PlasmoDocking: A User-Friendly Open-Source Web Tool for Virtual Screening Targeting Plasmodium falciparum Enzymes

IF 4.8 3区化学 Q2 CHEMISTRY, MULTIDISCIPLINARY

Journal of Computational Chemistry Pub Date : 2025-09-04 DOI:10.1002/jcc.70225

Fernando Loza Guariero, Eduardo Pantoja de Macedo, Elise Bittencourt de Laia, Joseph Albert Medeiros Evaristo, Geisa Paulino Caprini Evaristo, Fernando Berton Zanchi

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引用次数: 0

Abstract

Virtual screening through molecular docking represents a fundamental computational methodology extensively employed in the identification of therapeutic compounds for malaria and other parasitic diseases. Although numerous software platforms are available, including AutodockGPU, the command-line interface requirements present significant barriers to non-specialized users, while multi-target screening protocols introduce additional complexity in receptor preparation procedures. To address these limitations, we developed Plasmodocking, a comprehensive web-based platform designed to automate molecular docking simulations against predefined Plasmodium falciparum targets (https://plasmodocking-unir.ecotechamazonia.com.br/). The platform enables users to submit up to 10 molecular structures (.sdf format) for automated AutodockGPU screening against 38 pre-configured parasite targets, facilitating systematic comparison of binding energies with co-crystallized ligands. Developed using Python and Next.js, Plasmodocking accelerates malaria drug discovery by enabling simultaneous multi-target docking simulations within a single experimental framework. The open-source codebase is available at: https://github.com/LABIOQUIM/PlasmoDocking-Client.

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PlasmoDocking：一个用户友好的开源网络工具，用于针对恶性疟原虫酶的虚拟筛选

通过分子对接进行虚拟筛选是一种广泛用于鉴定疟疾和其他寄生虫病治疗化合物的基本计算方法。尽管有许多软件平台可用，包括AutodockGPU，但命令行界面要求对非专业用户存在重大障碍，而多目标筛选协议在受体制备过程中引入了额外的复杂性。为了解决这些限制，我们开发了Plasmodocking，这是一个基于web的综合平台，旨在针对预定义的恶性疟原虫目标（https://plasmodocking-unir.ecotechamazonia.com.br/）自动进行分子对接模拟。该平台允许用户提交多达10个分子结构(。sdf格式)用于自动AutodockGPU筛选38个预先配置的寄生虫靶标，促进与共结晶配体的结合能的系统比较。Plasmodocking使用Python和Next.js开发，通过在单个实验框架内实现同步多靶点对接模拟，加速了疟疾药物的发现。开源代码库可从https://github.com/LABIOQUIM/PlasmoDocking-Client获得。

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来源期刊

Journal of Computational Chemistry 化学-化学综合

CiteScore

6.60

自引率

3.30%

发文量

247

审稿时长

1.7 months

期刊介绍： This distinguished journal publishes articles concerned with all aspects of computational chemistry: analytical, biological, inorganic, organic, physical, and materials. The Journal of Computational Chemistry presents original research, contemporary developments in theory and methodology, and state-of-the-art applications. Computational areas that are featured in the journal include ab initio and semiempirical quantum mechanics, density functional theory, molecular mechanics, molecular dynamics, statistical mechanics, cheminformatics, biomolecular structure prediction, molecular design, and bioinformatics.