Bhargava Harshita, Sharma Amita, Suravajhala Prashanth
Department of Computer Science & IT, IIS (Deemed to be University), Jaipur, India.
Bioclues.org, Kukatpally, Hyderabad, 500072, India.
Curr Genomics. 2021 Dec 30;22(5):328-338. doi: 10.2174/1389202922666210920125800.
The drug discovery process has been a crucial and cost-intensive process. This cost is not only monetary but also involves risks, time, and labour that are incurred while introducing a drug in the market. In order to reduce this cost and the risks associated with the drugs that may result in severe side effects, the in silico methods have gained popularity in recent years. These methods have had a significant impact on not only drug discovery but also the related areas such as drug repositioning, drug-target interaction prediction, drug side effect prediction, personalised medicine, . Amongst these research areas predicting interactions between drugs and targets forms the basis for drug discovery. The availability of big data in the form of bioinformatics, genetic databases, along with computational methods, have further supported data-driven decision-making. The results obtained through these methods may be further validated using or experiments. This validation step can further justify the predictions resulting from approaches, further increasing the accuracy of the overall result in subsequent stages. A variety of approaches are used in predicting drug-target interactions, including ligand-based, molecular docking based and chemogenomic-based approaches. This paper discusses the chemogenomic methods, considering drug target interaction as a classification problem on whether or not an interaction between a particular drug and target would serve as a basis for understanding drug discovery/drug repositioning. We present the advantages and disadvantages associated with their application.
药物发现过程一直是一个关键且成本高昂的过程。这种成本不仅是金钱方面的，还涉及在将药物推向市场时所产生的风险、时间和劳动力。为了降低这种成本以及与可能导致严重副作用的药物相关的风险，近年来计算机模拟方法越来越受欢迎。这些方法不仅对药物发现产生了重大影响，而且对药物重新定位、药物 - 靶点相互作用预测、药物副作用预测、个性化医疗等相关领域也产生了重大影响。在这些研究领域中，预测药物与靶点之间的相互作用构成了药物发现的基础。生物信息学、基因数据库等形式的大数据以及计算方法的可用性，进一步支持了数据驱动的决策制定。通过这些方法获得的结果可以使用 或 实验进一步验证。这一验证步骤可以进一步证明 方法所得预测的合理性，从而在后续阶段进一步提高整体结果的准确性。预测药物 - 靶点相互作用使用了多种方法，包括基于配体的方法、基于分子对接的方法和基于化学基因组学的方法。本文将药物 - 靶点相互作用视为一个关于特定药物与靶点之间的相互作用是否可作为理解药物发现/药物重新定位基础的分类问题，讨论化学基因组学方法。我们展示了其应用相关的优点和缺点。
