Predicting Potential Endocrine Disrupting Chemicals Binding to Estrogen Receptor α (ERα) Using a Pipeline Combining Structure-Based and Ligand-Based in Silico Methods

Sellami, Asma; Montès, Matthieu; Lagarde, Nathalie

doi:10.3390/ijms22062846

Cited by 6 publications

(3 citation statements)

References 75 publications

(74 reference statements)

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“…Several splits were done, and models were built an evaluated with the associated train/test output. The split with the best results was kept for the following model optimization ( 29 , 163 ).…”

Section: Studied Methods and Associated Datamentioning

confidence: 99%

“…A solution to overcome each method limitations and take advantage of their complementarity is to use a combination of methods ( 28 , 165 , 166 ). Computational methods can be combined in integrative approaches using hierarchical or consensus screening ( 27 – 29 , 48 , 60 , 100 , 163 ) (for further references c.f. Table 1 – 4 ).…”

Section: Studied Methods and Associated Datamentioning

confidence: 99%

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Review of in silico studies dedicated to the nuclear receptor family: Therapeutic prospects and toxicological concerns

et al. 2022

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Being in the center of both therapeutic and toxicological concerns, NRs are widely studied for drug discovery application but also to unravel the potential toxicity of environmental compounds such as pesticides, cosmetics or additives. High throughput screening campaigns (HTS) are largely used to detect compounds able to interact with this protein family for both therapeutic and toxicological purposes. These methods lead to a large amount of data requiring the use of computational approaches for a robust and correct analysis and interpretation. The output data can be used to build predictive models to forecast the behavior of new chemicals based on their in vitro activities. This atrticle is a review of the studies published in the last decade and dedicated to NR ligands in silico prediction for both therapeutic and toxicological purposes. Over 100 articles concerning 14 NR subfamilies were carefully read and analyzed in order to retrieve the most commonly used computational methods to develop predictive models, to retrieve the databases deployed in the model building process and to pinpoint some of the limitations they faced.

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Section: Studied Methods and Associated Datamentioning

confidence: 99%

Section: Studied Methods and Associated Datamentioning

confidence: 99%

Review of in silico studies dedicated to the nuclear receptor family: Therapeutic prospects and toxicological concerns

et al. 2022

Self Cite

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“…To address the problem of resistance, researchers are exploring various computer-assisted approaches for drug design. These methods include quantitative structure–activity relationship (QSAR) 10 – 12 , machine learning (ML)-based models 13 – 15 , deep learning (DL)-based models 16 , molecular docking 10 , 17 , 18 , molecular dynamic simulations 18 , 19 , and pharmacophore analysis 18 , among others. It's important to note that most of these research endeavors primarily focus on targeting ERα rather than ERβ 20 .…”

Section: Introductionmentioning

confidence: 99%

StackER: a novel SMILES-based stacked approach for the accelerated and efficient discovery of ERα and ERβ antagonists

Schaduangrat,

Homdee,

Shoombuatong

2023

Sci Rep

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The role of estrogen receptors (ERs) in breast cancer is of great importance in both clinical practice and scientific exploration. However, around 15–30% of those affected do not see benefits from the usual treatments owing to the innate resistance mechanisms, while 30–40% will gain resistance through treatments. In order to address this problem and facilitate community-wide efforts, machine learning (ML)-based approaches are considered one of the most cost-effective and large-scale identification methods. Herein, we propose a new SMILES-based stacked approach, termed StackER, for the accelerated and efficient identification of ERα and ERβ inhibitors. In StackER, we first established an up-to-date dataset consisting of 1,996 and 1,207 compounds for ERα and ERβ, respectively. Using the up-to-date dataset, StackER explored a wide range of different SMILES-based feature descriptors and ML algorithms in order to generate probabilistic features (PFs). Finally, the selected PFs derived from the two-step feature selection strategy were used for the development of an efficient stacked model. Both cross-validation and independent tests showed that StackER surpassed several conventional ML classifiers and the existing method in precisely predicting ERα and ERβ inhibitors. Remarkably, StackER achieved MCC values of 0.829–0.847 and 0.712–0.786 in terms of the cross-validation and independent tests, respectively, which were 5.92–8.29 and 1.59–3.45% higher than the existing method. In addition, StackER was applied to determine useful features for being ERα and ERβ inhibitors and identify FDA-approved drugs as potential ERα inhibitors in efforts to facilitate drug repurposing. This innovative stacked method is anticipated to facilitate community-wide efforts in efficiently narrowing down ER inhibitor screening.

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EADB—A database providing curated data for developing QSAR models to facilitate the assessment of endocrine activity

Dong,

Guo,

Liu

et al. 2023

QSAR in Safety Evaluation and Risk Assessment

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Predicting Potential Endocrine Disrupting Chemicals Binding to Estrogen Receptor α (ERα) Using a Pipeline Combining Structure-Based and Ligand-Based in Silico Methods

Cited by 6 publications

References 75 publications

Review of in silico studies dedicated to the nuclear receptor family: Therapeutic prospects and toxicological concerns

Review of in silico studies dedicated to the nuclear receptor family: Therapeutic prospects and toxicological concerns

StackER: a novel SMILES-based stacked approach for the accelerated and efficient discovery of ERα and ERβ antagonists

EADB—A database providing curated data for developing QSAR models to facilitate the assessment of endocrine activity

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