| --- |
| license: cc-by-4.0 |
| task_categories: |
| - tabular-classification |
| - tabular-regression |
| language: |
| - en |
| tags: |
| - oligonucleotide |
| - hepatotoxicity |
| - antisense |
| - siRNA |
| - GalNAc |
| - in-silico |
| - synthetic |
| - toxicology |
| - liver |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # OligoTox Phase 2 Dataset |
|
|
| A computationally generated, AI-ready, literature-informed dataset for |
| modeling oligonucleotide-associated hepatotoxicity. Released by DBbun |
| LLC as part of a submission to the NIH/NCATS OligoTox Open Data Challenge |
| Phase 2. |
|
|
| ## Summary |
|
|
| The dataset connects oligonucleotide sequence, chemical modification |
| pattern, delivery platform, dose/exposure context, in vitro or |
| translational liver-relevant assay context, controls, and toxicity |
| readouts in a structured set of tables. The final dataset contains: |
|
|
| - **1,120 oligo records** (1,000 generated non-control oligos + 120 controls) |
| - **5,600 assay instances** across multiple time points and replicates |
| - **16,800 replicate-level toxicity readouts** |
| - **127 tables** comprising 8 core modeling tables (oligo metadata, aggregate chemistry, position-level modifications, biophysical, dose, assays, readouts, controls) plus per-source |
| evidence modules and supporting metadata files |
|
|
| The dataset is intended for use in developing, training, benchmarking, |
| and stress-testing in silico predictive models of oligonucleotide |
| toxicity. It is not presented as experimentally measured wet-lab data; |
| all values are computationally generated, with row-level provenance |
| metadata distinguishing literature-grounded generated values from |
| inferred and reported values. |
|
|
| ## Repository structure |
|
|
| ``` |
| / |
| ├── README.md ← This file |
| ├── LICENSE ← CC-BY-4.0 license text |
| │ |
| ├── oligos.csv ┐ |
| ├── chemical_modifications.csv │ |
| ├── oligo_modification_positions.csv │ 8 core modeling tables |
| ├── biophysical_properties.csv │ Use these directly for predictive |
| ├── dose_exposure.csv │ modeling tasks |
| ├── assays.csv │ |
| ├── toxicity_readouts.csv │ |
| ├── controls.csv ┘ |
| │ |
| ├── schema.json ┐ |
| ├── data_dictionary.csv │ |
| ├── data_dictionary_full.csv │ |
| ├── dataset_manifest.json │ Top-level documentation |
| ├── reproducibility_manifest.json │ and validation |
| ├── validation_summary.csv │ |
| ├── toxicity_readouts_calibration_audit.json ┘ |
| │ |
| ├── metadata/ (11 governance/provenance tables: |
| │ assumptions, provenance, sources, |
| │ triage log, gap map, etc.) |
| │ |
| ├── evidence/ (105 per-source and broad-context |
| │ evidence tables documenting |
| │ literature-grounded generation |
| │ logic for each source paper) |
| │ |
| └── figures/ (5 reference figures) |
| ``` |
|
|
| ## How to use |
|
|
| ```python |
| import pandas as pd |
| |
| # Core modeling tables (root level) |
| oligos = pd.read_csv("oligos.csv") |
| mods = pd.read_csv("chemical_modifications.csv") |
| positions = pd.read_csv("oligo_modification_positions.csv") |
| biophys = pd.read_csv("biophysical_properties.csv") |
| dose = pd.read_csv("dose_exposure.csv") |
| assays = pd.read_csv("assays.csv") |
| readouts = pd.read_csv("toxicity_readouts.csv") |
| controls = pd.read_csv("controls.csv") |
| |
| # Governance / provenance metadata (in metadata/ subfolder) |
| sources = pd.read_csv("metadata/sources.csv") |
| provenance = pd.read_csv("metadata/provenance.csv") |
| |
| # Several tables share metadata columns (oligo_id, source_id, value_origin, |
| # etc.). When joining for modeling, select only the predictor columns you |
| # need from each secondary table to avoid duplicate-column collisions. |
| model_ready = ( |
| readouts |
| .merge(assays[["assay_id", "model_system", "cell_model", "species", |
| "organoid_or_MPS", "assay_type", "replicate_count"]], |
| on="assay_id", how="left") |
| .merge(oligos[["oligo_id", "oligo_modality", "target_gene", |
| "sequence_5to3", "sequence_length", |
| "is_administered_oligo"]], |
| on="oligo_id", how="left") |
| .merge(mods[["oligo_id", "sugar_chemistry", "backbone_class", |
| "phosphorothioate_fraction", "GalNAc_conjugated"]], |
| on="oligo_id", how="left") |
| .merge(biophys[["oligo_id", "GC_content", "predicted_Tm_celsius", |
| "predicted_delta_G_kcal_mol", |
| "predicted_offtarget_hybridization_burden"]], |
| on="oligo_id", how="left") |
| .merge(dose[["oligo_id", "dose_nM", "exposure_duration_days"]], |
| on="oligo_id", how="left") |
| ) |
| |
| # Restrict to administered oligonucleotides for predictive modeling |
| model_ready = model_ready[model_ready["is_administered_oligo"] == True] |
| ``` |
|
|
| A baseline RandomForest classifier on standard predictors achieves |
| overall accuracy of approximately 0.84 on the calibrated dataset, with |
| per-class F1 scores of 0.95 (low-risk), 0.74 (moderate), and 0.49 |
| (high-risk). |
|
|
|
|
| ## Dataset structure |
|
|
| ### Core modeling tables |
|
|
| | Table | Description | |
| |-------|-------------| |
| | `oligos.csv` | Oligonucleotide identifiers, target genes, modality, 5'-to-3' sequence, sequence length, `is_administered_oligo` flag, control assignment, source role | |
| | `chemical_modifications.csv` | Aggregate chemistry per oligo: sugar chemistry, backbone class, phosphorothioate fraction, modification pattern, GalNAc conjugation, purity, characterization-method metadata | |
| | `oligo_modification_positions.csv` | Position-level chemical modifications: per-position base, sugar modification, backbone linkage, region (e.g., gapmer wing/gap, siRNA seed region), terminal conjugate, and provenance | |
| | `biophysical_properties.csv` | Predicted Tm, ΔG, GC content, off-target hybridization burden, sequence-derived risk fields | |
| | `dose_exposure.csv` | Dose/concentration, exposure duration, treatment frequency, exposure normalization | |
| | `assays.csv` | Model system, cell model, species/human-proxy flag, organoid/MPS status, replicate count, assay type | |
| | `toxicity_readouts.csv` | Replicate-level readouts: viability, ALT/AST proxy fold change, apoptosis, stress response, transcriptomic perturbation, immune activation, inflammatory context, overall risk score, hepatotoxicity label | |
| | `controls.csv` | Positive, negative, vehicle, and platform-specific control oligos with rationale, derived from public literature | |
|
|
| ### Supporting documentation tables |
|
|
| | File | Description | |
| |------|-------------| |
| | `schema.json` | Programmatic schema describing every table, column types, value ranges, and categorical values | |
| | `data_dictionary.csv` | Curated variable-level definitions for the core modeling tables | |
| | `data_dictionary_full.csv` | Auto-generated comprehensive column-level documentation across all dataset tables | |
| | `dataset_manifest.json` | Full inventory of dataset files with sizes | |
| | `reproducibility_manifest.json` | Reproducibility configuration: pipeline parameters, seeds, calibration step description | |
| | `validation_summary.csv` | Automated validation checks: row counts, identifier mapping, range checks, schema consistency | |
| | `assumptions.csv` | Explicit list of biological and modeling assumptions | |
| | `provenance.csv` | Provenance category definitions | |
| | `sources.csv` | Literature source metadata: paper titles, source IDs, relevance, license | |
| | `source_triage_log.csv` | Documentation of source-level inclusion, deferral, exclusion decisions | |
| | `information_gap_map.csv` | Identification of fields where future experimental data would have highest value | |
| | `model_readiness_report.csv` | Modeling-readiness summary across required predictor and outcome fields | |
| | `challenge_alignment_scorecard.csv` | Mapping of dataset components to challenge judging factors | |
| | `toxicity_readouts_calibration_audit.json` | Full specification of the stochastic readout calibration step (random seed, per-readout sigmas, score weights, label fractions) | |
| | `figures/` | Five reference figures: label distribution before/after calibration, classifier performance, risk score distribution, predictor distributions, top source contributors | |
|
|
| ### Per-source evidence modules |
|
|
| The dataset includes per-paper or per-context evidence tables (e.g., |
| `hsd17b13_genetics_aso_translation.csv`, `pnpla3_azd2693_precision_mash.csv`, |
| `galnac_sirna_offtarget_rat_hepatotoxicity.csv`) that record the |
| literature-grounded generation logic applied for each source. These |
| tables document which design rationale, control logic, or readout |
| constraint was contributed by each source. |
|
|
| ## Provenance |
|
|
| Every generated value carries a provenance label, which can take one of |
| five values: |
|
|
| - `reported_in_paper` — value reported directly in a source paper |
| - `extracted_from_paper` — value extracted from a source paper's tables/figures |
| - `inferred_from_paper` — value inferred from source-level reasoning |
| - `literature_grounded_generated` — value generated within |
| literature-informed bounds |
| - `not_reported` — value not available |
|
|
| Toxicity readouts are stochastically calibrated with assay-specific |
| noise to produce realistic predictive structure for in silico modeling. |
| The full calibration specification is recorded in |
| `toxicity_readouts_calibration_audit.json`. |
|
|
| ## Limitations |
|
|
| - This dataset is computationally generated; no physical oligonucleotides |
| were synthesized, purified, administered, or assayed. |
| - The dataset is intended for in silico model development, schema |
| evaluation, and benchmarking. It is not intended as direct evidence |
| for regulatory decision-making. |
| - The hepatotoxicity label distribution (60% low / 30% moderate / 10% |
| high) is a deliberate benchmark stratification chosen to produce a |
| non-trivial classification problem with both common and rare strata; |
| it is not a claim about wet-lab oligonucleotide toxicity prevalence. |
| - Of the 1,120 rows in `oligos.csv`, 1,089 represent oligos with |
| explicit sequences and 31 represent context records without an |
| administered oligo: vehicle/mock controls, clinical risk-score |
| contexts, extracellular-vesicle delivery contexts, small-molecule |
| rescue contexts, and non-administered injury-model contexts. Each |
| row carries an `is_administered_oligo` boolean column for |
| unambiguous filtering. Users restricting analysis to sequence-bearing |
| oligos should filter on `is_administered_oligo == True` (equivalent |
| to `sequence_length > 0`). Context records additionally carry an |
| `oligo_modality` value that explicitly identifies their non-oligo |
| nature (e.g., `vehicle_mock`, `context_not_administered_oligo`, |
| `extracellular_vesicle_context_not_oligo`, |
| `clinical-risk-score-context`). |
| - Original source PDFs are not redistributed; sources are identified |
| through title-level metadata only. |
|
|
| ## Citation |
|
|
| If you use this dataset, please cite: |
|
|
| ``` |
| DBbun LLC. OligoTox Phase 2 Dataset: A computationally generated, |
| literature-informed dataset for oligonucleotide-associated hepatotoxicity |
| modeling. Hugging Face Datasets, 2026. |
| ``` |
|
|
| ## Related work |
|
|
| DBbun LLC has previously released open synthetic biomedical datasets |
| including the [MELD-Plus 1M cohort](https://huggingface.co/datasets/DBbun/1M_MELD_Plus_v1.0), the [UK Biobank ASCVD 10M cohort](https://huggingface.co/datasets/DBbun/1M_CIRCULATIONAHA.120.052430_v1.0), and |
| the [Insomnia 1M cohort](https://huggingface.co/datasets/DBbun/1M_Insomnia_Nature_SR_2018_v1.0), all available under the DBbun namespace on |
| Hugging Face. |
|
|
