De novo generation of hit-like molecules from gene expression signatures using artificial intelligence
De novo generation of hit-like molecules from gene expression signatures using artificial intelligence
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DOI：
10.1038/s41467-019-13807-w
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发表时间：
2020-01-03
影响因子：
16.6
通讯作者：
Wichard, Joerg
中科院分区：
文献类型：
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作者：
Mendez-Lucio, Oscar;Baillif, Benoit;Wichard, Joerg
寻找具有所需生物活性的新分子是一项极其困难的任务。在这种背景下，人工智能和生成模型已用于分子从头设计和化合物优化。在这里，我们报告了一个生成模型，它连接了系统生物学和分子设计，用转录组数据调节生成对抗网络。通过这样做，我们可以自动设计具有高概率诱导所需转录组谱的分子。只要提供了所需状态的基因表达特征，该模型就能够为所需靶标设计活性样分子，而无需训练化合物的任何先前靶标注释。通过该模型设计的分子比通过基因表达特征的相似性鉴定的分子更类似于活性化合物。总的来说，这种方法代表了在漫长而艰难的药物发现道路上连接化学和生物学的另一种方法。
Finding new molecules with a desired biological activity is an extremely difficult task. In this context, artificial intelligence and generative models have been used for molecular de novo design and compound optimization. Herein, we report a generative model that bridges systems biology and molecular design, conditioning a generative adversarial network with transcriptomic data. By doing so, we can automatically design molecules that have a high probability to induce a desired transcriptomic profile. As long as the gene expression signature of the desired state is provided, this model is able to design active-like molecules for desired targets without any previous target annotation of the training compounds. Molecules designed by this model are more similar to active compounds than the ones identified by similarity of gene expression signatures. Overall, this method represents an alternative approach to bridge chemistry and biology in the long and difficult road of drug discovery.