EvaluationMaster: A GUI Tool for Structure-Based Virtual Screening Evaluation Analysis and Decision-Making Support.

IF 5.6 2区 化学 Q1 CHEMISTRY, MEDICINAL
Zheyuan Shen, Roufen Chen, Jian Gao, Xinglong Chi, Qingnan Zhang, Qingyu Bian, Binbin Zhou, Jinxin Che, Haibin Dai, Xiaowu Dong
{"title":"EvaluationMaster: A GUI Tool for Structure-Based Virtual Screening Evaluation Analysis and Decision-Making Support.","authors":"Zheyuan Shen, Roufen Chen, Jian Gao, Xinglong Chi, Qingnan Zhang, Qingyu Bian, Binbin Zhou, Jinxin Che, Haibin Dai, Xiaowu Dong","doi":"10.1021/acs.jcim.4c01818","DOIUrl":null,"url":null,"abstract":"<p><p>Structure-based virtual screening (SBVS) plays an indispensable role in the early phases of drug discovery, utilizing computational docking techniques to predict interactions between molecules and biological targets. During the SBVS process, selecting appropriate target structures and screening algorithms is crucial, as these choices significantly shape the outcomes. Typically, such selections require researchers to be proficient with multiple algorithms and familiar with evaluation and analysis processes, complicating their tasks. These algorithms' lack of graphical user interfaces (GUIs) further complicates it. To address these challenges, we introduced EvaluationMaster, the first GUI tool designed specifically to streamline and standardize the evaluation and decision-making processes in SBVS. It supports four docking algorithms' evaluation under multiple target structures and offers a comprehensive platform that manages the entire workflow─including the downloading of molecules, construction of decoy datasets, prediction of protein pockets, batch docking, and extensive data analysis. By automating complex evaluation tasks and providing clear visualizations of analysis results, EvaluationMaster significantly reduces the learning curve for researchers and boosts the efficiency of evaluations, potentially improving SBVS hit rates and accelerating the discovery and development of new therapeutic agents.</p>","PeriodicalId":44,"journal":{"name":"Journal of Chemical Information and Modeling ","volume":" ","pages":"7-14"},"PeriodicalIF":5.6000,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Chemical Information and Modeling ","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1021/acs.jcim.4c01818","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/12/18 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"CHEMISTRY, MEDICINAL","Score":null,"Total":0}
引用次数: 0

Abstract

Structure-based virtual screening (SBVS) plays an indispensable role in the early phases of drug discovery, utilizing computational docking techniques to predict interactions between molecules and biological targets. During the SBVS process, selecting appropriate target structures and screening algorithms is crucial, as these choices significantly shape the outcomes. Typically, such selections require researchers to be proficient with multiple algorithms and familiar with evaluation and analysis processes, complicating their tasks. These algorithms' lack of graphical user interfaces (GUIs) further complicates it. To address these challenges, we introduced EvaluationMaster, the first GUI tool designed specifically to streamline and standardize the evaluation and decision-making processes in SBVS. It supports four docking algorithms' evaluation under multiple target structures and offers a comprehensive platform that manages the entire workflow─including the downloading of molecules, construction of decoy datasets, prediction of protein pockets, batch docking, and extensive data analysis. By automating complex evaluation tasks and providing clear visualizations of analysis results, EvaluationMaster significantly reduces the learning curve for researchers and boosts the efficiency of evaluations, potentially improving SBVS hit rates and accelerating the discovery and development of new therapeutic agents.

EvaluationMaster:基于结构的虚拟筛选评估分析和决策支持图形用户界面工具。
基于结构的虚拟筛选(SBVS)在药物发现的早期阶段发挥着不可或缺的作用,它利用计算对接技术来预测分子与生物靶点之间的相互作用。在SBVS过程中,选择合适的目标结构和筛选算法至关重要,因为这些选择会显著影响结果。通常,这样的选择需要研究人员精通多种算法,熟悉评估和分析过程,使他们的任务复杂化。这些算法缺乏图形用户界面(gui),这进一步使问题复杂化。为了应对这些挑战,我们引入了EvaluationMaster,这是第一个专门设计用于简化和标准化SBVS中的评估和决策过程的GUI工具。它支持四种对接算法在多个目标结构下的评估,并提供一个全面的平台来管理整个工作流程,包括分子下载、诱饵数据集的构建、蛋白质口袋的预测、批量对接和广泛的数据分析。通过自动化复杂的评估任务并提供清晰的分析结果可视化,EvaluationMaster显著缩短了研究人员的学习曲线,提高了评估效率,潜在地提高了SBVS的命中率,加速了新治疗剂的发现和开发。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
9.80
自引率
10.70%
发文量
529
审稿时长
1.4 months
期刊介绍: The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery. Astute chemists, computer scientists, and information specialists look to this monthly’s insightful research studies, programming innovations, and software reviews to keep current with advances in this integral, multidisciplinary field. As a subscriber you’ll stay abreast of database search systems, use of graph theory in chemical problems, substructure search systems, pattern recognition and clustering, analysis of chemical and physical data, molecular modeling, graphics and natural language interfaces, bibliometric and citation analysis, and synthesis design and reactions databases.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信