Simultaneously Optimized Support Vector Regression Combined With Genetic Algorithm for QSAR Analysis of KDR/VEGFR-2 Inhibitors

Chi-MIC-share: a new feature selection algorithm for quantitative structure–activity relationship models

An algorithm based on an improved maximal information coefficient and a redundant allocation strategy, which can terminate feature selection automatically, is presented.

3D-MoRSE descriptors explained

3D-MoRSE is a very flexible 3D structure encoding framework for chemoinformatics and QSAR purposes due to the range of scattering parameter values and variety of weighting schemes used. While arising in many QSAR studies, up to this time they were considered as hardly interpreted and were treated like a "black box". This study is intended to lift the veil of mystery, providing a comprehensible way to the interpretation of 3D-MoRSE descriptors in QSAR/QSPR studies. The values of these descriptors are calculated with rather simple equation, but may vary when using differing starting geometries as optimization input. This variation increases with scattering parameter and also is higher for electronegativity weighted and unweighted descriptors. Though each 3D-MoRSE descriptor incorporates the information about the whole molecule structure, its final value is derived mostly from short-distance (up to 3Å) atomic pairs. And, if a QSAR study covers structurally similar set of compounds, then the role of 3D-MoRSE descriptor in a model can be interpreted using just several pairs of neighbor atoms. The guide to interpretation process is discussed and illustrated with a case study. Realizing the mathematical concept behind 3D-descriptors and knowing their properties it is easy not only to interpret, but also to predict the importance of 3D-MoRSE descriptors in a QSAR study. The process of prediction is described on the practical example and its accuracy is confirmed with further QSAR modeling.