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Drug Discovery: Overview

Drug Discovery: Overview

Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...

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Updated: Sep 12, 2025

High-throughput Screening for Broad-spectrum Chemical Inhibitors of RNA Viruses

High-throughput Screening for Broad-spectrum Chemical Inhibitors of RNA Viruses

Published on: May 5, 2014

一个基于结构的计算管道,用于广泛的抗病毒发现.

Maria A Castellanos¹, Alexander M Payne^1,2, Jenke Scheen³

¹Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

bioRxiv : the preprint server for biology

|August 6, 2025

概括

此摘要是机器生成的。

开发广泛的抗病毒药物对于疫情准备至关重要. 一个新的计算管道快速识别和评估对多种病毒的抑制剂,加速新兴威胁的药物发现.

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Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

相关实验视频

Last Updated: Sep 12, 2025

High-throughput Screening for Broad-spectrum Chemical Inhibitors of RNA Viruses

High-throughput Screening for Broad-spectrum Chemical Inhibitors of RNA Viruses

Published on: May 5, 2014

Assays for the Identification of Novel Antivirals against Bluetongue Virus

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Published on: October 11, 2013

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

科学领域:

计算病毒学计算病毒学.
药物发现药物发现
传染病传染病传染病

背景情况:

具有流行病潜力的新兴病毒需要快速的抗病毒开发.
传统的药物发现管道很长,突出了加速战略的需要.
针对多个病毒家族和变体的宽谱抗病毒药物是必不可少的.

研究的目的:

介绍一个基于结构的计算管道,用于识别和评估广谱抗病毒抑制剂.
为了支持光谱宽度评估和优先级在优化.
加速对当前和新兴病毒的抗病毒疗法的发现.

主要方法:

与目标构造相关的病毒序列的自动搜索.
使用可用的结构数据进行位预测.
评分蛋白质 - 配体复合体以估计使用药物造包的抗病毒活性范围.

主要成果:

对SARS-CoV-2和MERS-CoV主要蛋白酶 (Mpro) 的回顾性评估显示了结合亲缘关系的预测能力.
对SARS-CoV-2 Mpro 抑制剂针对各种冠状病毒的查表明了广泛频谱活性评估的潜力.
管道成功识别和评估了广谱抑制剂.

结论:

开发的计算管道加速了广泛的抗病毒疗法的识别.
这种方法有助于全球为未来的病毒爆发做好准备.
基于结构的药物设计是快速抗病毒发现的可行策略.