Datasets:

jang1563
/

NegBioDB

license: cc-by-sa-4.0
task_categories:
  - text-classification
  - text-generation
  - tabular-classification
language:
  - en
size_categories:
  - 10M<n<100M
tags:
  - biology
  - chemistry
  - drug-discovery
  - clinical-trials
  - protein-protein-interaction
  - gene-essentiality
  - negative-results
  - publication-bias
  - benchmark
  - biomedical
pretty_name: 'NegBioDB: Negative Results Database & Benchmark'
configs:
  - config_name: dti_pairs
    data_files: data/negbiodb_dti_pairs.parquet
  - config_name: dti_m1_balanced
    data_files: data/negbiodb_m1_balanced.parquet
  - config_name: ct_pairs
    data_files: data/ct/negbiodb_ct_pairs.parquet
  - config_name: ppi_pairs
    data_files: data/ppi/negbiodb_ppi_pairs.parquet
  - config_name: ge_pairs
    data_files: data/ge/negbiodb_ge_pairs.parquet

NegBioDB

Negative Results Database & Dual ML/LLM Benchmark for Biomedical Sciences

Approximately 90% of scientific experiments produce null or inconclusive results, yet the vast majority remain unpublished. NegBioDB systematically collects experimentally confirmed negative results across four biomedical domains and provides dual-track ML + LLM benchmarks to quantify the impact of this publication bias on AI models.

Quick Start

# Load any domain with the datasets library
from datasets import load_dataset

# DTI pairs (30.5M negative compound-target interactions)
dti = load_dataset("jang1563/NegBioDB", "dti_pairs", split="train")

# Clinical trial failures (102K intervention-condition pairs)
ct = load_dataset("jang1563/NegBioDB", "ct_pairs", split="train")

# PPI negatives (2.2M confirmed non-interactions)
ppi = load_dataset("jang1563/NegBioDB", "ppi_pairs", split="train")

# Gene essentiality (22.5M gene-cell-line pairs)
ge = load_dataset("jang1563/NegBioDB", "ge_pairs", split="train")

Or load directly with pandas:

import pandas as pd
from huggingface_hub import hf_hub_download

# Download a specific file
path = hf_hub_download("jang1563/NegBioDB", "data/negbiodb_dti_pairs.parquet", repo_type="dataset")
df = pd.read_parquet(path)
print(df.shape)  # (1_725_446, ~20 columns)

Database Statistics

Domain	Negative Results	Key Entities	Sources	DB Size
DTI	30,459,583	919K compounds, 3.7K targets	ChEMBL, PubChem, BindingDB, DAVIS	~21 GB
CT	132,925	177K interventions, 56K conditions	AACT, CTO, Open Targets, Shi & Du	~500 MB
PPI	2,229,670	18.4K proteins	IntAct, HuRI, hu.MAP, STRING	849 MB
GE	28,759,256	19,554 genes, 2,132 cell lines	DepMap (CRISPR, RNAi)	~16 GB
Total	~61.6M		14 sources	~38 GB

Key Findings

ML: Negative Source Matters

DTI -- Degree-matched negatives inflate LogAUC by +0.112 on average. Cold-target splits cause catastrophic failure:

DTI Model	Random (NegBioDB)	Random (Degree-Matched)	Cold-Target
DeepDTA	0.833	0.919	0.325
GraphDTA	0.843	0.967	0.241
DrugBAN	0.830	0.955	0.151

PPI -- PIPR cold_both AUROC drops to 0.409 (below random); MLPFeatures remains robust at 0.950.

CT -- NegBioDB negatives are trivially separable (AUROC ~1.0); M2 7-way classification is challenging (best macro-F1 = 0.51).

GE -- Cold-gene splits reveal severe generalization gaps; degree-balanced negatives modestly improve ranking metrics.

LLM: L4 Discrimination Reveals Domain Differences

Domain	L4 MCC Range	Interpretation	Contamination
DTI	≤ 0.18	Near random	Not detected
PPI	0.33--0.44	Moderate	Yes (temporal gap)
CT	0.48--0.56	Meaningful	Not detected
GE	Pending	--	--

Exported Datasets

DTI

File	Description
`negbiodb_dti_pairs.parquet`	1.7M compound-target pairs with 5 split columns
`negbiodb_m1_balanced.parquet`	M1: 1.73M rows (1:1 active:inactive)
`negbiodb_m1_realistic.parquet`	M1: 9.49M rows (1:10 ratio)
`negbiodb_m1_balanced_ddb.parquet`	Degree-balanced split
`negbiodb_m1_uniform_random.parquet`	Uniform random negatives (control)
`negbiodb_m1_degree_matched.parquet`	Degree-matched negatives (control)

CT

File	Description
`ct/negbiodb_ct_pairs.parquet`	102,850 failure pairs, 6 splits
`ct/negbiodb_ct_m1_balanced.parquet`	Binary: 11,222 rows (5,611 pos + 5,611 neg)
`ct/negbiodb_ct_m2.parquet`	7-way category: 112,298 rows

PPI

File	Description
`ppi/negbiodb_ppi_pairs.parquet`	2,220,786 negative pairs with split columns
`ppi/ppi_m1_balanced.parquet`	M1: 123,456 rows (1:1 pos:neg)
`ppi/ppi_m1_realistic.parquet`	M1: 679,008 rows (1:10 ratio)

GE

File	Description
`ge/negbiodb_ge_pairs.parquet`	22.5M gene-cell-line pairs with 5 split columns

LLM Benchmarks

File	Description
`llm_benchmarks/l1_mcq.jsonl`	L1: Multiple-choice classification
`llm_benchmarks/l4_tested_untested.jsonl`	L4: Tested vs. untested discrimination
`ct_llm/ct_l*_dataset.jsonl`	CT domain LLM datasets (L1-L4)
`ppi_llm/ppi_l*_dataset.jsonl`	PPI domain LLM datasets (L1-L4)
`ge_llm/ge_l*_dataset.jsonl`	GE domain LLM datasets (L1-L4)

Data Sources

Source	Records	License
ChEMBL v36	371K	CC BY-SA 3.0
PubChem BioAssay	29.6M	Public Domain
BindingDB	404K	CC BY
DAVIS	20K	Public
AACT (ClinicalTrials.gov)	216,987 trials	Public Domain
CTO	20,627	MIT
Open Targets	32,782 targets	Apache 2.0
Shi & Du 2024	119K + 803K rows	CC BY 4.0
IntAct	779 pairs	CC BY 4.0
HuRI	500,000 pairs	CC BY 4.0
hu.MAP 3.0	1,228,891 pairs	MIT
STRING v12.0	500,000 pairs	CC BY 4.0
DepMap CRISPR	28.7M gene-cell pairs	CC BY 4.0
DepMap RNAi	Integrated	CC BY 4.0

Reproducing from Source

Full pipeline code is available at GitHub.

git clone https://github.com/jang1563/NegBioDB.git
cd NegBioDB
make setup          # Create venv and install dependencies
make db             # Initialize SQLite database
make download       # Download all sources
make load-all       # Run ETL pipelines

Citation

@misc{negbiodb2026,
  title={NegBioDB: A Negative Results Database and Dual ML/LLM Benchmark for Biomedical Sciences},
  author={Kim, JangKeun},
  year={2026},
  url={https://github.com/jang1563/NegBioDB}
}

License & Contact

License: CC BY-SA 4.0 (required by ChEMBL's CC BY-SA 3.0 viral clause)

Contact: JangKeun Kim (jak4013@med.cornell.edu) -- Weill Cornell Medicine