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Repurposing High-Throughput Image Assays Enables Biological Activity Prediction for Drug Discovery
| Content Provider | Scilit |
|---|---|
| Author | Simm, Jaak Klambauer, Günter Arany, Adam Steijaert, Marvin Wegner, Jörg Kurt Gustin, Emmanuel Chupakhin, Vladimir Chong, Yolanda T. Vialard, Jorge Buijnsters, Peter Velter, Ingrid Vapirev, Alexander Singh, Shantanu Carpenter, Anne E. Wuyts, Roel Hochreiter, Sepp Moreau, Yves Ceulemans, Hugo |
| Copyright Year | 2018 |
| Description | Journal: Cell Chemical Biology Summary In both academia and the pharmaceutical industry, large-scale assays for drug discovery are expensive and often impractical, particularly for the increasingly important physiologically relevant model systems that require primary cells, organoids, whole organisms, or expensive or rare reagents. We hypothesized that data from a single high-throughput imaging assay can be repurposed to predict the biological activity of compounds in other assays, even those targeting alternate pathways or biological processes. Indeed, quantitative information extracted from a three-channel microscopy-based screen for glucocorticoid receptor translocation was able to predict assay-specific biological activity in two ongoing drug discovery projects. In these projects, repurposing increased hit rates by 50- to 250-fold over that of the initial project assays while increasing the chemical structure diversity of the hits. Our results suggest that data from high-content screens are a rich source of information that can be used to predict and replace customized biological assays. |
| Related Links | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6031326/pdf http://www.cell.com/article/S2451945618300370/pdf |
| e-ISSN | 24519448 |
| DOI | 10.1016/j.chembiol.2018.01.015 |
| Journal | Cell Chemical Biology |
| Issue Number | 5 |
| Volume Number | 25 |
| Language | English |
| Publisher | Elsevier BV |
| Publisher Date | 2018-05-01 |
| Access Restriction | Open |
| Subject Keyword | Journal: Cell Chemical Biology Cell Biology Drug Discovery High-throughput Screening High-content Imaging Machine Learning Matrix Factorization Bayesian Matrix Factorization Deep Learning Computational Chemistry |
| Content Type | Text |
| Resource Type | Article |
