Neural network models for predicting the properties of chemical compounds

Neural networks are a universal tool used to investigate the dependences between the structure of organic compounds and a broad spectrum of their physicochemical properties. The potential of neural network modeling is not yet exhausted, as the increasing number of publications on their use indicates. Neural network models can solve both classification (for a discrete set of values of the modeled property) and regression problems (for continuous values of the modeled property). The reason for the popularity of neural network models in applied research is their clarity and the fact that no deep knowledge of mathematical statistics is required for their effective use.