Comparing chemical structures to infer protein targets and functions is a common approach, but basing comparisons on chemical similarity alone can be misleading. Here we present a methodology for predicting target protein clusters using deep neural networks. The model is trained on clusters of compounds based on similarities calculated from combined compound-protein and protein-protein interaction data using a network topology approach. We compare several deep learning architectures including both convolutional and recurrent neural networks. The best performing method, the recurrent neural network architecture MolPMoFiT, achieved an F1 score approaching 0.9 on a held-out test set of 8907 compounds. In addition, in-depth analysis on a set of eleven well-studied chemical compounds with known functions showed that predictions were justifiable for all but one of the chemicals. Four of the compounds, similar in their molecular structure but with dissimilarities in their function, revealed advantages of our method compared to using chemical similarity.

中文翻译：

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使用深度学习从化学结构预测蛋白质网络拓扑簇

比较化学结构以推断蛋白质目标和功能是一种常见的方法，但仅基于化学相似性进行比较可能会产生误导。在这里，我们提出了一种使用深度神经网络预测目标蛋白簇的方法。该模型基于使用网络拓扑方法从组合的化合物-蛋白质和蛋白质-蛋白质相互作用数据计算的相似性对化合物簇进行训练。我们比较了几种深度学习架构，包括卷积神经网络和递归神经网络。表现最好的方法是循环神经网络架构 MolPMoFiT，在 8907 种化合物的保留测试集上取得了接近 0.9 的 F1 分数。此外，对一组 11 种经过充分研究的具有已知功能的化合物进行的深入分析表明，除一种化学物质外，其他所有化合物的预测都是合理的。其中四种化合物的分子结构相似，但功能不同，与使用化学相似性相比，我们的方法具有优势。