Best Practices for QSAR Model Development, Validation, and Exploitation

By Alexander Tropsha. 2010

1,776 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

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Citations in the corpus

Interpreting random forest classification models using a feature contribution method (2013, methodology): “Good practice of model development [17] involves: 1) data analysis 2) feature selection, 3) model building and 4) model evaluation.”
Deep reinforcement learning for de novo drug design (2017, methodology): “(42), including best practices formodel validation, as reported in several highly cited papers by our group (42, 43).”
A Review on Applications of Computational Methods in Drug Screening and Design (2020, background): “On the other hand, ligand-based drug design can also use quantitative structure–activity relationships (QSAR) [50,51] in which a correlation between calculated properties of molecules and their experimentally determined biological activity is derived, to predict the activity of new analogs.”
Tuning hERG out: Antitarget QSAR Models for Drug Development (2014, methodology): “Although these models appear to be well-fitted, a critical analysis reveals that the vast majority of the published QSAR models do not comply with the standard validation procedures and the different statistical criteria described in the best practices of QSAR modeling [80, 81].”
Enalos KNIME nodes: Exploring corrosion inhibition of steel in acidic medium (2013, methodology): “In particular, the following statistical indices have been proposed [23] to assess the predictive power of QSAR models, besides the popular squared correlation coefficient R(2)pred.”
QSAR-Based Virtual Screening: Advances and Applications in Drug Discovery (2018, background): “This is caused by the lack of understanding that QSAR is highly interdisciplinary and application ﬁeld as well as general ignorance of the best practices in the ﬁeld (Tropsha, 2010; Ban et al., 2017).”