Datasets:
Formats:
parquet
Languages:
English
Size:
10M - 100M
Tags:
biology
chemistry
drug-discovery
clinical-trials
protein-protein-interaction
gene-essentiality
License:
NegBioDB: Negative Results Database & Dual ML/LLM Benchmark
Biology-first, science-extensible negative results database and dual ML+LLM benchmark
Last updated: 2026-03-30
Project Vision
Approximately 90% of scientific experiments produce null or inconclusive results, yet the vast majority remain unpublished. This systematic gap fundamentally distorts AI/ML model training and evaluation.
Goal: Systematically collect and structure experimentally confirmed negative results across biomedical domains, and build benchmarks that quantify the impact of publication bias on AI/ML models.
Why This Matters
- Publication Bias: 85% of published papers report only positive results
- AI Model Bias: Models trained without negative data produce excessive false positives
- Economic Waste: Duplicated experiments, failed drug discovery pipelines (billions of dollars)
- Proven Impact: Models trained with negative data are more accurate (Organic Letters 2023, bioRxiv 2024)
Architecture
Four Biomedical Domains
┌────────────────────────────────────────────────────────────┐
│ NegBioDB │
│ DTI CT PPI GE │
│ (30.5M neg) (133K neg) (2.2M neg) (28.8M neg) │
│ ChEMBL+ AACT+ IntAct+ DepMap │
│ PubChem+ CTO+ HuRI+ CRISPR+RNAi │
│ BindingDB+ OpenTargets+ hu.MAP+ │
│ DAVIS Shi&Du STRING │
└────────────────────────────────────────────────────────────┘
│ │
┌──────┴──────┐ ┌─────┴──────┐
│ ML Benchmark │ │LLM Benchmark│
│ 3 models × │ │ 5 models × │
│ 5 splits × │ │ 4 levels × │
│ 2 neg types │ │ 4 configs │
└─────────────┘ └────────────┘
Key Technical Decisions
| Decision | Choice | Rationale |
|---|---|---|
| License | CC BY-SA 4.0 | Compatible with ChEMBL CC BY-SA 3.0 (viral clause) |
| Storage | SQLite per domain | Portable, zero-infrastructure, reproducible |
| Export | Parquet with split columns | Standard ML format; lazy-loading friendly |
| ML metrics | LogAUC + 6 others | LogAUC[0.001,0.1] measures early enrichment, not just AUROC |
| LLM evaluation | 4 levels (L1–L4) | Progressive difficulty: MCQ → extraction → reasoning → discrimination |
Domain Status Summary (as of 2026-03-30)
| Domain | DB Size | Negatives | ML Runs | LLM Runs | Status |
|---|---|---|---|---|---|
| DTI | ~21 GB | 30,459,583 | 24/24 ✅ | 81/81 ✅ | Complete |
| CT | ~500 MB | 132,925 | 108/108 ✅ | 80/80 ✅ | Complete |
| PPI | 849 MB | 2,229,670 | 54/54 ✅ | 80/80 ✅ | Complete |
| GE | ~16 GB | 28,759,256 | Seed 42 ✅ | 4/5 models ✅ | Near complete |
DTI Domain (Drug-Target Interaction)
Four sources: ChEMBL v36, PubChem BioAssay, BindingDB, DAVIS
Database
- 30,459,583 negative results
- Source tiers: gold 818,611 / silver 198 / bronze 28,845,632
- 5 split strategies: random / cold_compound / cold_target / scaffold / temporal
Key Results
- ML: Degree-matched negatives inflate LogAUC by +0.112 on average. Cold-target splits catastrophic (LogAUC 0.15–0.33) while AUROC stays deceptively high (0.76–0.89).
- LLM L4: All models near-random (MCC ≤ 0.18). DTI binding decisions are too nuanced for LLMs without domain context.
- LLM L1: Gemini achieves perfect accuracy (1.000) on 3-shot MCQ — artifact of format recognition.
CT Domain (Clinical Trial Failure)
Four sources: AACT (ClinicalTrials.gov), CTO, Open Targets, Shi & Du 2024
Database
- 132,925 failure results from 216,987 trials
- Tiers: gold 23,570 / silver 28,505 / bronze 60,223 / copper 20,627
- 8 failure categories: safety > efficacy > enrollment > strategic > regulatory > design > other
- Drug resolution: 4-step pipeline (ChEMBL exact → PubChem API → fuzzy JaroWinkler → manual CSV)
Benchmark Design
- ML: CT-M1 binary failure prediction; CT-M2 7-way failure category (most challenging)
- LLM: L1 5-way MCQ (1,500 items), L2 failure report extraction (500), L3 reasoning (200), L4 discrimination (500)
Key Results
- CT-M1: NegBioDB negatives trivially separable (AUROC=1.0). Control negatives reveal real difficulty (0.76–0.84).
- CT-M2: XGBoost best (macro-F1=0.51). Scaffold/temporal splits hardest (0.19).
- LLM L4: Gemini MCC=0.56 — highest across all domains. Meaningful discrimination possible for trial failure.
- LLM L3: Ceiling effect — GPT-4o-mini judge too lenient (4.4–5.0/5.0).
PPI Domain (Protein-Protein Interaction)
Four sources: IntAct, HuRI, hu.MAP 3.0, STRING v12.0
Database
- 2,229,670 negative results; 61,728 positive pairs (HuRI Y2H)
- 18,412 proteins; 4 split strategies: random / cold_protein / cold_both / degree_balanced
Key Results
- ML: MLPFeatures (hand-crafted) dominates cold splits (AUROC 0.95 cold_both); PIPR collapses to 0.41 (below random).
- LLM L1: 3-shot near-perfect (0.997–1.000) is an artifact of example format leakage.
- LLM L3: zero-shot >> 3-shot (4.3–4.7 vs 3.1–3.7); gold reasoning examples degrade structural reasoning.
- LLM L4: MCC 0.33–0.44 with confirmed temporal contamination (pre-2015 acc ~0.6–0.8, post-2020 acc ~0.07–0.25).
GE Domain (Gene Essentiality / DepMap)
Two sources: DepMap CRISPR (Chronos scores) and RNAi (DEMETER2)
Database
- 28,759,256 negative results (genes with no essentiality signal)
- Final tiers: Gold 753,878 / Silver 18,608,686 / Bronze 9,396,692
- 19,554 genes × 2,132 cell lines; 22,549,910 aggregated pairs
- 5 split strategies: random / cold_gene / cold_cell_line / cold_both / degree_balanced
Benchmark Design
- ML: XGBoost and MLPFeatures on gene expression + lineage features (gene-cell pair prediction)
- LLM: L1 4-way essentiality MCQ (1,200 items), L2 essentiality data extraction (500), L3 reasoning (200), L4 discrimination (475)
Key Results (partial — Llama pending)
- LLM L3: zero-shot >> 3-shot (overall mean 4.5 vs 2.5) — same pattern as PPI.
- LLM L4: Expected intermediate MCC (DepMap is widely studied; likely contamination present).
- ML: Seed 42 complete; final aggregated results pending seeds 43/44.
Dual Benchmark Framework
LLM Benchmark Levels
| Level | Task | Difficulty | Automation |
|---|---|---|---|
| L1 | Multiple-choice classification | Easy | Fully automated (exact match) |
| L2 | Structured field extraction | Medium | Automated (JSON schema check + field F1) |
| L3 | Free-text reasoning quality | Hard | LLM-as-judge (Gemini 2.5-Flash, 4 rubric dimensions) |
| L4 | Real vs synthetic discrimination | Hard | Automated (MCC on binary decision) |
LLM Models Evaluated
| Model | Provider | Type |
|---|---|---|
| Claude Haiku 4.5 | Anthropic API | Small API model |
| Gemini 2.5-Flash | Google API | Small API model |
| GPT-4o-mini | OpenAI API | Small API model |
| Qwen2.5-7B-Instruct | HuggingFace / vLLM | Open-weight local |
| Llama-3.1-8B-Instruct | HuggingFace / vLLM | Open-weight local |
Cross-Domain LLM L4 Summary
DTI (≤0.18) < PPI (0.33–0.44) < CT (0.48–0.56)
↑
Increasing task complexity
and LLM accessible signal
Timeline
| Milestone | Date |
|---|---|
| Project initiated | 2026-03-02 |
| DTI domain complete (ML + LLM) | 2026-03-13 |
| CT domain initiated | 2026-03-17 |
| CT domain complete (ML + LLM) | 2026-03-20 |
| PPI domain complete (ML + LLM) | 2026-03-23 |
| GE domain ETL + ML export | 2026-03-23 |
| GE LLM (4/5 models) | 2026-03-24 |
| Public release (GitHub + HuggingFace) | 2026-03-30 |