Current virtual screening applications focus not only on biological activity, but also on additional relevant properties of drug candidates, like absorption, distribution, metabolism, and excretion (ADME). In first-pass virtual screening, these prediction systems must be very fast because typically several millions of compounds must be processed. We have developed a linear PLS-based prediction system for binary classification of drug-drug interaction liability caused by cytochrome P450 3A4 inhibition. The system was trained using IC 5 0 values of 311 carefully selected molecules out of a raw data set containing 1152 compounds. It correctly predicts 95% of the training data and 90% of a semi-independent validation data set. The PLS model was calculated from 333 descriptors encoding a molecule. It outperforms an approach utilizing a three layered feed-forward artificial neural network architecture. The average calculation time required for a prediction is less than 0.3 seconds per molecule on a single microprocessor.