Deep learning applications for the accurate identification of low-transcriptional activity drugs and their mechanism of actions

• Published in: Pharmacological research 2022-06, Vol.180, p.106225-106225, Article 106225
• Main Authors: Gao, Shengqiao, Han, Lu, Luo, Dan, Xiao, Zhiyong, Liu, Gang, Zhang, Yongxiang, Zhou, Wenxia
• Format: Article
• Language: English
• Subjects: Deep learning; Gene expression profiles; Low transcriptional activity
• ISSN: 1043-6618; 1096-1186
• DOI: 10.1016/j.phrs.2022.106225

Analysis of drug-induced expression profiles facilitated comprehensive understanding of drug properties. However, many compounds exhibit weak transcription responses though they mostly possess definite pharmacological effects. Actually, as a representative example, over 66.4% of 312,438 molecular signatures in the Library of Integrated Cellular Signatures (LINCS) database exhibit low-transcriptional activities (i.e. TAS-low signatures). When computing the association between TAS-low signatures with shared mechanism of actions (MOAs), commonly used algorithms showed inadequate performance with an average area under receiver operating characteristic curve (AUROC) of 0.55, but the computation accuracy of the same task can be improved by our developed tool Genetic profile activity relationship (GPAR) with an average AUROC of 0.68. Up to 36 out of 74 TAS-low MOAs were well trained with AUROC ≥ 0.7 by GPAR, higher than those by other approaches. Further studies showed that GPAR benefited from the size of training samples more significantly than other approaches. Lastly, in biological validation of the MOA prediction for a TAS-low drug Tropisetron, we found an unreported mechanism that Tropisetron can bind to the glucocorticoid receptor. This study indicated that GPAR can serve as an effective approach for the accurate identification of low-transcriptional activity drugs and their MOAs, thus providing a good tool for drug repurposing with both TAS-low and TAS-high signatures. [Display omitted]