Shayakhmetov Rim, Kuznetsov Maksim, Zhebrak Alexander, Kadurin Artur, Nikolenko Sergey, Aliper Alexander, Polykovskiy Daniil
Insilico Medicine, Hong Kong, Hong Kong.
Neuromation OU, Tallinn, Estonia.
Front Pharmacol. 2020 Apr 17;11:269. doi: 10.3389/fphar.2020.00269. eCollection 2020.
Gene expression profiles are useful for assessing the efficacy and side effects of drugs. In this paper, we propose a new generative model that infers drug molecules that could induce a desired change in gene expression. Our model-the Bidirectional Adversarial Autoencoder-explicitly separates cellular processes captured in gene expression changes into two feature sets: those and to the drug incubation. The model uses features to produce a drug hypothesis. We have validated our model on the LINCS L1000 dataset by generating molecular structures in the SMILES format for the desired transcriptional response. In the experiments, we have shown that the proposed model can generate novel molecular structures that could induce a given gene expression change or predict a gene expression difference after incubation of a given molecular structure. The code of the model is available at https://github.com/insilicomedicine/BiAAE.
基因表达谱对于评估药物的疗效和副作用很有用。在本文中，我们提出了一种新的生成模型，该模型可以推断出能够诱导基因表达产生预期变化的药物分子。我们的模型——双向对抗自编码器——明确地将基因表达变化中捕获的细胞过程分为两个特征集：那些与药物孵育相关的和不相关的。该模型使用相关特征来生成药物假设。我们通过为所需的转录反应生成SMILES格式的分子结构，在LINCS L1000数据集上验证了我们的模型。在实验中，我们表明所提出的模型可以生成能够诱导给定基因表达变化的新型分子结构，或者预测给定分子结构孵育后的基因表达差异。该模型的代码可在https://github.com/insilicomedicine/BiAAE获取。
