  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 one-class classification models. The main
directions of the further development of this approach are outlined at the end
of the chapter.
