Literature DB >> 35859884

Contemporary Computational Applications and Tools in Drug Discovery.

Philip B Cox1, Rishi Gupta1.   

Abstract

In the past decade or so there has been a dramatic increase in the number of computational applications and tools that have been developed to enable medicinal chemists to prosecute modern drug discovery programs more efficiently. The upsurge of user-friendly, well-designed computational tools that enable structure-based drug design (SBDD) and cheminformatics (CI)-based drug design has equipped the medicinal chemist with an arsenal of tools and applications that significantly augments the entire design process, thereby enhancing the speed and efficiency of the design-make-test-analyze cycle. Modern computational applications and tools transcend all areas of drug discovery, and most savvy medicinal chemists can employ them effectively in a myriad of drug discovery applications. Indeed, the sheer scope and breadth of tools available to the medicinal chemist is vast and, to our knowledge, has not been comprehensively reviewed. In this article we have catalogued many computational tools, platforms, and applications that are currently available, with four main areas highlighted: commercially available tools/platforms, open-source applications, internally developed platforms (software tools developed within a pharma or biotech organization), and artificial intelligence/machine learning-based platforms. For ease of interpretation, for these categories we provide tables organized by vendor or organization name, the name of the application, whether the tool/application is employed predominantly for SBDD or CI-based design, and a summary of the main function of the tools, with associated hyperlinks to vendor Web sites. We have tried to be as comprehensive and as inclusive as possible; however, the pace of development of new and existing tools is so rapid that there may be omissions with respect to newly developed tools and current versions of the software.
© 2022 American Chemical Society.

Entities:  

Year:  2022        PMID: 35859884      PMCID: PMC9290028          DOI: 10.1021/acsmedchemlett.1c00662

Source DB:  PubMed          Journal:  ACS Med Chem Lett        ISSN: 1948-5875            Impact factor:   4.632


  53 in total

1.  New benchmark for chemical nomenclature software.

Authors:  Edward O Cannon
Journal:  J Chem Inf Model       Date:  2012-04-18       Impact factor: 4.956

2.  Identifying and characterizing binding sites and assessing druggability.

Authors:  Thomas A Halgren
Journal:  J Chem Inf Model       Date:  2009-02       Impact factor: 4.956

3.  Similarity searching and scaffold hopping in synthetically accessible combinatorial chemistry spaces.

Authors:  Markus Boehm; Tong-Ying Wu; Holger Claussen; Christian Lemmen
Journal:  J Med Chem       Date:  2008-04-02       Impact factor: 7.446

4.  Novel method for generating structure-based pharmacophores using energetic analysis.

Authors:  Noeris K Salam; Roberto Nuti; Woody Sherman
Journal:  J Chem Inf Model       Date:  2009-10       Impact factor: 4.956

5.  RigFit: a new approach to superimposing ligand molecules.

Authors:  C Lemmen; C Hiller; T Lengauer
Journal:  J Comput Aided Mol Des       Date:  1998-09       Impact factor: 3.686

6.  A CADD-alog of strategies in pharma.

Authors:  Wendy A Warr
Journal:  J Comput Aided Mol Des       Date:  2017-03-18       Impact factor: 3.686

7.  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

8.  Towards an integrated description of hydrogen bonding and dehydration: decreasing false positives in virtual screening with the HYDE scoring function.

Authors:  Ingo Reulecke; Gudrun Lange; Jürgen Albrecht; Robert Klein; Matthias Rarey
Journal:  ChemMedChem       Date:  2008-06       Impact factor: 3.466

9.  Open Drug Discovery Toolkit (ODDT): a new open-source player in the drug discovery field.

Authors:  Maciej Wójcikowski; Piotr Zielenkiewicz; Pawel Siedlecki
Journal:  J Cheminform       Date:  2015-06-22       Impact factor: 5.514

10.  A Bayesian machine learning approach for drug target identification using diverse data types.

Authors:  Neel S Madhukar; Prashant K Khade; Linda Huang; Kaitlyn Gayvert; Giuseppe Galletti; Martin Stogniew; Joshua E Allen; Paraskevi Giannakakou; Olivier Elemento
Journal:  Nat Commun       Date:  2019-11-19       Impact factor: 14.919

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