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Could deep learning in neural networks improve the QSAR models?
Gini, G; Zanoli, F; Gamba, A; Raitano, G; Benfenati, E.
Affiliation
  • Gini G; DEIB, Politecnico di Milano, Milan, Italy.
  • Zanoli F; DEIB, Politecnico di Milano, Milan, Italy.
  • Gamba A; Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Laboratory of Environmental Chemistry and Toxicology, Milan, Italy.
  • Raitano G; Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Laboratory of Environmental Chemistry and Toxicology, Milan, Italy.
  • Benfenati E; Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Laboratory of Environmental Chemistry and Toxicology, Milan, Italy.
SAR QSAR Environ Res ; 30(9): 617-642, 2019 Sep.
Article in En | MEDLINE | ID: mdl-31460798
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Neural Networks, Computer / Quantitative Structure-Activity Relationship / Deep Learning / Mutagens Type of study: Prognostic_studies Language: En Journal: SAR QSAR Environ Res Journal subject: SAUDE AMBIENTAL Year: 2019 Document type: Article Affiliation country: Italy Country of publication: United kingdom
Fulltext
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Neural Networks, Computer / Quantitative Structure-Activity Relationship / Deep Learning / Mutagens Type of study: Prognostic_studies Language: En Journal: SAR QSAR Environ Res Journal subject: SAUDE AMBIENTAL Year: 2019 Document type: Article Affiliation country: Italy Country of publication: United kingdom