Department of Chemistry, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, Pakistan.
Key Laboratory of Economic Plants and Biotechnology, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 560201, Yunnan, China.
Anticancer Agents Med Chem. 2019;19(5):592-598. doi: 10.2174/1871520618666181009163014.
One of the major goals of computational chemists is to determine and develop the pathways for anticancer drug discovery and development. In recent past, high performance computing systems elicited the desired results with little or no side effects. The aim of the current review is to evaluate the role of computational chemistry in ascertaining kinases as attractive targets for anticancer drug discovery and development.
Research related to computational studies in the field of anticancer drug development is reviewed. Extensive literature on achievements of theorists in this regard has been compiled and presented with special emphasis on kinases being the attractive anticancer drug targets.
Different approaches to facilitate anticancer drug discovery include determination of actual targets, multi-targeted drug discovery, ligand-protein inverse docking, virtual screening of drug like compounds, formation of di-nuclear analogs of drugs, drug specific nano-carrier design, kinetic and trapping studies in drug design, multi-target QSAR (Quantitative Structure Activity Relationship) model, targeted co-delivery of anticancer drug and siRNA, formation of stable inclusion complex, determination of mechanism of drug resistance, and designing drug like libraries for the prediction of drug-like compounds. Protein kinases have gained enough popularity as attractive targets for anticancer drugs. These kinases are responsible for uncontrolled and deregulated differentiation, proliferation, and cell signaling of the malignant cells which result in cancer.
Interest in developing drugs through computational methods is a growing trend, which saves equally the cost and time. Kinases are the most popular targets among the other for anticancer drugs which demand attention. 3D-QSAR modelling, molecular docking, and other computational approaches have not only identified the target-inhibitor binding interactions for better anticancer drug discovery but are also designing and predicting new inhibitors, which serve as lead for the synthetic preparation of drugs. In light of computational studies made so far in this field, the current review highlights the importance of kinases as attractive targets for anticancer drug discovery and development.
计算化学家的主要目标之一是确定和开发抗癌药物发现和开发的途径。在最近的过去，高性能计算系统在几乎没有或没有副作用的情况下产生了所需的结果。本综述的目的是评估计算化学在确定激酶作为有吸引力的抗癌药物靶点方面的作用。
综述了抗癌药物开发领域的计算研究。编译了关于该领域理论家成就的广泛文献，并特别强调了激酶作为有吸引力的抗癌药物靶点。
促进抗癌药物发现的不同方法包括确定实际靶点、多靶点药物发现、配体-蛋白反向对接、药物样化合物的虚拟筛选、药物双核类似物的形成、药物特异性纳米载体设计、药物设计中的动力学和捕获研究、多靶点 QSAR（定量构效关系）模型、抗癌药物和 siRNA 的靶向共递药、稳定包合复合物的形成、耐药机制的确定以及用于预测类药性化合物的类药性文库的设计。蛋白激酶作为有吸引力的抗癌药物靶点已获得足够的关注。这些激酶负责恶性细胞的不受控制和失调的分化、增殖和细胞信号转导，导致癌症。
通过计算方法开发药物的兴趣是一种增长趋势，它同样节省了成本和时间。激酶是抗癌药物中最受欢迎的靶点之一，需要引起重视。3D-QSAR 建模、分子对接和其他计算方法不仅确定了靶标-抑制剂结合相互作用，以更好地发现抗癌药物，而且还设计和预测新的抑制剂，作为合成药物制备的先导。鉴于目前在该领域进行的计算研究，本综述强调了激酶作为有吸引力的抗癌药物靶点在抗癌药物发现和开发中的重要性。
