The advent of molecular medicine allowed identifying the malfunctioning of subcellular processes as the source of many diseases. Since then, drugs are not only discovered, but actually designed to fulfill a precise task. Modern computational techniques, based on molecular modeling, play a relevant role both in target identification and drug lead development. By flanking and integrating standard experimental techniques, modeling has proven itself as a powerful tool across the drug design process. The success of computational methods depends on a balance between cost (computation time) and accuracy. Thus, the integration of innovative theories and more powerful hardware architectures allows molecular modeling to be used as a reliable tool for rationalizing the results of experiments and accelerating the development of new drug design strategies. We present an overview of the most common quantum chemistry computational approaches, providing for each one a general theoretical introduction to highlight limitations and strong points. We then discuss recent developments in software and hardware resources, which have allowed state-of-the-art of computational quantum chemistry to be applied to drug development.