The pharmacokinetic properties of absorption, distribution, metabolism and excretion (ADME) play a crucial role in drug discovery and development, since many drug candidates fail due to an inappropriate pharmacokinetic profile. Cytochrome P450 (CYP) enzymes are predominantly involved in Phase 1 metabolism of xenobiotics. Thus, it is important to better understand and prognosticate substrate binding and inhibition of CYP450.The goal of this study was to obtain QSAR (Quantitative Structure-Activity Relationship) models to identify substrates and inhibitors of CYP3A4. The data sets were collected and curated from online available databases and literature. Several QSAR models were obtained and validated according to the recommendations of the Organization for Economic Co-operation Development (OECD). The combination of different descriptors and machine learning methods led to robust and predictive QSAR models with high coverage. The interpretation of developed models was performed using the predicted probability maps (PPM). These maps help to encode major structural fragments to classify compounds as inhibitors or not inhibitors of CYP3A4. In conclusion, the obtained models can reliably identify substrates and non-substrates, and inhibitors and non-inhibitors of CYP3A4, which is very important  in the early stages of the development of new drugs.