Managing cancer is now one of the biggest concerns of health organizations. Many strategies have been developed in drug discovery pipelines to help rectify this problem and two of the best ones are drug repurposing and computational methods. The combination of these approaches can have immense impact on the course of drug discovery. In silico drug repurposing can significantly reduce the time, the cost and the effort of drug development. Computational methods such as structure-based drug design (SBDD) and virtual screening can predict the potentials of small molecule binders, such as drugs, for having favorable effect on a particular molecular target. However, the demand for accuracy and efficiency of SBDD requires more sophisticated and complicated approaches such as unbiased molecular dynamics (UMD) simulation that has been recently introduced. As a complementary strategy, the knowledge acquired from UMD simulations can increase the chance of finding the right candidates and the pipeline of its administration is introduced and discussed in this review. An elaboration of this pipeline is also made by detailing an example, the binding and unbinding pathways of dasatinib-c-Src kinase complex, which shows that how influential this method can be in rational drug repurposing in cancer treatment.

管理癌症现在是卫生组织最关心的问题之一。在药物发现管道中已经开发了许多策略来帮助解决这个问题，其中两个最好的策略是药物再利用和计算方法。这些方法的结合可以对药物发现的过程产生巨大的影响。计算机药物再利用可以显着减少药物开发的时间、成本和工作量。基于结构的药物设计 (SBDD) 和虚拟筛选等计算方法可以预测药物等小分子结合剂对特定分子靶点产生有利影响的潜力。然而，对 SBDD 的准确性和效率的需求需要更复杂和复杂的方法，例如最近引入的无偏分子动力学 (UMD) 模拟。作为一种补充策略，从 UMD 模拟中获得的知识可以增加找到合适候选人的机会，并且在本次审查中介绍和讨论了其管理管道。还通过详细示例来详细说明该管道，