Traditional drug research and development is a long and tedious process that has become incredibly inefficient relative to the amount of money put in. In fact, "the number of new drugs approved per billion US dollars spent on R&D has halved roughly every 9 years since 1950." Although in silico research cannot replace empirical trials, it allows us to efficiently and cheaply elucidate promising drug candidates to expedite the creation of new treatments, especially when multiple computational procedures are used in tandem. As new technologies emerge and the amount of accessible drug data continues to grow, machine learning and other computational algorithms will continue to improve in efficacy and allow for unprecedented advancements in the pharmaceutical field.