Drug research and development has a high attrition rate, with many promising drugs failing for efficacy or safety in the clinic. Increased use of detailed modeling approaches like quantitative systems pharmacology (QSP) may help in reducing overall failure rate, by helping the industry in decisions to fail early and cheaply, or to focus on patients and drug combinations that are more likely to respond or synergize, respectively. QSP offers computational methods to simulate how well different therapies may work in a patient, and therefore to better predict drug performance and reduce the cost in the development of new drug therapies. However, the development of detailed models requires a significant amount of biological data, and models often require knowledge of specific mechanisms. Coarse-grained, network-based models, such as Boolean and logic models, provide a tool for simulating complex systems without knowledge of specific mechanisms. These tools can be used to make early predictions about a biological system and can facilitate the development of more complex models. We offer a literature review of how Boolean modeling techniques are used in the identification of novel drug targets, as well as how they fall into the pipeline of developing in-depth ordinary differential equation models.