结构生物信息学用于合理药物设计

IF 3.4 3区 医学 Q2 HEMATOLOGY
Soroush Mozaffari , Agnethe Moen , Che Yee Ng , Gerry A.F. Nicolaes , Kanin Wichapong
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引用次数: 0

摘要

2024年,在国际血栓与止血学会(ISTH)大会上发表了题为“用于合理药物设计的结构生物信息学技术:从计算机到体内”的最新讲座。药物研发仍然是一项资源密集且复杂的工作,将一种新的治疗药物推向市场通常需要10多年的时间,耗资数十亿美元。然而,最近生物信息学和化学信息学的进步改变了药物发现的前景。关键技术,包括基于结构和配体的虚拟筛选、分子动力学模拟和人工智能驱动的模型,使研究人员能够探索广阔的化学空间,研究分子相互作用,预测结合亲和力,并以前所未有的准确性和效率优化候选药物。这些计算方法通过加速确定可行的候选药物和精炼先导化合物来补充实验技术。人工智能模型,与传统的基于物理的模拟一起,现在在预测结合亲和力和毒性等关键特性方面发挥着重要作用,有助于更明智的决策,特别是在药物发现过程的早期。尽管取得了这些进步,但在准确性、可解释性和所需的计算能力方面仍然存在挑战。本综述探讨了计算药物发现的最新技术,研究了最新的方法和技术,它们对药物开发管道的变革性影响,以及克服剩余限制所需的未来方向。最后,我们总结了相关数据,并重点介绍了在ISTH 2024年大会上展示的各种计算方法成功应用于开发新型抑制剂的案例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Structural bioinformatics for rational drug design
A State of the Art lecture titled “structural bioinformatics technologies for rational drug design: from in silico to in vivo” was presented at the International Society on Thrombosis and Haemostasis (ISTH) Congress in 2024. Drug discovery remains a resource-intensive and complex endeavor, which usually takes over a decade and costs billions to bring a new therapeutic agent to market. However, the landscape of drug discovery has been transformed by the recent advancements in bioinformatics and cheminformatics. Key techniques, including structure- and ligand-based virtual screening, molecular dynamics simulations, and artificial intelligence–driven models are allowing researchers to explore vast chemical spaces, investigate molecular interactions, predict binding affinity, and optimize drug candidates with unprecedented accuracy and efficiency. These computational methods complement experimental techniques by accelerating the identification of viable drug candidates and refining lead compounds. Artificial intelligence models, alongside traditional physics-based simulations, now play an important role in predicting key properties such as binding affinity and toxicity, contributing to more informed decision-making, particularly early in the drug discovery process. Despite these advancements, challenges remain in terms of accuracy, interpretability, and the needed computational power. This review explores the state of the art in computational drug discovery, examining the latest methods and technologies, their transformative impact on the drug development pipeline, and the future directions needed to overcome remaining limitations. Finally, we summarize relevant data and highlight cases where various computational approaches were successfully applied to develop novel inhibitors, as presented during the ISTH 2024 Congress.
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来源期刊
CiteScore
5.60
自引率
13.00%
发文量
212
审稿时长
7 weeks
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