Machine Learning in Drug Design

This paper describes an approach to modelling drug activity using machine learning tools. Some experiments in modelling the quantitative structure-activity relationship (QSAR) using a standard, Hansch, method and a machine learning systems, MAGNUS ASSISTANT and RETIS, on the same data. The results achieved by the machine learning systems are better than the results of the Hansch method; therefore, machine learning tools can be considered as very promising for solving that kind of problems. Obtained results also illustrate the variations of performance of the different machine learning systems applied to this drug design problem.