MolBar:无机和有机分子的分子识别器,完全支持立体异构†。

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY
Nils van Staalduinen and Christoph Bannwarth
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引用次数: 0

摘要

在化学结构数据库注册新分子结构之前,必须进行重复检查以确保数据库的完整性。简化分子输入行输入规范(SMILES)和国际理论化学和应用化学联合会(IUPAC)国际化学标识符(InChI)是广泛用于这些检查的分子标识符。在处理无机化学分子或非中心立体化学结构时,会出现明显的局限性。当立体构型需要分配给一组原子时,由于分子是以原子为中心描述的,因此广泛使用的标识符无法描述轴向和平面手性。为了解决这一局限性,我们引入了一种名为分子条形码(MolBar)的新型化学标识符。受理论化学领域的启发,除了传统的原子描述外,我们还采用了基于片段的方法。在这种方法中,片段的三维结构使用专门的力场进行归一化,并通过仅从原子位置得到的物理启发矩阵进行表征。由此产生的包换不变表示由特征值光谱构建而成,可提供有关键合和立体化学的全面信息。通过对包含 390 万个分子的 Molecule3D 数据集进行重复和包换不变性测试,证明了 MolBar 的稳健性。MolBar 的 Python 实现是开源的,可以通过 pip install molbar 安装。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

MolBar: a molecular identifier for inorganic and organic molecules with full support of stereoisomerism†

MolBar: a molecular identifier for inorganic and organic molecules with full support of stereoisomerism†

Before a new molecular structure is registered to a chemical structure database, a duplicate check is essential to ensure the integrity of the database. The Simplified Molecular Input Line Entry Specification (SMILES) and the IUPAC International Chemical Identifier (InChI) stand out as widely used molecular identifiers for these checks. Notable limitations arise when dealing with molecules from inorganic chemistry or structures characterized by non-central stereochemistry. When the stereoinformation needs to be assigned to a group of atoms, widely used identifiers cannot describe axial and planar chirality due to the atom-centered description of a molecule. To address this limitation, we introduce a novel chemical identifier called the Molecular Barcode (MolBar). Motivated by the field of theoretical chemistry, a fragment-based approach is used in addition to the conventional atomistic description. In this approach, the 3D structure of fragments is normalized using a specialized force field and characterized by physically inspired matrices derived solely from atomic positions. The resulting permutation-invariant representation is constructed from the eigenvalue spectra, providing comprehensive information on both bonding and stereochemistry. The robustness of MolBar is demonstrated through duplication and permutation invariance tests on the Molecule3D dataset of 3.9 million molecules. A Python implementation is available as open source and can be installed via pip install molbar.

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CiteScore
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