Prediction of Chemical-Physical Properties by Neural Networks for Structures

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.