Published October 23, 2025
| Version v1
Dataset
Open
DrugReflector model checkpoints
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Description
DrugReflector is an ensemble of three multi-layer perceptron classifiers trained on Connectivity Map transcriptional signatures to predict compound classes from transcriptional signatures. The training set was partitioned into three non-overlapping splits, and each model was trained on two of the three splits using individual perturbation signatures as inputs (without replicate averaging).
This DOI contains the resulting three torch model checkpoints, one for each split-trained model.
Files
Files
(274.5 MB)
| Name | Size | Download all |
|---|---|---|
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md5:3d195e67b3170cc26c9b3972d0222018
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91.5 MB | Download |
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md5:f162839273c04d66259b8457bcbd2e83
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91.5 MB | Download |
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md5:2c5f21fa08206f18de1940fb6f03b527
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91.5 MB | Download |
Additional details
Related works
- Is supplement to
- Software: 10.5281/zenodo.16921928 (DOI)
Software
- Repository URL
- https://github.com/Cellarity/drugreflector/
- Programming language
- Python
- Development Status
- Active
References
- Benjamin DeMeo et al., Active learning framework leveraging transcriptomics identifies modulators of disease phenotypes.Science0,eadi8577DOI:10.1126/science.adi8577