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| Status |
Public on Mar 31, 2023 |
| Title |
A Deep Learning based Efficacy Prediction System for Drug Discovery [Chikusetsusaponin IV] |
| Organism |
Mus musculus |
| Experiment type |
Expression profiling by high throughput sequencing
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| Summary |
Target protein-based drug and research compound discovery has been undeniably successful strategy in life science research, yet many diseases and biological processes lack obvious targets to enable these approaches. Here, to overcome this major challenge we have developed a deep-learning based efficacy prediction system (DLEPS) to identify potent agents to treat diverse diseases; DLEPS was trained using L1000 project chemical induced “changes of transcriptional profiles” (CTP) data as input. Strikingly, we found that the CTPs for previously unexamined molecules were precisely predicted (0.74 Pearson correlation coefficient). We used DLEPS to examine 4 disorders, and experimentally validated that perillen, chikusetsusaponin IV, trametinib, and liquiritin confer disease-relevant impacts against obesity, hyperuricemia, NASH, and COVID-19, respectively. Importantly, DLEPS also uncovered the biological insight that the MEK-ERK signaling pathway should be understood as a target for developing anti-NASH agents. Beyond illustrating that DLEPS is an effective tool for drug repurposing and development with diverse diseases (including those lacking targets), our study shows how diverse transcriptomics datasets can be harnessed to identify inhibitors and activator chemicals that can expand the scope of biological investigations well beyond mutant-bases analyses.
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| Overall design |
mRNA profiles of subcutaneous fatty tissue after 35 days treatment at 20mg/kg of Chikusetsusaponin IV or vehicle in HFD mice.
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| Contributor(s) |
Wang J, Zhu J, Guo B, Liu J, Zheng R, Xie Z |
| Citation(s) |
34140681 |
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| Submission date |
Jan 20, 2021 |
| Last update date |
Mar 31, 2023 |
| Contact name |
zhu jie |
| E-mail(s) |
1811210064@bjmu.edu.cn
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| Phone |
18940822582
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| Organization name |
北京大学医学部
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| Street address |
北京市海淀区学院路38号
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| City |
北京 |
| ZIP/Postal code |
100083 |
| Country |
China |
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| Platforms (1) |
| GPL13112 |
Illumina HiSeq 2000 (Mus musculus) |
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| Samples (9)
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| This SubSeries is part of SuperSeries: |
| GSE165175 |
A Deep Learning based Efficacy Prediction System for Drug Discovery |
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| Relations |
| BioProject |
PRJNA693494 |
| SRA |
SRP302542 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSE165171_gene.count.matrix.txt.gz |
674.3 Kb |
(ftp)(http) |
TXT |
| GSE165171_gene.tpm.matrix.txt.gz |
636.6 Kb |
(ftp)(http) |
TXT |
| GSE165171_transcript.count.matrix.txt.gz |
2.2 Mb |
(ftp)(http) |
TXT |
| GSE165171_transcript.tpm.matrix.txt.gz |
1.8 Mb |
(ftp)(http) |
TXT |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data are available on Series record |
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