Obtain chemical insight from neural network results I have recently read many papers where neural networks (NNs) are trained to predict chemical properties (starting from the structure of small chemical compounds) for compounds rather close to the ones with which they were trained. In some cases the predictions are rather good. But I see that apart from the predictive capability, people use NNs as "black boxes" in the sense that they do not tell you or give you chemical insight about why that compound has that value for that predicted property. At least that is my impression now. So my question here is, do you know cases where useful chemical information can be extracted from NN predictions?
Since you are interested in Artificial Neural Networks that also explain their decisions/predictions you may be interested in transparent neural networks, although they have not been used to my knowledge on chemical information.

Other interesting approaches include inductive logic programming which have among other things been applied to predicting binding of proteins to hexose