README.md

metadata

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

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, the UK Biobank ASCVD 10M cohort, and the Insomnia 1M cohort, all available under the DBbun namespace on Hugging Face.