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相关概念视频

Drug Discovery: Overview01:26

Drug Discovery: Overview

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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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Conserved Binding Sites01:49

Conserved Binding Sites

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
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Ligand Binding Sites02:40

Ligand Binding Sites

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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
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Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
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相关实验视频

Updated: Jun 27, 2025

Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source
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简化基于碎片的计算药物发现,通过基于基的虚拟预选进行进化优化.

Rohan Chandraghatgi1, Hai-Feng Ji2, Gail L Rosen3

  • 1Department of Biology, Drexel University, Philadelphia, Pennsylvania 19104, United States.

Journal of chemical information and modeling
|May 2, 2024
PubMed
概括
此摘要是机器生成的。

这项研究通过使用两阶段方法优化配体合成来增强计算药物发现. 它有效地识别各种疾病的高亲和度候选药物,加速治疗开发.

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Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery
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相关实验视频

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Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery
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科学领域:

  • 计算化学的计算化学
  • 药物发现 药物发现 药物发现
  • 生物信息学是一种生物信息学.

背景情况:

  • 计算方法,包括机器学习,对于药物发现至关重要,但面临着巨大的化学空间的挑战.
  • 现有的基于碎片的药物发现 (FBDD) 方法可以进一步优化效率.

研究的目的:

  • 引入用于计算性连接体合成的两阶段优化方法,基于选连接体药物发现 (FDSL-DD) 方法的碎片数据库.
  • 通过专注于有前途的化学地区,提高识别高亲和度候选药物的效率和精度.

主要方法:

  • 使用in silico选来识别和分割连接体,根据预测的结合亲和力附加属性.
  • 实施两阶段的优化:用于碎片组装的遗传算法,随后进行代改进以提高生物活性.
  • 在不同的目标上展示方法:人类固体癌症,细菌抗菌素耐药性和SARS-CoV-2.

主要成果:

  • 拟议的FDSL-DD采用双阶段优化,比目前最先进的计算FBDD方法更高效地产生高亲和度连接体候选物.
  • 结合药物相似性的多目标优化方法也产生了潜在的高亲和度连接体.
  • 综合方法显著优化了最初的药物发现过程.

结论:

  • 将详细的化学信息与有限的搜索框架相结合,显著优化了早期药物发现.
  • 开发的方法为开发新疗法提供了更精确,更有效的途径.
  • 这种方法加速了对关键疾病的候选药物的鉴定.