Systematic computational strategies for identifying protein targets and lead discovery

被引:1
|
作者
Kataria, Arti [1 ]
Srivastava, Ankit [2 ]
Singh, Desh Deepak [3 ]
Haque, Shafiul [4 ]
Han, Ihn [5 ]
Yadav, Dharmendra Kumar [6 ]
机构
[1] NIAID, Natl Inst Hlth NIH, Lab Bacteriol, NIH, Hamilton, MT 59840 USA
[2] NIAID, Natl Inst Hlth NIH, Lab Neurol Infect & Immun, NIH, Hamilton, MT 59840 USA
[3] Amity Univ Rajasthan, Amity Inst Biotechnol, Jaipur, India
[4] Jazan Univ, Coll Nursing & Hlth Sci, Res & Sci Studies Unit, Jazan 45142, Saudi Arabia
[5] Kwangwoon Univ, Plasma Biosci Res Ctr, Appl Plasma Med Ctr, Dept Elect & Biol Phys, Seoul 01897, South Korea
[6] Gachon Univ, Coll Pharm, Dept Biol, Hambakmoeiro 191, Incheon 21924, South Korea
来源
RSC MEDICINAL CHEMISTRY | 2024年 / 15卷 / 07期
关键词
X-RAY CRYSTALLOGRAPHY; LIGAND-BINDING SITES; DRUG DISCOVERY; MOLECULAR-DYNAMICS; CONNECTIVITY MAP; L-ASPARAGINASE; EXPRESSION SIGNATURES; STRUCTURE PREDICTION; STRUCTURAL BIOLOGY; SCORING FUNCTION;
D O I
10.1039/d4md00223g
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
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. Computational algorithms and tools have retrenched the drug discovery and development timeline.
引用
收藏
页码:2254 / 2269
页数:16
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