Understanding and Quantifying Molecular Flexibility: Torsion Angular Bin Strings.

IF 5.6 2区 化学 Q1 CHEMISTRY, MEDICINAL Journal of Chemical Information and Modeling Pub Date : 2024-10-28 Epub Date: 2024-10-10 DOI:10.1021/acs.jcim.4c01513
Jessica Braun, Paul Katzberger, Gregory A Landrum, Sereina Riniker
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Abstract

Molecular flexibility is a commonly used, but not easily quantified term. It is at the core of understanding composition and size of a conformational ensemble and contributes to many molecular properties. For many computational workflows, it is necessary to reduce a conformational ensemble to meaningful representatives, however defining them and guaranteeing the ensemble's completeness is difficult. We introduce the concepts of torsion angular bin strings (TABS) as a discrete vector representation of a conformer's dihedral angles and the number of possible TABS (nTABS) as an estimation for the ensemble size of a molecule, respectively. Here, we show that nTABS corresponds to an upper limit for the size of the conformational space of small molecules and compare the classification of conformer ensembles by TABS with classifications by RMSD. Overcoming known drawbacks like the molecular size dependency and threshold picking of the RMSD measure, TABS is shown to meaningfully discretize the conformational space and hence allows e.g. for fast checks of the coverage of the conformational space. The current proof-of-concept implementation is based on the ETKDGv3 conformer generator as implemented in the RDKit and known torsion preferences extracted from small-molecule crystallographic data.

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理解和量化分子柔性:扭转角斌串。
分子柔性是一个常用但不易量化的术语。它是理解构象集合的组成和大小的核心,并对许多分子特性有贡献。对于许多计算工作流程来说,有必要将构象集合还原为有意义的代表,然而定义这些代表并保证集合的完整性却很困难。我们引入了扭转角 bin 字符串(TABS)的概念,作为构象二面角的离散矢量表示,以及可能的 TABS 数量(nTABS)的概念,分别作为分子构象集合大小的估算。在此,我们证明 nTABS 相当于小分子构象空间大小的上限,并比较了 TABS 与 RMSD 对构象集合的分类。研究表明,TABS 克服了 RMSD 测量的分子大小依赖性和阈值选择等已知缺点,对构象空间进行了有意义的离散化,因此可以快速检查构象空间的覆盖范围等。目前的概念验证实现基于 RDKit 中实现的 ETKDGv3 构象生成器和从小分子晶体学数据中提取的已知扭转偏好。
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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.
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