Literature DB >> 18600573

Exploiting QSAR models in lead optimization.

Peter Gedeck1, Richard A Lewis.   

Abstract

QSAR models can play a vital role in both the opening phase and the endgame of lead optimization. In the opening phase, there is often a large quantity of data from high-throughput screening (HTS), and potential leads need to be selected from several distinct chemotypes. In the endgame, the throughput of the final, critical ADMET and pharmacokinetic assays is often not sufficient to allow full experimental characterization of all the structures in the available time. A considerable amount of the current research toward new QSAR models is based on the modeling of the general ADMET phenomena, with the aim of constructing globally applicable models. The process to construct QSAR models is relatively straightforward; however, it is also simple to build misleading, or even incorrect, models. This review considers the key developments in the field of QSAR modeling: how QSAR models are constructed, how they can be validated, their reliability and their applicability. If applied carefully and appropriately, the QSAR technique has a valuable role to play during lead optimization.

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Mesh:

Year:  2008        PMID: 18600573

Source DB:  PubMed          Journal:  Curr Opin Drug Discov Devel        ISSN: 1367-6733


  8 in total

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2.  Ligand and structure-based models for the prediction of ligand-receptor affinities and virtual screenings: Development and application to the beta(2)-adrenergic receptor.

Authors:  Santiago Vilar; Joel Karpiak; Stefano Costanzi
Journal:  J Comput Chem       Date:  2010-03       Impact factor: 3.376

3.  Towards interoperable and reproducible QSAR analyses: Exchange of datasets.

Authors:  Ola Spjuth; Egon L Willighagen; Rajarshi Guha; Martin Eklund; Jarl Es Wikberg
Journal:  J Cheminform       Date:  2010-06-30       Impact factor: 5.514

4.  Computational ligand-based rational design: Role of conformational sampling and force fields in model development.

Authors:  Jihyun Shim; Alexander D Mackerell
Journal:  Medchemcomm       Date:  2011-05       Impact factor: 3.597

5.  Predicting cancer-relevant proteins using an improved molecular similarity ensemble approach.

Authors:  Bin Zhou; Qi Sun; De-Xin Kong
Journal:  Oncotarget       Date:  2016-05-31

6.  Detection of drug-drug interactions by modeling interaction profile fingerprints.

Authors:  Santiago Vilar; Eugenio Uriarte; Lourdes Santana; Nicholas P Tatonetti; Carol Friedman
Journal:  PLoS One       Date:  2013-03-08       Impact factor: 3.240

7.  Cloud infrastructures for in silico drug discovery: economic and practical aspects.

Authors:  Daniele D'Agostino; Andrea Clematis; Alfonso Quarati; Daniele Cesini; Federica Chiappori; Luciano Milanesi; Ivan Merelli
Journal:  Biomed Res Int       Date:  2013-09-10       Impact factor: 3.411

8.  Towards agile large-scale predictive modelling in drug discovery with flow-based programming design principles.

Authors:  Samuel Lampa; Jonathan Alvarsson; Ola Spjuth
Journal:  J Cheminform       Date:  2016-11-24       Impact factor: 5.514

  8 in total

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