Literature DB >> 22080614

DEGAS: sharing and tracking target compound ideas with external collaborators.

Man-Ling Lee1, Ignacio Aliagas, Jennafer Dotson, Jianwen A Feng, Alberto Gobbi, Timothy Heffron.   

Abstract

To minimize the risk of failure in clinical trials, drug discovery teams must propose active and selective clinical candidates with good physicochemical properties. An additional challenge is that today drug discovery is often conducted by teams at different geographical locations. To improve the collaborative decision making on which compounds to synthesize, we have implemented DEGAS, an application which enables scientists from Genentech and from collaborating external partners to instantly access the same data. DEGAS was implemented to ensure that only the best target compounds are made and that they are made without duplicate effort. Physicochemical properties and DMPK model predictions are computed for each compound to allow the team to make informed decisions when prioritizing. The synthesis progress can be easily tracked. While developing DEGAS, ease of use was a particular goal in order to minimize the difficulty of training and supporting remote users.

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Year:  2011        PMID: 22080614     DOI: 10.1021/ci2003297

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


  3 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.  A probabilistic method to report predictions from a human liver microsomes stability QSAR model: a practical tool for drug discovery.

Authors:  Ignacio Aliagas; Alberto Gobbi; Timothy Heffron; Man-Ling Lee; Daniel F Ortwine; Mark Zak; S Cyrus Khojasteh
Journal:  J Comput Aided Mol Des       Date:  2015-02-24       Impact factor: 3.686

3.  chemalot and chemalot_knime: Command line programs as workflow tools for drug discovery.

Authors:  Man-Ling Lee; Ignacio Aliagas; Jianwen A Feng; Thomas Gabriel; T J O'Donnell; Benjamin D Sellers; Bernd Wiswedel; Alberto Gobbi
Journal:  J Cheminform       Date:  2017-06-12       Impact factor: 5.514

  3 in total

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