Аннотация:The Method of Continuous Molecular Fields is a universal approach to
predict various properties of chemical compounds, in which molecules are represented
by means of continuous fields (such as electrostatic, steric, electron density
functions, etc.). The essence of the proposed approach consists in performing statistical
analysis of functional molecular data by means of joint application of kernel
machine learning methods and special kernels which compare molecules by computing
overlap integrals of their molecular fields. This approach is an alternative
to traditional methods of building 3D “structure-activity” and “structure-property”
models based on the use of fixed sets of molecular descriptors. The methodology
of the approach is described in this chapter, followed by its application to building
regression 3D-QSAR models and conducting virtual screening based on oneclass
classification models. The main directions of the further development of this
approach are outlined at the end of the chapter.