Literature DB >> 29425037

Assessment and Reproducibility of Quantitative Structure-Activity Relationship Models by the Nonexpert.

Mukesh Patel1, Martyn L Chilton1, Andrea Sartini1, Laura Gibson1, Chris Barber1, Liz Covey-Crump1, Katarzyna R Przybylak2, Mark T D Cronin2, Judith C Madden2.   

Abstract

Model reliability is generally assessed and reported as an intrinsic component of quantitative structure-activity relationship (QSAR) publications; it can be evaluated using defined quality criteria such as the Organisation for Economic Cooperation and Development (OECD) principles for the validation of QSARs. However, less emphasis is afforded to the assessment of model reproducibility, particularly by users who may wish to use model outcomes for decision making, but who are not QSAR experts. In this study we identified a range of QSARs in the area of absorption, distribution, metabolism, and elimination (ADME) prediction and assessed their adherence to the OECD principles, as well as investigating their reproducibility by scientists without expertise in QSAR. Here, 85 papers were reviewed, reporting over 80 models for 31 ADME-related endpoints. Of these, 12 models were identified that fulfilled at least 4 of the 5 OECD principles and 3 of these 12 could be readily reproduced. Published QSAR models should aim to meet a standard level of quality and be clearly communicated, ensuring their reproducibility, to progress the uptake of the models in both research and regulatory landscapes. A pragmatic workflow for implementing published QSAR models and recommendations to modellers, for publishing models with greater usability, are presented herein.

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Year:  2018        PMID: 29425037     DOI: 10.1021/acs.jcim.7b00523

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


  7 in total

Review 1.  IVIVE: Facilitating the Use of In Vitro Toxicity Data in Risk Assessment and Decision Making.

Authors:  Xiaoqing Chang; Yu-Mei Tan; David G Allen; Shannon Bell; Paul C Brown; Lauren Browning; Patricia Ceger; Jeffery Gearhart; Pertti J Hakkinen; Shruti V Kabadi; Nicole C Kleinstreuer; Annie Lumen; Joanna Matheson; Alicia Paini; Heather A Pangburn; Elijah J Petersen; Emily N Reinke; Alexandre J S Ribeiro; Nisha Sipes; Lisa M Sweeney; John F Wambaugh; Ronald Wange; Barbara A Wetmore; Moiz Mumtaz
Journal:  Toxics       Date:  2022-05-01

2.  Large-Scale Modeling of Multispecies Acute Toxicity End Points Using Consensus of Multitask Deep Learning Methods.

Authors:  Sankalp Jain; Vishal B Siramshetty; Vinicius M Alves; Eugene N Muratov; Nicole Kleinstreuer; Alexander Tropsha; Marc C Nicklaus; Anton Simeonov; Alexey V Zakharov
Journal:  J Chem Inf Model       Date:  2021-02-03       Impact factor: 4.956

Review 3.  Towards reproducible computational drug discovery.

Authors:  Nalini Schaduangrat; Samuel Lampa; Saw Simeon; Matthew Paul Gleeson; Ola Spjuth; Chanin Nantasenamat
Journal:  J Cheminform       Date:  2020-01-28       Impact factor: 5.514

4.  Flame: an open source framework for model development, hosting, and usage in production environments.

Authors:  Manuel Pastor; José Carlos Gómez-Tamayo; Ferran Sanz
Journal:  J Cheminform       Date:  2021-04-19       Impact factor: 5.514

5.  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

Review 6.  QSPR/QSAR: State-of-Art, Weirdness, the Future.

Authors:  Andrey A Toropov; Alla P Toropova
Journal:  Molecules       Date:  2020-03-12       Impact factor: 4.411

7.  MAIP: a web service for predicting blood-stage malaria inhibitors.

Authors:  Nicolas Bosc; Eloy Felix; Ricardo Arcila; David Mendez; Martin R Saunders; Darren V S Green; Jason Ochoada; Anang A Shelat; Eric J Martin; Preeti Iyer; Ola Engkvist; Andreas Verras; James Duffy; Jeremy Burrows; J Mark F Gardner; Andrew R Leach
Journal:  J Cheminform       Date:  2021-02-22       Impact factor: 5.514

  7 in total

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