We demonstrate the application of machine learning methods to problems from biology, chemistry, and pharmacy, nameley the prediction of protein subcellular localization, prediction of chromatiographic separation of oligo nucleotides, and the prediction of percutaneous drug absorption. For these examples, we show how translating the primary data into problem-specific features is essential for solving classification and regression problems.