Comparison of Chemical Structure and Cell Morphology Information for Multitask Bioactivity Predictions.
Comparison of Chemical Structure and Cell Morphology Information for Multitask Bioactivity Predictions.
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多任务生物活性预测的化学结构和细胞形态信息比较。
DOI：
10.1021/acs.jcim.0c00864
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发表时间：
2021
影响因子：
5.6
通讯作者：
Trapotsi MA
中科院分区：
文献类型：
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作者：
Trapotsi MA
对化合物作用机制（MoA）的理解和潜在药物靶点的预测在小分子药物发现中发挥着重要作用。这项工作的目的是比较化学和细胞形态信息以进行生物活性预测。使用来自 ExCAPE 数据库的生物活性数据、来自 Cell Painting 数据集（最大的公开可用的细胞图像数据集，具有约 30,000 个复合扰动）的图像数据（以 CellProfiler 特征的形式）以及使用多任务贝叶斯矩阵分解（BMF）方法的扩展连接指纹（ECFP）进行比较。我们发现，当使用 ECFP 作为复合描述符时，BMF 澳门和随机森林 (RF) 的性能总体相似。然而，当使用图像数据作为复合信息时，BMF 澳门在 224 个目标中的 159 个（71%）中表现优于 RF。使用 BMF 澳门，分别以 ECFP 数据和图像数据作为辅助信息，对 224 个目标中的 100 个（相当于约 45%）和 90 个（约 40%）进行了高预测性能（AUC > 0.8）的预测。有些目标可以通过图像数据作为辅助信息更好地预测，例如 β-连环蛋白，而其他目标可以通过基于指纹的辅助信息更好地预测，例如属于 G 蛋白偶联受体 1 家族的蛋白质，可以从每个描述符域中的基础数据分布中合理化这些目标。总之，细胞形态变化和化学结构信息都包含有关化合物生物活性的信息，这也是部分互补的，因此可以有助于硅MoA分析。
The understanding of the mechanism-of-action (MoA) of compounds and the prediction of potential drug targets play an important role in small-molecule drug discovery. The aim of this work was to compare chemical and cell morphology information for bioactivity prediction. The comparison was performed using bioactivity data from the ExCAPE database, image data (in the form of CellProfiler features) from the Cell Painting data set (the largest publicly available data set of cell images with ∼30,000 compound perturbations), and extended connectivity fingerprints (ECFPs) using the multitask Bayesian matrix factorization (BMF) approach Macau. We found that the BMF Macau and random forest (RF) performance were overall similar when ECFPs were used as compound descriptors. However, BMF Macau outperformed RF in 159 out of 224 targets (71%) when image data were used as compound information. Using BMF Macau, 100 (corresponding to about 45%) and 90 (about 40%) of the 224 targets were predicted with high predictive performance (AUC > 0.8) with ECFP data and image data as side information, respectively. There were targets better predicted by image data as side information, such as β-catenin, and others better predicted by fingerprint-based side information, such as proteins belonging to the G-protein-Coupled Receptor 1 family, which could be rationalized from the underlying data distributions in each descriptor domain. In conclusion, both cell morphology changes and chemical structure information contain information about compound bioactivity, which is also partially complementary, and can hence contribute toin silicoMoA analysis.