Background: Virtual screening (VS) is essential for analyzing potential drug candidates in drug discovery. Often, this involves the conversion of large volumes of compound data into specific formats suitable for computational analysis. Managing and processing this wealth of information, especially when dealing with vast numbers of compounds in various forms, such as names, identifiers, or SMILES strings, can present significant logistical and technical challenges. Methods: To streamline this process, we developed PyComp, a software tool using Python's PyQt5 library, and compiled it into an executable with Pyinstaller. PyComp provides a systematic way for users to retrieve and convert a list of compound names, IDs (even in a range), or SMILES strings into the desired 3D format. Results: PyComp greatly enhances the efficiency of data extraction, conversion, and storage processes involved in VS. It searches for similar compounds coupled with its ability to handle misidentified compounds and offers users an easy-to-use, customizable tool for managing largescale compound data. By streamlining these operations, PyComp allows researchers to save significant time and effort, thus accelerating the pace of drug discovery research. Conclusion: PyComp effectively addresses some of the most pressing challenges in highthroughput VS: efficient management and conversion of large volumes of compound data. As a user-friendly, customizable software tool, PyComp is pivotal in improving the efficiency and success of large-scale drug screening efforts, paving the way for faster discovery of potential therapeutic compounds.

中文翻译：

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PyComp：高通量虚拟药物筛选中高效数据提取、转换和管理的多功能工具

背景：虚拟筛选（VS）对于分析药物发现中的潜在候选药物至关重要。通常，这涉及将大量复合数据转换为适合计算分析的特定格式。管理和处理如此丰富的信息，尤其是在处理大量各种形式的化合物（例如名称、标识符或 SMILES 字符串）时，可能会带来重大的后勤和技术挑战。方法：为了简化这个过程，我们使用Python的PyQt5库开发了PyComp，一个软件工具，并使用Pyinstaller将其编译成可执行文件。PyComp 为用户提供了一种系统方法来检索复合名称、ID（甚至在某个范围内）或 SMILES 字符串的列表并将其转换为所需的 3D 格式。结果：PyComp 大大提高了 VS 涉及的数据提取、转换和存储过程的效率。它可以搜索相似的化合物，并能够处理错误识别的化合物，并为用户提供易于使用、可定制的工具来管理大规模化合物数据。通过简化这些操作，PyComp 使研究人员能够节省大量时间和精力，从而加快药物发现研究的步伐。结论：PyComp 有效解决了高通量 VS 中一些最紧迫的挑战：大量复合数据的高效管理和转换。作为一种用户友好、可定制的软件工具，PyComp 对于提高大规模药物筛选工作的效率和成功率至关重要，为更快地发现潜在治疗化合物铺平道路。