Literature DB >> 24981618

In silico prediction of drug safety: despite progress there is abundant room for improvement.

William J Egan1, Gregor Zlokarnik2, Peter D J Grootenhuis2.   

Abstract

Predictive models for drug safety are crucial for helping to avoid costly late-stage failures. We review recent work on models for genotoxicity, liver toxicity, CYP450 inhibition and cardiotoxicity. These models have improved somewhat in recent years, and research has expanded into new frontiers, such as the prediction of liver toxicity. However, much more needs to be done.:
© 2004 Elsevier Ltd . All rights reserved.

Entities:  

Year:  2004        PMID: 24981618     DOI: 10.1016/j.ddtec.2004.11.002

Source DB:  PubMed          Journal:  Drug Discov Today Technol        ISSN: 1740-6749


  8 in total

1.  Trust, but verify: on the importance of chemical structure curation in cheminformatics and QSAR modeling research.

Authors:  Denis Fourches; Eugene Muratov; Alexander Tropsha
Journal:  J Chem Inf Model       Date:  2010-07-26       Impact factor: 4.956

Review 2.  In Silico Models for Hepatotoxicity.

Authors:  Claire Ellison; Mark Hewitt; Katarzyna Przybylak
Journal:  Methods Mol Biol       Date:  2022

3.  Cheminformatics analysis of assertions mined from literature that describe drug-induced liver injury in different species.

Authors:  Denis Fourches; Julie C Barnes; Nicola C Day; Paul Bradley; Jane Z Reed; Alexander Tropsha
Journal:  Chem Res Toxicol       Date:  2010-01       Impact factor: 3.739

4.  Autoimmune myocarditis, valvulitis, and cardiomyopathy.

Authors:  Jennifer M Myers; DeLisa Fairweather; Sally A Huber; Madeleine W Cunningham
Journal:  Curr Protoc Immunol       Date:  2013

5.  Use of structure-activity landscape index curves and curve integrals to evaluate the performance of multiple machine learning prediction models.

Authors:  Norman C Ledonne; Kevin Rissolo; James Bulgarelli; Leonard Tini
Journal:  J Cheminform       Date:  2011-02-07       Impact factor: 5.514

6.  Clustering drug-drug interaction networks with energy model layouts: community analysis and drug repurposing.

Authors:  Lucreţia Udrescu; Laura Sbârcea; Alexandru Topîrceanu; Alexandru Iovanovici; Ludovic Kurunczi; Paul Bogdan; Mihai Udrescu
Journal:  Sci Rep       Date:  2016-09-07       Impact factor: 4.379

7.  A New Structure-Activity Relationship (SAR) Model for Predicting Drug-Induced Liver Injury, Based on Statistical and Expert-Based Structural Alerts.

Authors:  Fabiola Pizzo; Anna Lombardo; Alberto Manganaro; Emilio Benfenati
Journal:  Front Pharmacol       Date:  2016-11-22       Impact factor: 5.810

8.  VenomPred: A Machine Learning Based Platform for Molecular Toxicity Predictions.

Authors:  Salvatore Galati; Miriana Di Stefano; Elisa Martinelli; Marco Macchia; Adriano Martinelli; Giulio Poli; Tiziano Tuccinardi
Journal:  Int J Mol Sci       Date:  2022-02-14       Impact factor: 5.923

  8 in total

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