A strategy on the definition of applicability domain of model based on population analysis

Yun, Yong‐Huan; Wu, Dongming; Li, Guangyi; Zhang, Qiaoyan; Xia, Yang; Li, Qinfen; Cao, Dong‐Sheng; Xu, Qingsong

doi:10.1016/j.chemolab.2017.09.007

Cited by 12 publications

(8 citation statements)

References 38 publications

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“…Applicability domain (AD) is a crucial part of the QSAR methodology that, if used correctly, may significantly improve the prediction results. , There are multiple ways to calculate the applicability of a QSAR model. − In this study, we used two different approaches for estimation of the model’s AD. In the first approach, we estimated the Tanimoto similarity , between the test set compounds and nearest neighbor in the training set using Morgan fingerprints.…”

Section: Methodsmentioning

confidence: 99%

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

Jain

Siramshetty

Alves

et al. 2021

J. Chem. Inf. Model.

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Computational methods to predict molecular properties regarding safety and toxicology represent alternative approaches to expedite drug development, screen environmental chemicals, and thus significantly reduce associated time and costs. There is a strong need and interest in the development of computational methods that yield reliable predictions of toxicity, and many approaches, including the recently introduced deep neural networks, have been leveraged towards this goal. Herein, we report on the collection, curation, and integration of data from the public data sets that were the source of the ChemIDplus database for systemic acute toxicity. These efforts generated the largest publicly available such data set comprising > 80,000 compounds measured against a total of 59 acute systemic toxicity end points. This data was used for developing multiple singleand multitask models utilizing random forest, deep neural networks, convolutional, and graph convolutional neural network approaches. For the first time, we also reported the consensus models based on different multitask approaches. To the best of our knowledge, prediction models for 36 of the 59 end points have never been published before. Furthermore, our results demonstrated a significantly better performance of the consensus model obtained from three multitask learning approaches that particularly predicted the 29 smaller tasks (less than 300 compounds) better than other models developed in the study. The curated data set and the developed models have been made publicly available at https://github.com/ncats/ld50-multitask, https://predictor.ncats.io/, and https://cactus.nci.nih.gov/download/acute-toxicity-db (data set only) to support regulatory and research applications.

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Section: Methodsmentioning

confidence: 99%

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

Jain

Siramshetty

Alves

et al. 2021

J. Chem. Inf. Model.

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“…The rivality index is a DM measurement of the predictability of a molecule combining the CLASS-LAG [ 23 ] and population analysis methods [ 20 ] in a simpler definition of AD and in a fastest calculation. Thus, the calculation of RI does not require the building of a model, the values of AD contribute with an absolute measurement of the capability to a molecule to be correctly predicted by a model and, in addition, these RI values inform of the molecules inside, outside and borders of the AD.…”

Section: Methodsmentioning

confidence: 99%

“…Yun et al [ 20 ] have proposed to define the AD by means of population analysis (PA) strategy, including model population analysis (MPA) and approach population analysis (APA). MPA employed bounding box, Mahalannobis distance and K-nearest neighbors methods to define AD with a large amount of sub-datasets derived from training set.…”

Section: Introductionmentioning

confidence: 99%

Study of the Applicability Domain of the QSAR Classification Models by Means of the Rivality and Modelability Indexes

Ruiz

Gómez‐Nieto

2018

Molecules

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The reliability of a QSAR classification model depends on its capacity to achieve confident predictions of new compounds not considered in the building of the model. The results of this external validation process show the applicability domain (AD) of the QSAR model and, therefore, the robustness of the model to predict the property/activity of new molecules. In this paper we propose the use of the rivality and modelability indexes for the study of the characteristics of the datasets to be correctly modeled by a QSAR algorithm and to predict the reliability of the built model to prognosticate the property/activity of new molecules. The calculation of these indexes has a very low computational cost, not requiring the building of a model, thus being good tools for the analysis of the datasets in the first stages of the building of QSAR classification models. In our study, we have selected two benchmark datasets with similar number of molecules but with very different modelability and we have corroborated the capacity of the predictability of the rivality and modelability indexes regarding the classification models built using Support Vector Machine and Random Forest algorithms with 5-fold cross-validation and leave-one-out techniques. The results have shown the excellent ability of both indexes to predict outliers and the applicability domain of the QSAR classification models. In all cases, these values accurately predicted the statistic parameters of the QSAR models generated by the algorithms.

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“…to calculate the applicability of a QSAR model (Patel et al, 2018;Sahigara et al, 2013;Sushko et al, 2010;Yun et al, 2017). In this study, for estimation of the model's AD, the Tanimoto similarity was assessed (Bajusz et al, 2015;TT, 1958) between test set compounds and its nearest neighbor in the training set, using Morgan fingerprints.…”

Section: Applicability Domain Assessmentmentioning

confidence: 99%

Development of Robust Quantitative Structure-Activity Relationship Models for CYP2C9, CYP2D6, and CYP3A4 Catalysis and Inhibition

et al. 2021

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Cytochrome P450 (CYP) enzymes are responsible for the metabolism of >75% of marketed drugs, making it essential to identify the contributions of individual CYPs to the total clearance of a new candidate drug. Overreliance on one CYP for clearance levies a high risk of drug-drug interactions; and considering that several human CYP enzymes are polymorphic, it can also lead to highly variable pharmacokinetics in the clinic. Thus, it would be advantageous to understand the likelihood of new chemical entities to interact with the major CYP enzymes at an early stage in the drug discovery process. Typical screening assays using human liver microsomes do not provide sufficient information to distinguish the specific CYPs responsible for clearance. In this regard, we experimentally assessed the metabolic stability of ~5,000 compounds for the three most prominent xenobiotic metabolizing human CYPs, i.e., CYP2C9, CYP2D6 and CYP3A4, and used the datasets to develop quantitative structure-activity relationship models for the prediction of high clearance substrates for these enzymes. Screening library included the NCATS Pharmaceutical Collection, comprised of clinically approved low molecular weight compounds, and an annotated library consisting of drug-like compounds. To identify inhibitors, the library was screened against a luminescence-based CYP inhibition assay; and through cross-referencing hits from the two assays, we were able to distinguish substrates and inhibitors of these enzymes. The best substrate and inhibitor models (Balanced accuracies ~0.7), as well as the data used to develop these models, have been made publicly available (https://opendata.ncats.nih.gov/adme) to advance drug discovery across all research groups.

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A strategy on the definition of applicability domain of model based on population analysis

Cited by 12 publications

References 38 publications

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

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

Study of the Applicability Domain of the QSAR Classification Models by Means of the Rivality and Modelability Indexes

Development of Robust Quantitative Structure-Activity Relationship Models for CYP2C9, CYP2D6, and CYP3A4 Catalysis and Inhibition

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