QSAR dataset 雄激素受体数据集

Francesca Grisoni (francesca.grisoni '@' unimib.it), Davide Ballabio (davide.ballabio '@' unimib.it), Viviana Consonni, Milano Chemometrics and QSAR Research Group (http://www.michem.unimib.it/), Universit?? degli Studi Milano - Bicocca, Milano (Italy) Data Set Information: This dataset was used to develop classification QSAR models for the discrimination of binder/positive (199) and non-binder/negative (1488) molecules by means of different machine learning methods. Details can be found in the quoted reference: F. Grisoni, V. Consonni, D. Ballabio, (2019) Machine Learning Consensus to Predict the Binding to the Androgen Receptor within the CoMPARA project, Journal of chemical information and modeling, 59, 1839-1848; doi: 10.1021/acs.jcim.8b00794. Attributes (molecular fingerprints) were calculated at the Milano Chemometrics and QSAR Research Group (Universit?? degli Studi Milano - Bicocca, Milano, Italy) on a set of chemicals provided by the National Center of Computational Toxicology, at the U.S. Environmental Protection Agency in the framework of the CoMPARA collaborative modelling project, which targeted the development of QSAR models to identify binders to the Androgen Receptor. Attribute Information: 1024 binary molecular fingerprints and 1 experimental class: 1-1024) binary molecular fingerprint 1025) experimental class: positive (binder) and negative (non-binder) Relevant Papers: F. Grisoni, V. Consonni, D. Ballabio, (2019) Machine Learning Consensus to Predict the Binding to the Androgen Receptor within the CoMPARA project, Journal of chemical information and modeling, 59, 1839-1848; doi: 10.1021/acs.jcim.8b00794 Citation Request: Please, cite the following paper if you publish results based on the QSAR androgen receptor dataset: F. Grisoni, V. Consonni, D. Ballabio, (2019) Machine Learning Consensus to Predict the Binding to the Androgen Receptor within the CoMPARA project, Journal of chemical information and modeling, 59, 1839-1848; doi: 10.1021/acs.jcim.8b00794

弗朗西斯卡·格里索尼（Francesca Grisoni，邮箱：[e-mail]）、戴维·巴拉比奥（Davide Ballabio，邮箱：[e-mail]）、维维安娜·孔索尼，隶属于米兰化学计量学与定量构效关系（Quantitative Structure-Activity Relationship，QSAR）研究组（Milano Chemometrics and QSAR Research Group，http://www.michem.unimib.it/），意大利米兰比可卡大学（Università degli Studi Milano - Bicocca，米兰，意大利）。

数据集概况：本数据集用于开发分类定量构效关系（QSAR）模型，以区分雄激素受体（Androgen Receptor）结合剂（阳性样本，共199个）与非结合剂（阴性样本，共1488个），所用方法涵盖多种机器学习技术。详细研究细节可参见下述参考文献：F. Grisoni, V. Consonni, D. Ballabio, (2019) 《Machine Learning Consensus to Predict the Binding to the Androgen Receptor within the CoMPARA project》，*Journal of Chemical Information and Modeling*，59卷，1839-1848页；DOI: 10.1021/acs.jcim.8b00794。本研究的分子指纹属性由该研究组在美国环境保护署（U.S. Environmental Protection Agency）CoMPARA协同建模项目框架下，基于美国国家计算毒理学中心（National Center of Computational Toxicology）提供的化学品集计算得到。该项目旨在开发定量构效关系模型以识别雄激素受体结合剂。

属性信息：本数据集包含1024个二元分子指纹与1个实验类别标签：
1. 第1至1024项：二元分子指纹
2. 第1025项：实验类别标签，分为阳性（结合剂）与阴性（非结合剂）两类。

相关论文：F. Grisoni, V. Consonni, D. Ballabio, (2019) 《Machine Learning Consensus to Predict the Binding to the Androgen Receptor within the CoMPARA project》，*Journal of Chemical Information and Modeling*，59卷，1839-1848页；DOI: 10.1021/acs.jcim.8b00794。

引用要求：若基于本雄激素受体QSAR数据集发表研究成果，请引用下述论文：F. Grisoni, V. Consonni, D. Ballabio, (2019) 《Machine Learning Consensus to Predict the Binding to the Androgen Receptor within the CoMPARA project》，*Journal of Chemical Information and Modeling*，59卷，1839-1848页；DOI: 10.1021/acs.jcim.8b00794。