ESSENCE-Dock: A Consensus-Based Approach to Enhance Virtual Screening Enrichment in Drug Discovery

被引:2
|
作者
Nelen, Jochem [1 ,2 ]
Carmena-Bargueno, Miguel [1 ,2 ]
Martinez-Cortes, Carlos [1 ]
Rodriguez-Martinez, Alejandro [1 ,2 ]
Villalgordo-Soto, Jose Manuel [3 ]
Perez-Sanchez, Horacio [1 ]
机构
[1] UCAM Univ Catolica Murcia, Struct Bioinformat & High Performance Comp Res Grp, HiTech Innovat Hub, Murcia 30107, Spain
[2] Univ Catolica Murcia UCAM, Hlth Sci PhD Program, Murcia 30107, Spain
[3] Eurofins Villapharma, Parque Tecnol Fuente Alamo, E-30820 Murcia, Spain
关键词
LEAD FINDER; ACCURACY;
D O I
10.1021/acs.jcim.3c01982
中图分类号
R914 [药物化学];
学科分类号
100701 ;
摘要
Drug development is a complex, costly, and time-consuming endeavor. While high-throughput screening (HTS) plays a critical role in the discovery stage, it is one of many factors contributing to these challenges. In certain contexts, virtual screening can complement the HTS, potentially offering a more streamlined approach in the initial stages of drug discovery. Molecular docking is an example of a popular virtual screening technique that is often used for this purpose; however, its effectiveness can vary greatly. This has led to the use of consensus docking approaches that combine results from different docking methods to improve the identification of active compounds and reduce the occurrence of false positives. However, many of these methods do not fully leverage the latest advancements in molecular docking. In response, we present ESSENCE-Dock (Effective Structural Screening ENrichment ConsEnsus Dock), a new consensus docking workflow aimed at decreasing false positives and increasing the discovery of active compounds. By utilizing a combination of novel docking algorithms, we improve the selection process for potential active compounds. ESSENCE-Dock has been made to be user-friendly, requiring only a few simple commands to perform a complete screening while also being designed for use in high-performance computing (HPC) environments.
引用
收藏
页码:1605 / 1614
页数:10
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