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Serena Manganelli
Researcher at Mario Negri Institute for Pharmacological Research
Publications - 25
Citations - 555
Serena Manganelli is an academic researcher from Mario Negri Institute for Pharmacological Research. The author has contributed to research in topics: Quantitative structure–activity relationship & Applicability domain. The author has an hindex of 11, co-authored 24 publications receiving 374 citations. Previous affiliations of Serena Manganelli include Nestlé.
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Journal ArticleDOI
CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity.
Kamel Mansouri,Nicole Kleinstreuer,Ahmed Abdelaziz,Domenico Alberga,Vinicius M. Alves,Vinicius M. Alves,Patrik L. Andersson,Carolina Horta Andrade,Fang Bai,Ilya A. Balabin,Davide Ballabio,Emilio Benfenati,Barun Bhhatarai,Scott Boyer,Jingwen Chen,Viviana Consonni,Sherif Farag,Denis Fourches,Alfonso T. García-Sosa,Paola Gramatica,Francesca Grisoni,Christopher M. Grulke,Huixiao Hong,Dragos Horvath,Xin Hu,Ruili Huang,Nina Jeliazkova,Jiazhong Li,Xuehua Li,Huanxiang Liu,Serena Manganelli,Giuseppe Felice Mangiatordi,Uko Maran,Gilles Marcou,Todd M. Martin,Eugene N. Muratov,Dac-Trung Nguyen,Orazio Nicolotti,Nikolai Georgiev Nikolov,Ulf Norinder,Ester Papa,Michel Petitjean,Geven Piir,Pavel V. Pogodin,Vladimir Poroikov,Xianliang Qiao,Ann M. Richard,Alessandra Roncaglioni,Patricia Ruiz,Chetan Rupakheti,Chetan Rupakheti,Sugunadevi Sakkiah,Alessandro Sangion,Karl-Werner Schramm,Chandrabose Selvaraj,Imran Shah,Sulev Sild,Lixia Sun,Olivier Taboureau,Yun Tang,Igor V. Tetko,Roberto Todeschini,Weida Tong,Daniela Trisciuzzi,Alexander Tropsha,George Van Den Driessche,Alexandre Varnek,Zhongyu Wang,Eva Bay Wedebye,Antony J. Williams,Hongbin Xie,Alexey V. Zakharov,Ziye Zheng,Richard S. Judson +73 more
TL;DR: The Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts are described, which follows the steps of the Collaborative Estrogen Recept Activity Prediction Project (CERAPP).
Journal ArticleDOI
Comparison of in silico tools for evaluating rat oral acute toxicity.
Rodolfo Gonella Diaza,Serena Manganelli,Antonella Esposito,Alessandra Roncaglioni,Alberto Manganaro,Emilio Benfenati +5 more
TL;DR: Five software programs for the evaluation of mammalian acute toxicity, exploring acute oral toxicity data expressed as median lethal dose (LD50), and found that all models gave high performance for certain classes while other classes were always badly predicted.
Journal ArticleDOI
QSAR Modeling of ToxCast Assays Relevant to the Molecular Initiating Events of AOPs Leading to Hepatic Steatosis
Domenico Gadaleta,Serena Manganelli,Alessandra Roncaglioni,Cosimo Toma,Emilio Benfenati,Enrico Mombelli +5 more
TL;DR: This article presents QSAR models based on random forest classifiers and DRAGON molecular descriptors for the prediction of in vitro assays that are relevant to MIEs leading to hepatic steatosis and proves to be useful as an effective in silico screening test for hepatic Steatosis.
Journal ArticleDOI
QSAR model for predicting cell viability of human embryonic kidney cells exposed to SiO2 nanoparticles
TL;DR: The average statistical quality of the model for the viability (%) of HEK293 exposed to different concentrations of silica nanoparticles measured by MTT assay is satisfactory and the R(2) values of the best models were above 0.68.
Journal ArticleDOI
Comparing the CORAL and random forest approaches for modelling the in vitro cytotoxicity of silica nanomaterials
Antonio Cassano,Richard L. Marchese Robinson,Anna Palczewska,Tomasz Puzyn,Agnieszka Gajewicz,Lang Tran,Serena Manganelli,Mark T. D. Cronin +7 more
TL;DR: With regard to the physicochemical properties of the nanomaterials, the aspect ratio and zeta potential were found to be the two most important variables for Random Forest, and the average feature contributions calculated for the corresponding descriptors were consistent with the clear trends observed in the data set.