MOTIVATION Traditional drug discovery approaches identify a target for a disease and find a compound that binds to the target. In this approach, structures of compounds are considered as the most important features because it is assumed that similar structures will bind to the same target. Therefore, structural analogs of the drugs that bind to the target are selected as drug candidates. However, even though compounds are not structural analogs, they may achieve the desired response. A new drug discovery method based on drug response, which can complement the structure-based methods, is needed. RESULTS We implemented Siamese neural networks called ReSimNet that take as input two chemical compounds and predicts the CMap score of the two compounds, which we use to measure the transcriptional response similarity of the two compounds. ReSimNet learns the embedding vector of a chemical compound in a transcriptional response space. ReSimNet is trained to minimize the difference between the cosine similarity of the embedding vectors of the two compounds and the CMap score of the two compounds. ReSimNet can find pairs of compounds that are similar in response even though they may have dissimilar structures. In our quantitative evaluation, ReSimNet outperformed the baseline machine learning models. The ReSimNet ensemble model achieves a Pearson correlation of 0.518 and a precision@1% of 0.989. In addition, in the qualitative analysis, we tested ReSimNet on the ZINC15 database and showed that ReSimNet successfully identifies chemical compounds that are relevant to a prototype drug whose mechanism of action is known. AVAILABILITY AND IMPLEMENTATION The source code and the pre-trained weights of ReSimNet are available at https://github.com/dmis-lab/ReSimNet. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

中文翻译：

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ReSimNet：使用暹罗神经网络的药物反应相似性预测。

动机传统的药物发现方法可确定疾病的靶标并找到与该靶标结合的化合物。在这种方法中，化合物的结构被认为是最重要的特征，因为假定相似的结构将与相同的靶标结合。因此，选择与靶标结合的药物的结构类似物作为候选药物。但是，即使化合物不是结构类似物，它们也可以实现所需的响应。需要一种新的基于药物反应的药物发现方法，该方法可以补充基于结构的方法。结果我们实现了称为ReSimNet的暹罗神经网络，该网络以两种化学化合物为输入并预测两种化合物的CMap得分，我们用它们来测量两种化合物的转录反应相似性。ReSimNet可以在转录响应空间中了解化合物的嵌入载体。对ReSimNet进行了培训，以使两种化合物的嵌入向量的余弦相似度与两种化合物的CMap分数之间的差异最小。ReSimNet可以找到响应相似的化合物对，即使它们可能具有不同的结构。在我们的定量评估中，ReSimNet优于基准机器学习模型。ReSimNet集成模型实现了0.518的皮尔逊相关性和0.989的1％精度。此外，在定性分析中，我们在ZINC15数据库上对ReSimNet进行了测试，结果表明ReSimNet成功地鉴定了与作用机理已知的原型药物相关的化合物。可用性和实现ReSimNet的源代码和预先训练的权重可从https://github.com/dmis-lab/ReSimNet获得。补充信息补充数据可从Bioinformatics在线获得。