An overview of the structure-information representation (SIR) method of molecular structure description is presented with an emphasis on structure descriptors found useful in QSAR modeling of ADME-Tox endpoints. An artificial neural network (ANN) model of human plasma protein binding will be presented as an example of the utility of this method. A novel non-linear trend analysis used to interpret the relationship between changes in the input descriptor values and the corresponding change in the output prediction will be discussed as a means of interpretation for the input descriptors. Molconn structure-information representation descriptors were used in conjunction with artificial neural network descriptor selection and data-fitting techniques to produce a human plasma protein binding model based on 1000 drugs. The model demonstrated an external validation MAE of 12.7% on 200 drugs, with 90% of predictions within 30% of the experimental value. Discussion will include the role of the predicted properties that are used as inputs for this model.