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Prediction of Chemical‐Physical Properties by Neural Networks for Structures
Author(s) -
Duce Celia,
Micheli Alessio,
Solaro Roberto,
Starita Antonina,
Tiné Maria Rosaria
Publication year - 2006
Publication title -
macromolecular symposia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.257
H-Index - 76
eISSN - 1521-3900
pISSN - 1022-1360
DOI - 10.1002/masy.200650203
Subject(s) - cheminformatics , flexibility (engineering) , artificial neural network , solvation , computer science , glass transition , polymer , biological system , molecule , artificial intelligence , materials science , computational chemistry , chemistry , mathematics , organic chemistry , statistics , biology
Here we present an overview of a new approach to cheminformatics based on recursive neural networks. This approach allows for combining the flexibility and advantages of neural networks with the representational power of structured domains. Current advances, which include applications to the prediction of the solvation free energy of small molecules in water and of the glass transition temperature of (meth)acrylic polymers are reported.