Nazli Adila, Qiu Jingyi, Tang Ziyi, He Yun
Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, School of Pharmaceutical Sciences, Chongqing University, Chongqing, 401331, P. R. China.
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, 266 Fangzheng Avenue, Chongqing, 400714, P. R. China.
Curr Med Chem. 2024;31(4):464-501. doi: 10.2174/0929867330666230123143458.
With the emergence of drug-resistant bacteria, the development of new antibiotics is urgently required. Target-based drug discovery is the most frequently employed approach for the drug development process. However, traditional drug target identification techniques are costly and time-consuming. As research continues, innovative approaches for antibacterial target identification have been developed which enabled us to discover drug targets more easily and quickly.
In this review, methods for finding drug targets from omics databases have been discussed in detail including principles, procedures, advantages, and potential limitations. The role of phage-driven and bacterial cytological profiling approaches is also discussed. Moreover, current article demonstrates the advancements being made in the establishment of computational tools, machine learning algorithms, and databases for antibacterial target identification.
Bacterial drug targets successfully identified by employing these aforementioned techniques are described as well.
The goal of this review is to attract the interest of synthetic chemists, biologists, and computational researchers to discuss and improve these methods for easier and quicker development of new drugs.
随着耐药菌的出现，迫切需要开发新的抗生素。基于靶点的药物发现是药物开发过程中最常用的方法。然而，传统的药物靶点识别技术成本高且耗时。随着研究的不断深入，已开发出用于抗菌靶点识别的创新方法，使我们能够更轻松、快速地发现药物靶点。
在本综述中，详细讨论了从组学数据库中寻找药物靶点的方法，包括原理、程序、优点和潜在局限性。还讨论了噬菌体驱动和细菌细胞学分析方法的作用。此外，本文展示了在建立用于抗菌靶点识别的计算工具、机器学习算法和数据库方面取得的进展。
还描述了通过采用上述技术成功鉴定的细菌药物靶点。
本综述的目的是吸引合成化学家、生物学家和计算研究人员的关注，以讨论和改进这些方法，从而更轻松、快速地开发新药。
