基于生物活性构象的QSAR建模中二维和三维描述符的比较。

IF 2.8 4区 医学 Q3 CHEMISTRY, MEDICINAL
Hanoch Senderowitz, Malkeet Singh Bahia, Omer Kaspi, Meir Touitou, Idan Binayev, Seema Dhail, Jacob Spiegel, Netaly Khazanov, Abraham Yosipof
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引用次数: 2

摘要

QSAR模型在许多研究领域得到了广泛而成功的应用。这种模型的成功在很大程度上取决于通常被分类为1D、2D、3D或4D的分子描述符。虽然3D信息可能很重要,例如:为了模拟配体-蛋白质结合,之前对2D和3D描述符的性能进行的比较是不确定的。然而,在这样的比较中,模拟的配体不一定由它们的生物活性构象来表示。考虑到这一点,我们在PDB中挖掘了具有一致活性数据的相同蛋白质的蛋白质配体复合物集。结果,总共461个结构分布在六个系列中,被汇编成一个精心策划的数据集,这是第一个此类数据集,其中每个配体都由其生物活性构象代表。接下来,每个集合用2D、3D和2D + 3D描述符进行表征,并使用k-Nearest Neighbors、Random Forest和Lasso Regression三种机器学习算法进行建模。模型的性能在来自父数据集的外部测试集上随机或以合理的方式进行评估。我们发现,当结合2D和3D描述符时,获得了许多更重要的模型。我们将这些改进归功于2D和3D描述符对不同但互补的分子特性进行编码的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A comparison between 2D and 3D descriptors in QSAR modeling based on bio-active conformations.

A comparison between 2D and 3D descriptors in QSAR modeling based on bio-active conformations.

QSAR models are widely and successfully used in many research areas. The success of such models highly depends on molecular descriptors typically classified as 1D, 2D, 3D, or 4D. While 3D information is likely important, e. g., for modeling ligand-protein binding, previous comparisons between the performances of 2D and 3D descriptors were inconclusive. Yet in such comparisons the modeled ligands were not necessarily represented by their bioactive conformations. With this in mind, we mined the PDB for sets of protein-ligand complexes sharing the same protein for which uniform activity data were reported. The results, totaling 461 structures spread across six series were compiled into a carefully curated, first of its kind dataset in which each ligand is represented by its bioactive conformation. Next, each set was characterized by 2D, 3D and 2D + 3D descriptors and modeled using three machine learning algorithms, namely, k-Nearest Neighbors, Random Forest and Lasso Regression. Models' performances were evaluated on external test sets derived from the parent datasets either randomly or in a rational manner. We found that many more significant models were obtained when combining 2D and 3D descriptors. We attribute these improvements to the ability of 2D and 3D descriptors to code for different, yet complementary molecular properties.

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来源期刊
Molecular Informatics
Molecular Informatics CHEMISTRY, MEDICINAL-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.30
自引率
2.80%
发文量
70
审稿时长
3 months
期刊介绍: Molecular Informatics is a peer-reviewed, international forum for publication of high-quality, interdisciplinary research on all molecular aspects of bio/cheminformatics and computer-assisted molecular design. Molecular Informatics succeeded QSAR & Combinatorial Science in 2010. Molecular Informatics presents methodological innovations that will lead to a deeper understanding of ligand-receptor interactions, macromolecular complexes, molecular networks, design concepts and processes that demonstrate how ideas and design concepts lead to molecules with a desired structure or function, preferably including experimental validation. The journal''s scope includes but is not limited to the fields of drug discovery and chemical biology, protein and nucleic acid engineering and design, the design of nanomolecular structures, strategies for modeling of macromolecular assemblies, molecular networks and systems, pharmaco- and chemogenomics, computer-assisted screening strategies, as well as novel technologies for the de novo design of biologically active molecules. As a unique feature Molecular Informatics publishes so-called "Methods Corner" review-type articles which feature important technological concepts and advances within the scope of the journal.
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