Sankar Jeyanthi, Rajendran Venkatesh, Kuriakose Beena Briget, Alhazmi Amani Hamad, Wong Ling Shing, Muthusamy Karthikeyan
Pharmacogenomics and CADD Lab, Department of Bioinformatics, Alagappa University, Karaikudi, 630 003, Tamil Nadu, India.
Department of Basic Medical Sciences, College of Applied Medical Sciences, King Khalid University, Khamis Mushyt, Abha, 61412, Saudi Arabia.
Sci Rep. 2025 Jul 14;15(1):25367. doi: 10.1038/s41598-025-08653-4.
The process of drug discovery is intricate, and encompasses a series of detailed phases of research, development, and testing, aimed at evaluating the safety and effectiveness of prospective therapeutic agents. Artificial Intelligence has emerged as a transformative tool in this domain, adept at analysing vast datasets to uncover intricate patterns and relationships unperceivable to humans. This study introduces a bioactivity prediction application employing the Quantitative Structure-Activity Relationship model to forecast bioactivity against Angiotensin II receptor, a major drug target in hypertension management. Angiotensin II receptor modulation holds promise for treating a spectrum of diseases, including hypertension, cardiovascular ailments, and renal disorders. Through AI-driven approaches researchers in the field of drug discovery are able to effectively identify a majority of promising drug candidates, expediting the lead optimization process while reducing costs. This paradigm shift not only accelerates therapeutic development but also minimizes the need for exhaustive in vitro or in vivo testing, thus enhancing the efficiency of drug discovery endeavours.
药物发现过程错综复杂，涵盖了一系列详细的研究、开发和测试阶段，旨在评估潜在治疗药物的安全性和有效性。人工智能已成为该领域的变革性工具，擅长分析海量数据集，以揭示人类难以察觉的复杂模式和关系。本研究介绍了一种生物活性预测应用程序，该程序采用定量构效关系模型来预测针对血管紧张素II受体的生物活性，血管紧张素II受体是高血压治疗中的主要药物靶点。血管紧张素II受体调节有望治疗一系列疾病，包括高血压、心血管疾病和肾脏疾病。通过人工智能驱动的方法，药物发现领域的研究人员能够有效地识别出大多数有前景的候选药物，加快先导化合物优化过程，同时降低成本。这种范式转变不仅加速了治疗药物的开发，还最大限度地减少了进行详尽的体外或体内测试的需求，从而提高了药物发现工作的效率。
