Literature DB >> 17897035

Designing drugs on the internet? Free web tools and services supporting medicinal chemistry.

Peter Ertl1, Stephen Jelfs.   

Abstract

The drug discovery process is supported by a multitude of freely available tools on the Internet. This paper summarizes some of the databases and tools that are of particular interest to medicinal chemistry. These include numerous data collections that provide access to valuable chemical data resources, allowing complex queries of compound structures, associated physicochemical properties and biological activities to be performed and, in many cases, providing links to commercial chemical suppliers. Further applications are available for searching protein-ligand complexes and identifying important binding interactions that occur. This is particularly useful for understanding the molecular recognition of ligands in the lead optimization process. The Internet also provides access to databases detailing metabolic pathways and transformations which can provide insight into disease mechanism, identify new targets entities or the potential off-target effects of a drug candidate. Furthermore, sophisticated online cheminformatics tools are available for processing chemical structures, predicting properties, and generating 2D or 3D structure representations--often required prior to more advanced analyses. The Internet provides a wealth of valuable resources that, if fully exploited, can greatly benefit the drug discovery community. In this paper, we provide an overview of some of the more important of these and, in particular, the freely accessible resources that are currently available.

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Year:  2007        PMID: 17897035     DOI: 10.2174/156802607782194707

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


  4 in total

1.  Molecular structure input on the web.

Authors:  Peter Ertl
Journal:  J Cheminform       Date:  2010-02-02       Impact factor: 5.514

2.  Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions.

Authors:  Peter Ertl; Ansgar Schuffenhauer
Journal:  J Cheminform       Date:  2009-06-10       Impact factor: 5.514

3.  Quantitative assessment of the expanding complementarity between public and commercial databases of bioactive compounds.

Authors:  Christopher Southan; Péter Várkonyi; Sorel Muresan
Journal:  J Cheminform       Date:  2009-07-06       Impact factor: 5.514

4.  A deep learning approach for the blind logP prediction in SAMPL6 challenge.

Authors:  Samarjeet Prasad; Bernard R Brooks
Journal:  J Comput Aided Mol Des       Date:  2020-01-30       Impact factor: 3.686

  4 in total

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