One of the most challenging tasks in the drug-discovery process is the efficient identification of small molecules with desired phenotypes. In this study, we propose a novel computational method for omics-based de novo drug design, which we call TRIOMPHE (transcriptome-based inference and generation of molecules with desired phenotypes). We investigated the correlation between chemically induced transcriptome profiles (reflecting cellular responses to compound treatment) and genetically perturbed transcriptome profiles (reflecting cellular responses to gene knock-down or gene overexpression of target proteins) in terms of ligand–target interactions. Subsequently, we developed novel machine learning methods to generate the chemical structures of new molecules with desired transcriptome profiles in the framework of a variational autoencoder. The use of desired transcriptome profiles enables the automatic design of molecules that are likely to have bioactivities for target proteins of interest. We showed that our methods can generate chemically valid molecules that are likely to have biological activities on 10 target proteins; moreover, they can outperform previous methods that had the same objective. Our omics-based structure generator is expected to be useful for the de novo design of drugs for a variety of target proteins.

中文翻译：

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TRIOMPHE：通过机器学习基于转录组的推断和生成具有所需表型的分子

药物发现过程中最具挑战性的任务之一是有效识别具有所需表型的小分子。在这项研究中，我们提出了一种新的基于组学的从头计算方法药物设计，我们称之为 TRIOMPHE（基于转录组的推断和具有所需表型的分子的生成）。我们研究了化学诱导的转录组谱（反映细胞对化合物处理的反应）和基因扰动的转录组谱（反映细胞对基因敲低或靶蛋白基因过表达的反应）在配体-靶标相互作用方面的相关性。随后，我们开发了新的机器学习方法，以在变分自编码器的框架内生成具有所需转录组谱的新分子的化学结构。使用所需的转录组图谱可以自动设计可能对目标蛋白具有生物活性的分子。我们展示了我们的方法可以生成化学上有效的分子，这些分子可能对 10 种目标蛋白具有生物活性；此外，它们可以超越具有相同目标的先前方法。我们基于组学的结构生成器有望用于各种靶蛋白药物的从头设计。