Structure-based virtual screening of perfluoroalkyl and polyfluoroalkyl substances (PFASs) as endocrine disruptors of androgen receptor activity using molecular docking and machine learning

Singam, Ettayapuram Ramaprasad Azhagiya; Tachachartvanich, Phum; Fourches, Denis; Soshilov, Anatoly A.; Hsieh, Jennifer C.Y.; Merrill, Michele A. La; Smith, Martyn T.; Durkin, Kathleen A.

doi:10.1016/j.envres.2020.109920

Cited by 24 publications

(17 citation statements)

References 58 publications

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“…Therefore, a huge amount of data are missing for each bioassay, which can lead to biased decisions regarding the potential toxicity of chemicals . To broaden the application of big data in toxicology, scholars have introduced ML and DL as promising tools for predicting the chemical toxicity of new environmental chemicals that lack toxicity information. , …”

Section: Introductionmentioning

confidence: 99%

Effects of Class Imbalance and Data Scarcity on the Performance of Binary Classification Machine Learning Models Developed Based on ToxCast/Tox21 Assay Data

Kim

Jeong

Choi

2022

Chem. Res. Toxicol.

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The development of toxicity classification models using the ToxCast database has been extensively studied. Machine learning approaches are effective in identifying the bioactivity of untested chemicals. However, ToxCast assays differ in the amount of data and degree of class imbalance (CI). Therefore, the resampling algorithm employed should vary depending on the data distribution to achieve optimal classification performance. In this study, the effects of CI and data scarcity (DS) on the performance of binary classification models were investigated using ToxCast bioassay data. An assay matrix based on CI and DS was prepared for 335 assays with biologically intended target information, and 28 CI assays and 3 DS assays were selected. Thirty models established by combining five molecular fingerprints (i.e., Morgan, MACCS, RDKit, Pattern, and Layered) and six algorithms [i.e., gradient boosting tree, random forest (RF), multi-layered perceptron, k-nearest neighbor, logistic regression, and naive Bayes] were trained using the selected assay data set. Of the 30 trained models, MACCS–RF showed the best performance and thus was selected for analyses of the effects of CI and DS. Results showed that recall and F1 were significantly lower when training with the CI assays than with the DS assays. In addition, hyperparameter tuning of the RF algorithm significantly improved F1 on CI assays. This study provided a basis for developing a toxicity classification model with improved performance by evaluating the effects of data set characteristics. This study also emphasized the importance of using appropriate evaluation metrics and tuning hyperparameters in model development.

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Section: Introductionmentioning

confidence: 99%

Effects of Class Imbalance and Data Scarcity on the Performance of Binary Classification Machine Learning Models Developed Based on ToxCast/Tox21 Assay Data

Kim

Jeong

Choi

2022

Chem. Res. Toxicol.

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“…It has been most widely used to solve binary classification problems. , SVM can also be used to predict toxicity end points, such as estrogen receptor activity, hepatocyte toxicity, and inflammation. ,, kNN is a nonparametric method that classifies targets to be predicted based on the nearest training data in a feature space . kNN has been used to predict acute toxicity (LD50), chronic toxicity, genotoxicity, carcinogenicity, neurotoxicity, and androgen receptor activity. ,− NB is a simple probabilistic classifier based on Bayesian rules and is used for conditional probabilities . It has been used to predict reproductive toxicity, aquatic toxicity, developmental toxicity, and mitochondrial toxicity. ,,, Finally, a decision tree (DT) analyzes data to create patterns as a combination of predictable rules .…”

Section: Model Algorithmsmentioning

confidence: 99%

Artificial Intelligence-Based Toxicity Prediction of Environmental Chemicals: Future Directions for Chemical Management Applications

Jeong

Choi

2022

Environ. Sci. Technol.

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Recently, research on the development of artificial intelligence (AI)-based computational toxicology models that predict toxicity without the use of animal testing has emerged because of the rapid development of computer technology. Various computational toxicology techniques that predict toxicity based on the structure of chemical substances are gaining attention, including the quantitative structure–activity relationship. To understand the recent development of these models, we analyzed the databases, molecular descriptors, fingerprints, and algorithms considered in recent studies. Based on a selection of 96 papers published since 2014, we found that AI models have been developed to predict approximately 30 different toxicity end points using more than 20 toxicity databases. For model development, molecular access system and extended-connectivity fingerprints are the most commonly used molecular descriptors. The most used algorithm among the machine learning techniques is the random forest, while the most used algorithm among the deep learning techniques is a deep neural network. The use of AI technology in the development of toxicity prediction models is a new concept that will aid in achieving a scientific accord and meet regulatory applications. The comprehensive overview provided in this study will provide a useful guide for the further development and application of toxicity prediction models.

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“…Accordingly, PFAS exposure often results in altered androgen and insulin-like factor 3 (INSL3) dependent processes ( 172 – 175 ) ( Figure 3 ). There are two suggested mechanisms by which PFAS produce harmful endocrine effects: either by disturbing steroidogenesis ( 6 ) or by interfering with steroid hormone receptors ( 176 , 177 ).…”

Section: Known Effects Of Pfas Exposure On Male Fertilitymentioning

confidence: 99%

Assessment of the Emerging Threat Posed by Perfluoroalkyl and Polyfluoroalkyl Substances to Male Reproduction in Humans

et al. 2022

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Per-fluoroalkyl and polyfluoroalkyl substances (PFAS) are a diverse group of synthetic fluorinated chemicals used widely in industry and consumer products. Due to their extensive use and chemical stability, PFAS are ubiquitous environmental contaminants and as such, form an emerging risk factor for male reproductive health. The long half-lives of PFAS is of particular concern as the propensity to accumulate in biological systems prolong the time taken for excretion, taking years in many cases. Accordingly, there is mounting evidence supporting a negative association between PFAS exposure and an array of human health conditions. However, inconsistencies among epidemiological and experimental findings have hindered the ability to definitively link negative reproductive outcomes to specific PFAS exposure. This situation highlights the requirement for further investigation and the identification of reliable biological models that can inform health risks, allowing sensitive assessment of the spectrum of effects of PFAS exposure on humans. Here, we review the literature on the biological effects of PFAS exposure, with a specific focus on male reproduction, owing to its utility as a sentinel marker of general health. Indeed, male infertility has increasingly been shown to serve as an early indicator of a range of co-morbidities such as coronary, inflammatory, and metabolic diseases. It follows that adverse associations have been established between PFAS exposure and the incidence of testicular dysfunction, including pathologies such as testicular cancer and a reduction in semen quality. We also give consideration to the mechanisms that render the male reproductive tract vulnerable to PFAS mediated damage, and discuss novel remediation strategies to mitigate the negative impact of PFAS contamination and/or to ameliorate the PFAS load of exposed individuals.

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Structure-based virtual screening of perfluoroalkyl and polyfluoroalkyl substances (PFASs) as endocrine disruptors of androgen receptor activity using molecular docking and machine learning

Cited by 24 publications

References 58 publications

Effects of Class Imbalance and Data Scarcity on the Performance of Binary Classification Machine Learning Models Developed Based on ToxCast/Tox21 Assay Data

Effects of Class Imbalance and Data Scarcity on the Performance of Binary Classification Machine Learning Models Developed Based on ToxCast/Tox21 Assay Data

Artificial Intelligence-Based Toxicity Prediction of Environmental Chemicals: Future Directions for Chemical Management Applications

Assessment of the Emerging Threat Posed by Perfluoroalkyl and Polyfluoroalkyl Substances to Male Reproduction in Humans

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