docking analysis cdocker protocol in discovery studio 2.5 (Accelrys)
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Accelrys
docking analysis cdocker protocol in discovery studio 2.5
Docking Analysis Cdocker Protocol In Discovery Studio 2.5, supplied by Accelrys, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/docking+analysis+cdocker+protocol+in+discovery+studio+2%2E5/docking+analysis+cdocker+protocol+in+discovery+studio+2+5/10__1016_slash_j__cplett__2018__06__022-34-7-30
Average 90 stars, based on 1 article reviews
Docking Analysis Cdocker Protocol In Discovery Studio 2.5, supplied by Accelrys, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/docking+analysis+cdocker+protocol+in+discovery+studio+2%2E5/docking+analysis+cdocker+protocol+in+discovery+studio+2+5/10__1016_slash_j__cplett__2018__06__022-34-7-30
Average 90 stars, based on 1 article reviews
docking analysis cdocker protocol in discovery studio 2.5 - by Bioz Stars,
2026-09
90/100 stars
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Binding Assay:Article Title: Ternary classification models for predicting hormonal activities of chemicals via nuclear receptors Article Snippet: Endocrine disrupting chemicals (EDCs) can exhibit adverse effects by increasing or blocking hormonal activities as agonists or antagonists through nuclear receptors.. Computational toxicology research provides a fast and automated screening tool for determining the potential effects of EDCs.. Here, we collected a large dataset of known hormonal activities to develop ternary classification models of androgen receptor (AR) and thyroid hormone receptor (TR), in combination linear discriminant analysis (LDA), classification and regression trees (CART), and support vector machines (SVM). Software:Article Title: Ternary classification models for predicting hormonal activities of chemicals via nuclear receptors Article Snippet: Endocrine disrupting chemicals (EDCs) can exhibit adverse effects by increasing or blocking hormonal activities as agonists or antagonists through nuclear receptors.. Computational toxicology research provides a fast and automated screening tool for determining the potential effects of EDCs.. Here, we collected a large dataset of known hormonal activities to develop ternary classification models of androgen receptor (AR) and thyroid hormone receptor (TR), in combination linear discriminant analysis (LDA), classification and regression trees (CART), and support vector machines (SVM). |