Giuseppina Gini

Politecnico di Milano — Italy

Computer Science · Physical Sciences

34h-index4.0kcitations220works1.32yr avg
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Research Topics

Computational Drug Discovery Methods(90), Robot Manipulation and Learning(29), Analytical Chemistry and Chromatography(23), Muscle activation and electromyography studies(21), Machine Learning in Materials Science(19)

Publications220 total

From Feature‐Based Chemical Similarity to Chemical Language Models—A Paradigm Shift in Computer‐Aided Molecular Design and Property Predictions
Wiley Interdisciplinary Reviews Computational Molecular Science·2025· 3 citedOpen Access
Introduction to Machine Learning for Materials Property Modeling
Challenges and advances in computational chemistry and physics·2025
QSAR: Using the Past to Study the Present
Methods in molecular biology·2024· 1 cited
Evaluation of QSAR models for predicting mutagenicity: outcome of the Second Ames/QSAR international challenge project
SAR and QSAR in environmental research·2023· 19 citedOpen Access
Contributors
Elsevier eBooks·2023
Big data and deep learning: extracting and revising chemical knowledge from data
Elsevier eBooks·2023· 1 cited
List of contributors
Elsevier eBooks·2023
The impact of artificial intelligence methods on drug design
Elsevier eBooks·2023· 1 cited
Active upper limb prostheses: a review on current state and upcoming breakthroughs
Progress in Biomedical Engineering·2022· 90 citedOpen Access
Correction to Structures of Endocrine-Disrupting Chemicals Determine Binding to and Activation of the Estrogen Receptor α and Androgen Receptor
Environmental Science & Technology·2022· 4 cited
QSAR Methods
Methods in molecular biology·2022· 11 cited
Structures of Endocrine-Disrupting Chemicals Correlate with the Activation of 12 Classic Nuclear Receptors
Environmental Science & Technology·2021· 56 cited
From Data to Models
2021· 2 cited
QSAR modeling without descriptors using graph convolutional neural networks: the case of mutagenicity prediction
Molecular Diversity·2021· 45 cited
Programming for Intelligent Robot Systems
2020
Structures of Endocrine-Disrupting Chemicals Determine Binding to and Activation of the Estrogen Receptor α and Androgen Receptor
Environmental Science & Technology·2020· 77 cited
The QSAR similarity principle in the deep learning era: Confirmation or revision?
Foundations of Chemistry·2020· 12 cited
Machine Learning and Deep Learning Methods in Ecotoxicological QSAR Modeling
Methods in pharmacology and toxicology·2020· 20 cited
Could deep learning in neural networks improve the QSAR models?
SAR and QSAR in environmental research·2019· 38 cited
Integrating in silico models and read-across methods for predicting toxicity of chemicals: A step-wise strategy
Environment International·2019· 94 citedOpen Access
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Frequent Co-authors

Emilio Benfenati(37), Andrey A. Toropov(16), Alla P. Toropova(16), Danuta Leszczyńska(12), Jerzy Leszczyński(12), Alberto Manganaro(6), Michele Folgheraiter(5), Alessandra Roncaglioni(4), Thomas Ferrari(3), Azadi Golbamaki(3), S.E. Martyanov(3), Paolo Mazzatorta(3), Daniel Neagu(3), Nazanin Golbamaki Bakhtyari(2), Tomasz Puzyn(2), Qasim Chaudhry(2), Haoyue Tan(2), Huixiao Hong(2), Xiaowei Zhang(2), Hongxia Yu(2)