Search Results - "Gini, Giuseppina"

The QSAR similarity principle in the deep learning era: Confirmation or revision? by Gini, Giuseppina

“…Structure–activity relationship (SAR) and quantitative SAR (QSAR) are modeling methods largely used in assessing biological properties of chemical substances…”
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Integrating in silico models and read-across methods for predicting toxicity of chemicals: A step-wise strategy by Benfenati, Emilio, Chaudhry, Qasim, Gini, Giuseppina, Dorne, Jean Lou

“…In silico methods and models are increasingly used for predicting properties of chemicals for hazard identification and hazard characterisation in the absence…”
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QSAR modeling without descriptors using graph convolutional neural networks: the case of mutagenicity prediction by Hung, Chiakang, Gini, Giuseppina

“…Deep neural networks are effective in learning directly from low-level encoded data without the need of feature extraction. This paper shows how QSAR models…”
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QSAR: Using the Past to Study the Present by Gini, Giuseppina C

“…Quantitative structure-activity relationships (QSAR) is a method for predicting the physical and biological properties of small molecules; it is in use in…”
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Structures of Endocrine-Disrupting Chemicals Correlate with the Activation of 12 Classic Nuclear Receptors by Tan, Haoyue, Chen, Qinchang, Hong, Huixiao, Benfenati, Emilio, Gini, Giuseppina C, Zhang, Xiaowei, Yu, Hongxia, Shi, Wei

“…Endocrine-disrupting chemicals (EDCs) can inadvertently interact with 12 classic nuclear receptors (NRs) that disrupt the endocrine system and cause adverse…”
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Structures of Endocrine-Disrupting Chemicals Determine Binding to and Activation of the Estrogen Receptor α and Androgen Receptor by Tan, Haoyue, Wang, Xiaoxiang, Hong, Huixiao, Benfenati, Emilio, Giesy, John P, Gini, Giuseppina C, Kusko, Rebeca, Zhang, Xiaowei, Yu, Hongxia, Shi, Wei

“…Endocrine-disrupting chemicals (EDCs) can interact with nuclear receptors, including estrogen receptor α (ERα) and androgen receptor (AR), to affect the normal…”
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Correction to Structures of Endocrine-Disrupting Chemicals Determine Binding to and Activation of the Estrogen Receptor α and Androgen Receptor by Tan, Haoyue, Wang, Xiaoxiang, Hong, Huixiao, Benfenati, Emilio, Giesy, John P, Gini, Giuseppina C, Kusko, Rebeca, Zhang, Xiaowei, Yu, Hongxia, Shi, Wei

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QSAR Methods by Gini, Giuseppina

“…This chapter introduces the basis of computational chemistry and discusses how computational methods have been extended from physical to biological properties,…”
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The acceptance of in silico models for REACH: Requirements, barriers, and perspectives by Benfenati, Emilio, Diaza, Rodolfo Gonella, Cassano, Antonio, Pardoe, Simon, Gini, Giuseppina, Mays, Claire, Knauf, Ralf, Benighaus, Ludger

“…In silico models have prompted considerable interest and debate because of their potential value in predicting the properties of chemical substances for…”
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SMILES-based QSAR approaches for carcinogenicity and anticancer activity: comparison of correlation weights for identical SMILES attributes by Toropov, Andrey A, Toropova, Alla P, Benfenati, Emilio, Gini, Giuseppina, Leszczynska, Danuta, Leszczynski, Jerzy

“…CORAL software (http://www.insilico.eu/coral/) has been used for modeling of carcinogenicity (logTD50) of 401 compounds, and anticancer activity (-logIC50) of…”
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Calculation of molecular features with apparent impact on both activity of mutagens and activity of anticancer agents by Toropov, Andrey A, Toropova, Alla P, Benfenati, Emilio, Gini, Giuseppina, Leszczynska, Danuta, Leszczynski, Jerzy

“…The analysis of the influence of molecular features which can be extracted from the simplified molecular input line entry system (SMILES) and involved in the…”
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QSAR Methods by Gini, Giuseppina

“…In this chapter, we introduce the basis of computational chemistry and discuss how computational methods have been extended to some biological properties and…”
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QSAR modelling toxicity toward rats of inorganic substances by means of CORAL by Toropova Alla, Toropov Andrey, Benfenati Emilio, Gini Giuseppina

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OCWLGI descriptors: theory and praxis by Toropov, Andrey A, Toropova, Alla P, Benfenati, Emilio, Gini, Giuseppina

“…The aim of this review is description of the logic and evolution of optimal descriptors OCWLGI calculated with the molecular graph and the demonstration of…”
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Development of QSAR models for predicting anti-HIV-1 activity using the Monte Carlo method by Toropov Andrey, Toropova Alla, Raska Ivan, Benfenati Emilio, Gini Giuseppina

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CORAL: binary classifications (active/inactive) for Liver-Related Adverse Effects of Drugs by Toropov, Andrey A, Toropova, Alla P, Rasulev, Bakhtiyor F, Benfenati, Emilio, Gini, Giuseppina, Leszczynska, Danuta, Leszczynski, Jerzy

“…Classification data related to the Liver-Related Adverse Effects of Drugs have been studied with the CORAL software (http://www.insilico.eu/coral). Two…”
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An open source multistep model to predict mutagenicity from statistical analysis and relevant structural alerts by Ferrari, Thomas, Gini, Giuseppina

“…Background Mutagenicity is the capability of a substance to cause genetic mutations. This property is of high public concern because it has a close…”
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QSAR modeling of anxiolytic activity taking into account the presence of keto- and enol-tautomers by balance of correlations with ideal slopes by Toropova Alla, Toropov Andrey, Benfenati Emilio, Gini Giuseppina, Leszczynska Danuta, Leszczynski Jerzy

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Reasoning on objects and grasping using description logics by Vitucci, Nicola, Gini, Giuseppina

“…In the growing interest about robot ontologies, the role of description logics (DL) used to query the ontology is often neglected. After discussing the…”
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New Joint Design to Create a More Natural and Efficient Biped by Gini, Giuseppina, Scarfogliero, Umberto, Folgheraiter, Michele

“…This paper presents a human‐oriented approach to design the mechanical architecture and the joint controller for a biped robot. Starting from the analysis of…”
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