barneyhill/aso-atlas-2

ASO Atlas 2.0

A benchmark dataset for evaluating antisense oligonucleotide (ASO) prediction across the preclinical pipeline.

Dataset Summary

ASO Atlas 2.0 contains 283,239 measurements across 168,395 unique ASO compounds extracted from 606 USPTO patent filings via an LLM-based extraction pipeline. Each compound's chemistry is encoded in HELM notation, enabling direct use as model input.

The dataset spans four preclinical assay types that mirror the sequential screening pipeline used in ASO drug development:

Config	Rows	Compounds	Description
in_vitro_inhibition	174,297	163,688	Single-dose mRNA knockdown (% inhibition)
dose_response	99,719	16,814	Multi-dose inhibition for IC50 fitting
hepatotoxicity	4,323	2,037	Liver toxicity biomarkers (ALT, AST, etc.)
neurotoxicity	4,900	3,316	Functional observation battery scores

Usage

from datasets import load_dataset

# Load a single config
ds = load_dataset("barneyhill/aso-atlas-2", "in_vitro_inhibition")

# Load all configs
configs = ["in_vitro_inhibition", "dose_response", "hepatotoxicity", "neurotoxicity"]
atlas = {c: load_dataset("barneyhill/aso-atlas-2", c) for c in configs}

Data Fields

Common fields (all configs)

Field	Type	Description
USPTO ID	string	Source patent identifier (e.g., US20150011212)
Table Number	int	Table number within the patent
Compound ID	int	Compound identifier within the patent
HELM Annotation	string	Full chemical structure in HELM notation
target_RNA	string	Target gene/transcript name

in_vitro_inhibition / dose_response

Field	Type	Description
cell_line	string	Cell line used (e.g., HepG2, A549)
dosage_nm	float	Dose in nanomolar
Inhibition_pct	float	mRNA inhibition percentage (−1000 to 100)
cells_per_well	float	Cells seeded per well
cell_line_species	string	Species of origin (human, mouse, etc.)
transfection_method	string	Delivery method (electroporation, lipofection, etc.)
treatment_period_hrs	float	Treatment duration in hours
ccle_cell_line_name	string	Matched CCLE cell line name
ccle_model_id	string	DepMap model ID
ccle_oncotree_lineage	string	Oncotree tissue lineage
ccle_oncotree_disease	string	Oncotree disease classification
cell_line_mapping_source	string	How the CCLE match was determined

hepatotoxicity

Field	Type	Description
species	string	Test species (mouse, rat, monkey, dog)
species_strain	string	Strain (e.g., CD-1 mice, Sprague-Dawley rats)
dosage_mg_per_kg	float	Dose per administration (mg/kg)
num_doses	float	Number of doses administered
dosing_period_days	float	Duration of dosing period
adminstration_method	string	Route (subcutaneous, intraperitoneal)
measurement_source	string	Sample type (plasma, urine)
ALT	list[float]	Alanine aminotransferase values (IU/L)
AST	list[float]	Aspartate aminotransferase values (IU/L)
ALB	list[float]	Albumin values (g/dL)
BUN	list[float]	Blood urea nitrogen values (mg/dL)
CREA	list[float]	Creatinine values (mg/dL)
TBIL	list[float]	Total bilirubin values (mg/dL)
PC_ratio	list[float]	Protein/creatinine ratio

neurotoxicity

Field	Type	Description
species	string	Test species
species_strain	string	Strain
dosage_ug	float	Dose in micrograms
FOB_score	list[float]	Functional observation battery scores (0–7)
latency_time_hours	float	Time from dosing to observation
administration_method	string	Route (typically intracerebroventricular)
tolerability_score_type	string	Scoring system used

HELM Chemistry Encoding

HELM (Hierarchical Editing Language for Macromolecules) encodes full oligonucleotide chemistry including sugar modifications, backbone linkages, and base modifications.

Example: RNA1{[moe](C)[sp].[moe](T)[sp].d(A)[sp].d(G)[sp].[cet](A)}$$$$

Sugar	Chemistry	Backbone	Chemistry
d	2'-deoxy (DNA)	[sp]	Phosphorothioate
[moe]	2'-O-methoxyethyl	.	Phosphodiester
[cet]	Constrained ethyl	[am]	Phosphoramidate
[lna]	Locked nucleic acid
[fR]	2'-fluoro
[m]	2'-O-methyl

Dataset Creation

Source

All data was extracted from publicly available USPTO patent filings using an LLM-based text mining pipeline. Patents were identified via keyword search for antisense oligonucleotide-related terms, and data tables within each patent were parsed to extract compound structures, assay conditions, and measured endpoints.

Processing

1. HELM quality filtering: removed sequences with uncertain chemistry (?), length <11 nucleotides, missing DNA gap, homopolymers, or naked DNA
2. Gene name standardisation via manual curated mappings
3. Cell line standardisation and CCLE (Cancer Cell Line Encyclopedia) enrichment
4. Inhibition values filtered to [−1000%, 100%]
5. Hepatic biomarker values filtered to physiologically plausible ranges
6. FOB scores validated to 0–7 range
7. Deduplication by compound and measurement

No canonical train/test split

This dataset ships raw measurements without a predefined split. The accompanying paper uses patent-level GroupKFold cross-validation to prevent data leakage (compounds from the same patent always stay in the same fold). We recommend the same strategy.

Considerations

Biases

• Patent bias: Data comes exclusively from pharmaceutical patent filings, which over-represent compound classes pursued by industry (predominantly 2'-MOE gapmers). Academic and early-stage chemistries may be underrepresented.
• Species bias: Mouse and rat data dominate; monkey and dog data are sparse.
• Reporting bias: Patents report results selectively. Negative results may be underrepresented.

Limitations

• No independent validation: All measurements originate from patent applicants' own experiments. Cross-laboratory reproducibility is unknown.
• Extraction errors: LLM-based extraction may introduce errors. A validation audit is included in the accompanying code repository.
• Heterogeneous protocols: Assay conditions (cell lines, doses, timepoints) vary across patents and are not standardised.

Ethical considerations

No human subjects data. All data derives from public USPTO patent documents and public biological databases (CCLE). No personally identifiable information is present.```