility information are dynamically assembled into reaction sequences (vProtocols) utilizing commercially available starting material feedstock. These vProtocols are evolved and optimized by a genetic algorithm, which leverages fitness functions based on predicted properties of generated molecular products. We present the underlying concepts, methodology and initial results of this prospective approach.

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Year:  2005        PMID: 15807484     DOI: 10.1021/ci0496853

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


  4 in total

1.  Prediction-driven matched molecular pairs to interpret QSARs and aid the molecular optimization process.

Authors:  Yurii Sushko; Sergii Novotarskyi; Robert Körner; Joachim Vogt; Ahmed Abdelaziz; Igor V Tetko
Journal:  J Cheminform       Date:  2014-12-11       Impact factor: 5.514

2.  Neural Networks for the Prediction of Organic Chemistry Reactions.

Authors:  Jennifer N Wei; David Duvenaud; Alán Aspuru-Guzik
Journal:  ACS Cent Sci       Date:  2016-10-14       Impact factor: 14.553

3.  AutoGrow4: an open-source genetic algorithm for de novo drug design and lead optimization.

Authors:  Jacob O Spiegel; Jacob D Durrant
Journal:  J Cheminform       Date:  2020-04-17       Impact factor: 5.514

4.  SAVI, in silico generation of billions of easily synthesizable compounds through expert-system type rules.

Authors:  Hitesh Patel; Wolf-Dietrich Ihlenfeldt; Philip N Judson; Yurii S Moroz; Yuri Pevzner; Megan L Peach; Victorien Delannée; Nadya I Tarasova; Marc C Nicklaus
Journal:  Sci Data       Date:  2020-11-11       Impact factor: 6.444

  4 in total

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