A Primer on Molecular Similarity in QSAR and Virtual Screening Part III – Connecting descriptors and experimental measurements – model generation

The generation of models relating structure and some form of measurement of structural properties, be it bioactivity or any physicochemical property, consists of the description of the problem, the choice of a dataset of experimental endpoints, and the construction of the actual mathematical model. The descriptor generation was discussed in the first part of this primer, with the conclusion to choose descriptors relevant to the problem and of not overly complex nature. In the second part we were discussing experimental endpoints that can be used as “output variables” of models. Here the conclusion was that also experimental data are not without fault – data measured in a single laboratory may show significant differences (e.g. in high-throughput screenings), and combining results from different labs might be even more error-prone. Therefore, a clean dataset, measured by a single, well-defined experimental procedure should be used in the ideal case. In the current, concluding part of the primer I would like to discuss the basics of constructing a useful mathematical model that connects molecular descriptors as an independent variable, and outputs (predicted) properties as the dependent variable. The focus of this part of the primer will be on two steps commonly performed in the generation of QSAR/QSPR models, namely feature selection and model validation.