Datasets:
Formats:
parquet
Languages:
English
Size:
10M - 100M
Tags:
biology
chemistry
drug-discovery
clinical-trials
protein-protein-interaction
gene-essentiality
License:
File size: 1,788 Bytes
c3bf51a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | # NegBioDB -- Roadmap
> Last updated: 2026-03-30
## Completed (Phase 1)
### DTI Domain (Drug-Target Interaction)
- 30.5M negative results from 4 sources (ChEMBL, PubChem, BindingDB, DAVIS)
- ML baselines: DeepDTA, GraphDTA, DrugBAN across 5 splits + 2 negative controls
- LLM benchmark: L1-L4 tasks, 5 models, zero-shot and 3-shot configs
- Key finding: degree-matched negatives inflate LogAUC by +0.112
### CT Domain (Clinical Trial Failure)
- 132,925 failure results from 216,987 trials (AACT, CTO, Open Targets, Shi & Du)
- ML baselines: XGBoost, MLP, GNN across M1 (binary) and M2 (7-way) tasks
- LLM benchmark: L1-L4, 5 models
- Key finding: NegBioDB negatives trivially separable (AUROC=1.0); M2 macro-F1=0.51
### PPI Domain (Protein-Protein Interaction)
- 2.2M negative results from 4 sources (IntAct, HuRI, hu.MAP, STRING)
- ML baselines: Siamese CNN, PIPR, MLPFeatures across 4 splits
- LLM benchmark: L1-L4, 5 models
- Key finding: PIPR AUROC drops to 0.41 on cold_both; temporal contamination detected
### GE Domain (Gene Essentiality / DepMap)
- 28.8M negative results from DepMap CRISPR and RNAi screens
- ML baselines: XGBoost, MLPFeatures (seed 42 complete)
- LLM benchmark: 4/5 models complete
- Key finding: cold-gene splits reveal severe generalization gaps
## In Progress
- GE domain: remaining ML seeds (43/44) and Llama LLM runs
- Paper preparation for NeurIPS 2026 Evaluations & Datasets Track
## Planned
### Phase 2: Community & Platform
- Web interface for search, browse, and download
- Python library: `pip install negbiodb`
- REST API with tiered access
- Community submission portal
- Public leaderboard
### Phase 3: Scale
- Expand curated entries via literature mining
- Specialized bio-LLM evaluations
- Cross-track ensemble evaluation (ML + LLM)
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