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Deep generative model for therapeutic targets using transcriptomic disease-associated data-USP7 case study
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DOI：10.1093/bib/bbac270.png)
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The generation of candidate hit molecules with the potential to be used in cancer treatment is a challenging task. In this context, computational methods based on deep learning have been employed to improve in silico drug design methodologies. Nonetheless, the applied strategies have focused solely on the chemical aspect of the generation of compounds, disregarding the likely biological consequences for the organism's dynamics. Herein, we propose a method to implement targeted molecular generation that employs biological information, namely, disease-associated gene expression data, to conduct the process of identifying interesting hits. When applied to the generation of USP7 putative inhibitors, the framework managed to generate promising compounds, with more than 90% of them containing drug-like properties and essential active groups for the interaction with the target. Hence, this work provides a novel and reliable method for generating new promising compounds focused on the biological context of the disease.
Keyword:
drug design
deep learning
cancer
transcriptome
期刊
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7.7
论文数:
5.8K
被引数:
2.7W
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OAAI分享收藏Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules使用数据驱动的分子连续表示的自动化学设计.png)
ACS CENTRAL SCIENCEOAAI
ACS CENTRAL SCIENCEIF10.4
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