Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data.
Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data.
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DOI：
10.1021/acs.molpharmaceut.6b00248
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发表时间：
2016-07-05
影响因子：
4.9
通讯作者：
Zhavoronkov A
中科院分区：
文献类型：
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作者：
Aliper A;Plis S;Artemov A;Ulloa A;Mamoshina P;Zhavoronkov A
深度学习正在迅速推动许多科学技术领域的发展，在图像、文本、语音和视频识别、机器人和自动驾驶等领域拥有多个成功案例。在本文中，我们演示了在大型转录反应数据集上训练的深度神经网络 (DNN) 如何仅根据各种药物的转录谱将其分类为治疗类别。我们使用了来自 LINCS 项目的 A549、MCF-7 和 PC-3 细胞系中 678 种药物的扰动样本，并将这些样本与源自 MeSH 的 12 个治疗用途类别联系起来。为了训练 DNN，我们利用了基因级转录组数据和使用通路激活评分算法处理的转录组数据，以获得用不同浓度药物扰动 6 小时和 24 小时的样本汇总数据集。在基因和通路级别分类中，DNN 在每个多类分类问题上都令人信服地优于支持向量机 (SVM) 模型，然而，基于通路级别分类的模型表现更好。我们首次展示了基于转录组数据训练的深度学习神经网络，以识别不同生物系统和条件下多种药物的药理学特性。我们还建议使用深度神经网络混淆矩阵进行药物重新定位。这项工作是将深度学习应用于药物发现和开发的原理证明。
Deep learning is rapidly advancing many areas of science and technology with multiple success stories in image, text, voice and video recognition, robotics and autonomous driving. In this paper we demonstrate how deep neural networks (DNN) trained on large transcriptional response data sets can classify various drugs to therapeutic categories solely based on their transcriptional profiles. We used the perturbation samples of 678 drugs across A549, MCF‐7 and PC‐3 cell lines from the LINCS project and linked those to 12 therapeutic use categories derived from MeSH. To train the DNN, we utilized both gene level transcriptomic data and transcriptomic data processed using a pathway activation scoring algorithm, for a pooled dataset of samples perturbed with different concentrations of the drug for 6 and 24 hours. In both gene and pathway level classification, DNN convincingly outperformed support vector machine (SVM) model on every multiclass classification problem, however, models based on a pathway level classification perform better. For the first time we demonstrate a deep learning neural net trained on transcriptomic data to recognize pharmacological properties of multiple drugs across different biological systems and conditions. We also propose using deep neural net confusion matrices for drug repositioning. This work is a proof of principle for applying deep learning to drug discovery and development.