Mansouri Kamel, Moreira-Filho José T, Lowe Charles N, Charest Nathaniel, Martin Todd, Tkachenko Valery, Judson Richard, Conway Mike, Kleinstreuer Nicole C, Williams Antony J
National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, Research Triangle Park, NC, 27709, USA.
Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, USA.
J Cheminform. 2024 Feb 20;16(1):19. doi: 10.1186/s13321-024-00814-3.
The rapid increase of publicly available chemical structures and associated experimental data presents a valuable opportunity to build robust QSAR models for applications in different fields. However, the common concern is the quality of both the chemical structure information and associated experimental data. This is especially true when those data are collected from multiple sources as chemical substance mappings can contain many duplicate structures and molecular inconsistencies. Such issues can impact the resulting molecular descriptors and their mappings to experimental data and, subsequently, the quality of the derived models in terms of accuracy, repeatability, and reliability. Herein we describe the development of an automated workflow to standardize chemical structures according to a set of standard rules and generate two and/or three-dimensional "QSAR-ready" forms prior to the calculation of molecular descriptors. The workflow was designed in the KNIME workflow environment and consists of three high-level steps. First, a structure encoding is read, and then the resulting in-memory representation is cross-referenced with any existing identifiers for consistency. Finally, the structure is standardized using a series of operations including desalting, stripping of stereochemistry (for two-dimensional structures), standardization of tautomers and nitro groups, valence correction, neutralization when possible, and then removal of duplicates. This workflow was initially developed to support collaborative modeling QSAR projects to ensure consistency of the results from the different participants. It was then updated and generalized for other modeling applications. This included modification of the "QSAR-ready" workflow to generate "MS-ready structures" to support the generation of substance mappings and searches for software applications related to non-targeted analysis mass spectrometry. Both QSAR and MS-ready workflows are freely available in KNIME, via standalone versions on GitHub, and as docker container resources for the scientific community. Scientific contribution: This work pioneers an automated workflow in KNIME, systematically standardizing chemical structures to ensure their readiness for QSAR modeling and broader scientific applications. By addressing data quality concerns through desalting, stereochemistry stripping, and normalization, it optimizes molecular descriptors' accuracy and reliability. The freely available resources in KNIME, GitHub, and docker containers democratize access, benefiting collaborative research and advancing diverse modeling endeavors in chemistry and mass spectrometry.
公开可用的化学结构和相关实验数据的快速增长为构建适用于不同领域的稳健定量构效关系(QSAR)模型提供了宝贵机会。然而,普遍关注的是化学结构信息和相关实验数据的质量。当这些数据从多个来源收集时尤其如此,因为化学物质映射可能包含许多重复结构和分子不一致性。此类问题会影响所得的分子描述符及其与实验数据的映射,进而影响衍生模型在准确性、可重复性和可靠性方面的质量。在此,我们描述了一种自动化工作流程的开发,该工作流程可根据一组标准规则对化学结构进行标准化,并在计算分子描述符之前生成二维和/或三维“适用于QSAR”的形式。该工作流程是在KNIME工作流环境中设计的,由三个高级步骤组成。首先,读取结构编码,然后将所得的内存表示与任何现有的标识符进行交叉引用以确保一致性。最后,使用一系列操作对结构进行标准化,包括脱盐、去除立体化学信息(对于二维结构)、互变异构体和硝基的标准化、价态校正、尽可能进行中和,然后去除重复项。此工作流程最初是为支持合作建模QSAR项目而开发的,以确保不同参与者的结果具有一致性。然后对其进行更新并推广用于其他建模应用。这包括对“适用于QSAR”的工作流程进行修改,以生成“适用于质谱(MS)”的结构,以支持物质映射的生成以及与非靶向分析质谱相关的软件应用的搜索。QSAR和适用于MS的工作流程均可通过GitHub上的独立版本以及作为科学界的Docker容器资源在KNIME中免费获得。科学贡献:这项工作在KNIME中开创了一种自动化工作流程,系统地标准化化学结构以确保其适用于QSAR建模和更广泛的科学应用。通过脱盐、去除立体化学信息和归一化来解决数据质量问题,它优化了分子描述符的准确性和可靠性。KNIME、GitHub和Docker容器中免费提供的资源使获取变得民主化,有利于合作研究并推动化学和质谱领域的各种建模工作。