识别蛋白质靶标和发现先导物的系统计算策略

IF 3.597 Q2 Pharmacology, Toxicology and Pharmaceutics
MedChemComm Pub Date : 2024-05-31 DOI:10.1039/D4MD00223G
Arti Kataria, Ankit Srivastava, Desh Deepak Singh, Shafiul Haque, Ihn Han and Dharmendra Kumar Yadav
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引用次数: 0

摘要

计算算法和工具缩短了药物发现和开发的时间。由于生物大分子及其异分子复合物的结构信息急剧增加,计算方法的适用性变得越来越重要。目前,计算方法已广泛应用于确定新的蛋白质靶点、药物可药性评估、药理图谱、分子对接、先导分子的虚拟筛选、生物活性预测、蛋白质配体复合物的分子动力学、亲和力预测以及设计更好的配体。在此,我们将概述最近报道的计算药物发现工作流程的主要组成部分,包括用于蛋白质靶点识别和优化配体选择的算法、工具和数据库。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Systematic computational strategies for identifying protein targets and lead discovery

Systematic computational strategies for identifying protein targets and lead discovery

Systematic computational strategies for identifying protein targets and lead discovery

Computational algorithms and tools have retrenched the drug discovery and development timeline. The applicability of computational approaches has gained immense relevance owing to the dramatic surge in the structural information of biomacromolecules and their heteromolecular complexes. Computational methods are now extensively used in identifying new protein targets, druggability assessment, pharmacophore mapping, molecular docking, the virtual screening of lead molecules, bioactivity prediction, molecular dynamics of protein–ligand complexes, affinity prediction, and for designing better ligands. Herein, we provide an overview of salient components of recently reported computational drug-discovery workflows that includes algorithms, tools, and databases for protein target identification and optimized ligand selection.

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来源期刊
MedChemComm
MedChemComm BIOCHEMISTRY & MOLECULAR BIOLOGY-CHEMISTRY, MEDICINAL
CiteScore
4.70
自引率
0.00%
发文量
0
审稿时长
2.2 months
期刊介绍: Research and review articles in medicinal chemistry and related drug discovery science; the official journal of the European Federation for Medicinal Chemistry. In 2020, MedChemComm will change its name to RSC Medicinal Chemistry. Issue 12, 2019 will be the last issue as MedChemComm.
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