Recent great advances in the field of high-performance computing offer new opportunities in bioinformatics, computational chemistry, and computational biology. In this paper, we use the computational methods, for instance, the support vector machines (SVM) to optimize the prediction of solubility of compounds. SVMs are trained with known data of soluble and insoluble compounds of a database, and such information is subsequently used to improve the prediction obtained by virtual screening technique. The use of larger databases increases the probability generating leads or hits, the necessary calculation time increases with the volume of the database and the accuracy of virtual screening methods. We discuss the benefits of the use of massively parallel architectures, in particular, the graphics processing units. We empirically demonstrate that the graphics processing units are well adapted for the methodical acceleration of the support vector machines, of the order up to 45 times, compared to their sequential version. © 2018, Springer-Verlag GmbH Austria, part of Springer Nature.