Background: Non-small cell lung cancer, also known as NSCLC, is the type of lung cancer responsible for the biggest number of deaths globally. The signaling protein known as ALK5 plays a significant part in cancer development by contributing to the multiplication of cancer cells and promoting an inflammatory response through its interactions with immune cells. The pharmaceutical industry uses computational ligand-protein docking and screening for various chemicals. This is done to speed up the drug development process and locate promising therapeutic candidates. This research aimed to use computational approaches to search for efficient ALK5 inhibitors that could slow the proliferation of cancer cells. In addition, the research attempted to determine which plant-derived ligand would be the most effective inhibitor by calculating the binding free energy between the ligand and the protein. This would allow for the selection of the most effective plant-derived ligand. Methods: In order to achieve this goal, proteins exhibiting a low mutation rate were chosen, and Autodock for Flexible Receptors (ADR) was used to construct the Ramachandran plot, successfully targeting the protein. Results: According to the findings of the in-silico research, the molecule farnesiferol c, which is derived from Ferula assafoetida, is the most effective ligand to inhibit the ALK5 protein. Its G value is lower than that of commercial medications and 12 other ligands and displays the highest potency level. Conclusion: this intriguing chemical is promising as a therapeutic candidate for treating non-small cell lung cancer. However, additional in-vitro testing is necessary to determine that it is effective against the ALK5 protein as a targeted therapy for lung cancer in the future.