Literature DB >> 27827546

Applicability domain: towards a more formal definition.

T Hanser1, C Barber1, J F Marchaland1, S Werner1.   

Abstract

In recent years the applicability domain (AD) of a prediction system has become an important concern in (Q)SAR modelling, especially in the context of human safety assessment. Today AD is an active research topic, and many methods have been designed to estimate the adequacy of a model and the confidence in its outcome for a given prediction task. Unfortunately, the wide spectrum of techniques developed for this purpose is based on various definitions of the concept of AD, often taking into account different types of information. This variety of methodologies confuses the end users and makes the comparison of the AD for different models almost impossible. In this article, we demonstrate that AD is not a monolithic concept and can be broken down into three well-defined sub-domains assessing confidence at the model, prediction and decision levels, respectively. By leveraging this separation of concerns we have an opportunity to clarify, formalize and extend the definition of AD. We propose a framework that captures this new vision with the aim to initiate a global effort to converge towards a common AD definition within the (Q)SAR community.

Entities:  

Keywords:  Applicability domain; QSAR; TARDIS; confidence modelling; decision domain; machine learning

Mesh:

Year:  2016        PMID: 27827546     DOI: 10.1080/1062936X.2016.1250229

Source DB:  PubMed          Journal:  SAR QSAR Environ Res        ISSN: 1026-776X            Impact factor:   3.000


  15 in total

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4.  Role of simple descriptors and applicability domain in predicting change in protein thermostability.

Authors:  Kenneth N McGuinness; Weilan Pan; Robert P Sheridan; Grant Murphy; Alejandro Crespo
Journal:  PLoS One       Date:  2018-09-07       Impact factor: 3.240

5.  Extending in Silico Protein Target Prediction Models to Include Functional Effects.

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6.  Avoiding hERG-liability in drug design via synergetic combinations of different (Q)SAR methodologies and data sources: a case study in an industrial setting.

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Authors:  Andrea Morger; Miriam Mathea; Janosch H Achenbach; Antje Wolf; Roland Buesen; Klaus-Juergen Schleifer; Robert Landsiedel; Andrea Volkamer
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8.  Study of the Applicability Domain of the QSAR Classification Models by Means of the Rivality and Modelability Indexes.

Authors:  Irene Luque Ruiz; Miguel Ángel Gómez-Nieto
Journal:  Molecules       Date:  2018-10-24       Impact factor: 4.411

9.  Revealing cytotoxic substructures in molecules using deep learning.

Authors:  Henry E Webel; Talia B Kimber; Silke Radetzki; Martin Neuenschwander; Marc Nazaré; Andrea Volkamer
Journal:  J Comput Aided Mol Des       Date:  2020-04-16       Impact factor: 3.686

10.  Comprehensive Analysis of Applicability Domains of QSPR Models for Chemical Reactions.

Authors:  Assima Rakhimbekova; Timur I Madzhidov; Ramil I Nugmanov; Timur R Gimadiev; Igor I Baskin; Alexandre Varnek
Journal:  Int J Mol Sci       Date:  2020-08-03       Impact factor: 5.923

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