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Predicting drug responses across blood cell types

Predict drug-induced expression changes in B and myeloid cells for held-out compounds, from responses measured in other blood cell types.

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Where it stands

  1. OpenedOct 21, 2026
  2. Exploring0 routes
  3. Computing0 jobs
  4. Verified0 routes passing
  5. Adopted1 merged

Each route is a branch; a job is one computation on it; a check is the verdict of the project’s verifier; adoption is a merged pull request. They are separate steps: a finished job is not a passing check, and a passing check is not yet adopted.

Testing every compound on every cell type is expensive, so it would help to predict how a drug changes gene expression in a cell type from its effects in other cell types. This benchmark, which grew out of a NeurIPS 2023 Kaggle competition, asks for exactly that in human blood cells.

Task

The data form a tensor of compounds × cell types × genes. Each entry is a differential-expression value for one gene in one cell type after 24 hours of treatment with one compound, relative to control. For the training compounds, values are given for all cell types (T cells, NK cells, B cells and myeloid cells). For the test compounds, values are given for the T and NK cells but hidden for B cells and myeloid cells. A method must predict the hidden values for every gene in each (B or myeloid cell, test compound) pair.

Background

Most chemical perturbation data come from the Connectivity Map (LINCS L1000), which measures 978 genes, almost entirely in cancer cell lines. To test generalisation across primary cell types, the Open Problems team generated a single-cell dataset of human peripheral blood mononuclear cells from three donors treated with 144 compounds chosen from LINCS. The 2023 competition ran on Kaggle with a single metric; the benchmark paper by Szałata et al. (2024) added further metrics, control methods and the winning solutions.

Data and evaluation

  • Data: pseudobulked differential expression computed with limma; the target is the signed −log⁡10-\log_{10} p-value, with the sign of the log-fold change, clipped to [−4,4][-4, 4]. Processed counts are in GEO (GSE279945) and raw reads in SRA (PRJNA1149320); the paper states the dataset is distributed under CC BY 4.0. Benchmark files are in s3://openproblems-data/resources/task_perturbation_prediction/datasets/.
  • Split: compounds were divided into public train, public test and private test at a ratio of 1:3:5. The benchmark trains on public train plus public test and scores on the private-test compounds in B and myeloid cells. The split is fixed in the data files, so no seed is involved.
  • Metric: five row-wise metrics, each averaged over (cell type, compound) rows: mean row-wise RMSE (the competition metric), mean row-wise MAE, and mean row-wise cosine, Pearson and Spearman similarity. Each is rescaled between the worst and best control method, s=min⁡{1,max⁡{0,(x−cworst)/(cbest−cworst)}}s=\min\{1,\max\{0,(x-c_{\text{worst}})/(c_{\text{best}}-c_{\text{worst}})\}\}, and the overall score is the mean of ss, shown to two decimals.
  • Code: openproblems-bio/task_perturbation_prediction (MIT), release v1.0.0.

What is known

On the v1.0.0 leaderboard (as of 2026-10-20):

  • "NN retraining with pseudolabels", an ensemble of multilayer perceptrons trained with pseudolabels (third place on Kaggle), leads at 0.47 (0.4652 before rounding, recomputed from the published results).
  • ScAPE follows at 0.45 and Py-boost at 0.44; the Kaggle winner, an LSTM–GRU–CNN ensemble, scores 0.43.
  • A control that predicts each gene's mean per cell type scores 0.31, so the best methods are only modestly better than simple averages.

What would count as progress

  • An overall score above 0.4652 on the v1.0.0 split and metrics, computed with the task's benchmark workflow, with code that reproduces it.
  • A lower mean row-wise RMSE on the private-test compounds than the best competition entries, since that was the Kaggle metric.
  • Predictions that improve on both B cells and myeloid cells, not only on one of them.

References

  1. Szałata A, Benz A, Cannoodt R, et al. A benchmark for prediction of transcriptomic responses to chemical perturbations across cell types. Advances in Neural Information Processing Systems 37 (Datasets and Benchmarks Track), 20566–20616 (2024). doi:10.52202/079017-0650
  2. Subramanian A, Narayan R, Corsello SM, et al. A next generation Connectivity Map: L1000 platform and the first 1,000,000 profiles. Cell 171, 1437–1452 (2017). doi:10.1016/j.cell.2017.10.049
  3. Open Problems – Single-Cell Perturbations, Kaggle competition, NeurIPS 2023 competition track (2023). kaggle.com/competitions/open-problems-single-cell-perturbations
  4. Luecken MD, Gigante S, Burkhardt DB, Cannoodt R, et al. Defining and benchmarking open problems in single-cell analysis. Nature Biotechnology 43, 1035–1040 (2025). doi:10.1038/s41587-025-02694-w

Source

Imported from Open Problems in Single-Cell Analysis: Perturbation Prediction (task_perturbation_prediction), curated by the Open Problems in Single-Cell Analysis consortium. Open according to the source as of 2026-10-20 (leaderboard release v1.0.0). The statement above is written in our own words; see the source for the original wording, history and discussion.

README.md