相似的化合物与相似的构象异构体：PubChem 2-D 和 3-D 相邻集之间的互补性。,Journal of Cheminformatics

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相似的化合物与相似的构象异构体：PubChem 2-D 和 3-D 相邻集之间的互补性。
Journal of Cheminformatics ( IF 7.1 ) Pub Date : 2016-11-04 , DOI: 10.1186/s13321-016-0163-1
Sunghwan Kim ₁ , Evan E Bolton ₁ , Stephen H Bryant ₁

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PubChem 是小分子生物活性的公共存储库。为了有效利用其大量化学信息，PubChem 执行 2 维 (2-D) 和 3 维 (3-D) 邻域，使用 PubChem 预先计算 PubChem 化合物数据库中分子之间的“邻居”关系分别是基于子图指纹的 2-D 相似性和基于高斯形状叠加的 3-D 相似性。这些邻域允许 PubChem 为用户提供对 PubChem 中每个化合物的 2-D 和 3-D 邻域（也分别称为“相似化合物”和“相似符合者”）列表的即时访问。然而，由于 3-D 邻接比 2-D 邻接更耗时，因此考虑到有限的计算资源，两种邻接方案的结果有何不同是一个重要问题。本研究分析了 PubChem 2-D 和 3-D 邻居之间的互补性。当考虑 PubChem 中的所有化合物时，2-D 和 3-D 邻居之间的重叠仅占邻居总数的 2%。对于包含带有注释信息的化合物的数据集，重叠随着数据集变小而增加。然而，它没有超过 31%，并且 PubChem 2-D 或 3-D 相似性仍然可以识别大部分邻居，但两者都不能识别。引入了给定数据集的分子的邻居偏好指数（NPI），它量化了数据集中分子是否具有更多的 2-D 或 3-D 邻居。所有 PubChem 化合物的 NPI 直方图均具有双峰形状，其中两个最大值位于 NPI = ±1 处，最小值位于 NPI = 0 处。但是，包含带有注释信息的化合物的子集的 NPI 直方图具有较大比例的具有强烈偏好的化合物对于一种与另一种相邻的方法（NPI = ±1）以及具有中性偏好的化合物（NPI = 0）。我们的研究结果表明，对于 PubChem 中的大多数化合物来说，它们与其他化合物的结构相似性主要可以通过 2-D 或 3-D 邻这化合物来识别，但不能同时通过两者来识别，这表明 2-D 之间存在很强�互补性。 D 和 3-D 相邻结果。因此，尽管 3-D 邻域对计算资源要求很高，但它提供了一种替代方法，用户可以立即访问结构相似的分子，而如果仅使用 2-D 邻域，则无法检测到这些分子。

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Similar compounds versus similar conformers: complementarity between PubChem 2-D and 3-D neighboring sets.

PubChem is a public repository for biological activities of small molecules. For the efficient use of its vast amount of chemical information, PubChem performs 2-dimensional (2-D) and 3-dimensional (3-D) neighborings, which precompute “neighbor” relationships between molecules in the PubChem Compound database, using the PubChem subgraph fingerprints-based 2-D similarity and the Gaussian-shape overlay-based 3-D similarity, respectively. These neighborings allow PubChem to provide the user with immediate access to the list of 2-D and 3-D neighbors (also called “Similar Compounds” and “Similar Conformers”, respectively) for each compound in PubChem. However, because 3-D neighboring is much more time-consuming than 2-D neighboring, how different the results of the two neighboring schemes are is an important question, considering limited computational resources. The present study analyzed the complementarity between the PubChem 2-D and 3-D neighbors. When all compounds in PubChem were considered, the overlap between 2-D and 3-D neighbors was only 2% of the total neighbors. For the data sets containing compounds with annotated information, the overlap increased as the data sets became smaller. However, it did not exceed 31% and substantial fractions of neighbors were still recognized by either PubChem 2-D or 3-D similarity, but not by both. The Neighbor Preference Index (NPI) of a molecule for a given data set was introduced, which quantified whether a molecule had more 2-D or 3-D neighbors in the data set. The NPI histogram for all PubChem compounds had a bimodal shape with two maxima at NPI = ±1 and a minimum at NPI = 0. However, the NPI histograms for the subsets containing compounds with annotated information had a greater fraction of compounds with a strong preference for one neighboring method to the other (at NPI = ±1) as well as compounds with a neutral preference (at NPI = 0). The results of our study indicate that, for the majority of the compounds in PubChem, their structural similarity to other compounds can be recognized predominantly by either 2-D or 3-D neighborings, but not by both, showing a strong complementarity between 2-D and 3-D neighboring results. Therefore, despite its heavy requirements for computational resources, 3-D neighboring provides an alternative way in which the user can instantly access structurally similar molecules that cannot be detected if only 2-D neighboring is used.

更新日期：2016-11-04

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