Signaling pathways oversee highly efficient cellular mechanisms such as growth, division, and death. These processes are controlled by robust negative feedback loops that inhibit receptor-mediated growth factor pathways. Specifically, the ERK, the AKT, and the S6K feedback loops attenuate signaling via growth factor receptors and other kinase receptors to regulate cell growth. Irregularity in any of these supervised processes can lead to uncontrolled cell proliferation and possibly Cancer. These irregularities primarily occur as mutated genes, and an exhaustive search of the perfect drug combination by performing experiments can be both costly and complex. Hence, in this paper, we model the Lung Cancer pathway as a Modified Boolean Network that incorporates feedback. By simulating this network, we theoretically predict the drug combinations that achieve the desired goal for the majority of mutations. Our theoretical analysis identifies Cryptotanshinone, a traditional Chinese herb derivative, as a potent drug component in the fight against cancer. We validated these theoretical results using multiple wet lab experiments carried out on H2073 and SW900 lung cancer cell lines.