Datasets:

jang1563
/

NegBioDB

@@ -1,561 +1,48 @@
-# NegBioDB — Execution Roadmap
-> Last updated: 2026-03-30 (v19 — DTI ✅ CT ✅ PPI ✅ GE near-complete: ML seed 42 done, LLM 4/5 models done)
----
-## Critical Findings (Updated March 2026)
-1. **HCDT 2.0 License: CC BY-NC-ND 4.0** — Cannot redistribute derivatives. Must independently recreate from underlying sources (BindingDB, ChEMBL, GtoPdb, PubChem, TTD). Use 10 uM primary threshold (not 100 uM) to differentiate.
-2. **InertDB License: CC BY-NC** — Cannot include in commercial track. Provide optional download script only.
-3. **Submission requirements**: downloadable data, Croissant metadata, code available, Datasheet for Datasets.
-4. **LIT-PCBA compromised** (2025 audit found data leakage) — Creates urgency for NegBioDB as replacement gold-standard.
-5. **Recommended NegBioDB License: CC BY-SA 4.0** — Compatible with ChEMBL (CC BY-SA 3.0) via one-way upgrade.
-6. **No direct competitor exists** as of March 2026.
-7. **No LLM benchmark tests negative DTI tasks** — ChemBench, Mol-Instructions, MedQA, SciBench all lack negative result evaluation. NegBioBench LLM track is first-of-kind.
-8. **LLM evaluation also free** — Gemini Flash free tier as LLM-as-Judge + ollama local models as baselines. Flagship models (GPT-4, Claude) added post-stabilization only.
-9. **Data volume is NOT the bottleneck** — ChEMBL alone has ~527K quality inactive records (pchembl < 5, validated). PubChem has ~61M target-annotated confirmatory inactives. Estimated 200K+ unique compound-target pairs available. Minimum target raised to **10K curated entries** (from 5K).
-10. **PubChem FTP bulk is far superior to API** — `bioactivities.tsv.gz` (3 GB) contains all 301M bioactivity rows. Processing: < 1 day. API approach would take weeks.
-11. **LLM-as-Judge rate limit (250 RPD)** — Must-have tasks (L1, L2, L4) all use automated evaluation. Judge needed only for should-have L3 (1,530 calls = 6 days). All judge tasks with 3 models = 20 days. With 6 models = 39 days (NOT feasible for sprint).
-12. **Paper narrative must be problem-first** — "Existing benchmarks are broken" (Exp 1 + Exp 4), not "Here's a database." Database is the solution, not the contribution.
-13. **Positive data protocol required** — NegBioDB is negative-only. For ML benchmarking (M1), positive data must be sourced from ChEMBL (pChEMBL ≥ 6). Report two class ratios: balanced (1:1) and realistic (1:10). See §Positive Data Protocol below.
-14. **Random negative baseline must be precisely defined** — Exp 1 compares NegBioDB negatives against random negatives. Random = uniform sampling from untested compound-target pairs (TDC standard). See §Random Negative Control Design.
-15. **Paper format: 9 pages** + unlimited appendix. Croissant is **mandatory** (desk rejection if missing/invalid).
-16. **GPU strategy: Kaggle free tier** (30 hrs/week) is sufficient for 18 ML baseline runs (~36-72 GPU-hours over 4 weeks). Fallback: Colab Pro ($10/month).
-17. **ChEMBL v36** (Sep 2025, 24.3M activities) should be used, not v35. `chembl_downloader` fetches latest by default.
-18. **Nature MI 2025** — Biologically driven negative subsampling paper independently shows "assumed negatives" distort DTI models. Related: EviDTI (Nature Comms 2025), DDB paper (BMC Biology 2025), LIT-PCBA audit (2025).
----
----
-## Positive Data Protocol (P0 — Expert Panel Finding)
-NegBioDB is a negative-only database. For ML benchmarking (Task M1: binary DTI prediction), **positive (active) data is required**. This section defines the protocol.
-### Positive Data Source
-```sql
--- Extract active DTIs from ChEMBL v36 SQLite
--- Threshold: pChEMBL ≥ 6 (IC50/Ki/Kd/EC50 ≤ 1 uM)
-SELECT
-  a.molregno, a.pchembl_value, a.standard_type,
-  cs.canonical_smiles, cs.standard_inchi_key,
-  cp.accession AS uniprot_id
-FROM activities a
-JOIN compound_structures cs ON a.molregno = cs.molregno
-JOIN assays ass ON a.assay_id = ass.assay_id
-JOIN target_dictionary td ON ass.tid = td.tid
-LEFT JOIN target_components tc ON td.tid = tc.tid
-LEFT JOIN component_sequences cp ON tc.component_id = cp.component_id
-WHERE a.pchembl_value >= 6
-  AND a.standard_type IN ('IC50', 'Ki', 'Kd', 'EC50')
-  AND a.data_validity_comment IS NULL
-  AND td.target_type = 'SINGLE PROTEIN'
-  AND cp.accession IS NOT NULL
-```
-### Positive-Negative Pairing
-| Setting | Ratio | Purpose | Primary Use |
-|---------|-------|---------|-------------|
-| **Balanced** | 1:1 (active:inactive) | Fair model comparison | Exp 1, Exp 4, baselines |
-| **Realistic** | 1:10 (active:inactive) | Real-world HTS simulation | Supplementary evaluation |
-- Positives restricted to **shared targets** between ChEMBL actives and NegBioDB inactives (same target pool)
-- Same compound standardization pipeline (RDKit) applied to positives
-- DAVIS matrix known actives (pKd ≥ 7, Kd ≤ 100 nM) used as **Gold-standard validation set**
-### Overlap Prevention
-- Active and inactive compound-target pairs must not overlap (same pair cannot be both active and inactive)
-- Borderline zone (pChEMBL 4.5–5.5) excluded from both positive and negative sets for clean separation
-- Overlap analysis: report % of NegBioDB negatives where the same compound appears as active against a different target
----
-## Random Negative Control Design (P0 — Expert Panel Finding)
-Experiment 1 compares NegBioDB's experimentally confirmed negatives against **random negatives**. The random negative generation must be precisely defined.
-### Control Conditions for Exp 1
-| Control | Method | What it Tests |
-|---------|--------|---------------|
-| **Uniform random** | Sample untested compound-target pairs uniformly at random from the full cross-product space | Standard TDC approach; tests baseline inflation |
-| **Degree-matched random** | Sample untested pairs matching the degree distribution of NegBioDB pairs | Isolates the effect of experimental confirmation vs. degree bias |
-**All Exp 1 runs:**
-- 3 ML models (DeepDTA, GraphDTA, DrugBAN)
-- Random split only (for controlled comparison)
-- Same positive data, same split seed
-- Only the negative set changes: NegBioDB confirmed vs. uniform random vs. degree-matched random
-- **Total: 3 models × 3 negative conditions = 9 runs** (was 3 runs; updated)
-- **Note:** The 3 NegBioDB-negative random-split runs are shared with the baseline count (9 baselines include random split). Thus Exp 1 adds only **6 new runs** (uniform random + degree-matched random). Similarly, Exp 4 shares the random-split baseline and adds only **3 new DDB runs**. Overall: 9 baseline + 6 Exp 1 + 3 Exp 4 = **18 total**.
-- **Exp 4 definition:** The DDB comparison uses a full-task degree-balanced split on the merged M1 balanced benchmark. Positives and negatives are reassigned together under the same split policy.
-### Reporting
-- Table: [Model × Negative Source × Metric] for LogAUC, AUPRC, MCC
-- Expected: NegBioDB > degree-matched > uniform random for precision-oriented metrics
-- If NegBioDB ≈ uniform random → narrative shifts to Exp 4 (DDB bias) as primary result
----
-## Phase 1: Implementation Sprint (Weeks 0-11)
-### Week 1: Scaffolding + Download + Schema ✅ COMPLETE
-- [x] **Project scaffolding**: Create `src/negbiodb/`, `scripts/`, `tests/`, `migrations/`, `config.yaml`, `Makefile`, `pyproject.toml`
-- [x] **Dependency management**: `pyproject.toml` with Python 3.11+, rdkit, pandas, pyarrow, mlcroissant, tqdm, scikit-learn
-- [x] **Makefile skeleton**: Define target structure (full pipeline encoding in Week 2)
-- [x] Finalize database schema (SQLite for MVP) — apply `migrations/001_initial_schema.sql`
-- [x] Download all source data (see below — < 1 day total)
-- [x] **Verify ChEMBL v36** (Sep 2025) downloaded, not v35
-- [x] **[B7] Verify PubChem bioactivities.tsv.gz column names** after download
-- [ ] **[B4] Hardware decision**: Test local RAM/GPU. If < 32GB RAM → use Llama 3.1 8B + Mistral 7B (not 70B). If ≥ 32GB → quantized Llama 3.3 70B (Q4). Document choice.
-- [ ] **[B2] Verify citations**: Search for Nature MI 2025 negative subsampling paper + Science 2025 editorial. If not found → substitute with EviDTI, DDB paper, LIT-PCBA audit
-- [ ] **[B3] Monitor submission deadlines**
-### Week 2: Standardization + Extraction Start ✅ COMPLETE
-- [x] Implement compound standardization pipeline (RDKit: salt removal, normalization, InChIKey)
-- [x] Implement target standardization pipeline (UniProt accession as canonical ID)
-- [x] Set up cross-DB deduplication (InChIKey[0:14] connectivity layer)
-- [x] **Makefile pipeline**: Encode full data pipeline dependency graph as executable Makefile targets
-- [ ] **[B5] Check shared target pool size**: Count intersection of NegBioDB targets ∩ ChEMBL pChEMBL ≥ 6 targets. If < 200 targets → expand NegBioDB target extraction
-- [ ] **[B6] Check borderline exclusion impact**: Run pChEMBL distribution query on ChEMBL. Estimate data loss from excluding pChEMBL 4.5–5.5 zone
-### Week 2-4: Data Extraction ✅ COMPLETE
-**Result: 30.5M negative_results (>minimum target of 10K — far exceeded)**
-**Data Sources (License-Safe Only):**
-| Source | Available Volume | Method | License |
-|--------|-----------------|--------|---------|
-| PubChem BioAssay (confirmatory inactive) | **~61M** (target-annotated) | **FTP bulk: `bioactivities.tsv.gz` (3 GB)** + `bioassays.tsv.gz` (52 MB) | Public domain |
-| ChEMBL pChEMBL < 5 (quality-filtered) | **~527K** records → ~100-200K unique pairs | **SQLite via `chembl_downloader`** (4.6 GB, 1h setup) | CC BY-SA 3.0 |
-| ChEMBL activity_comment "Not Active" | **~763K** (literature-curated) | SQL query on same SQLite dump | CC BY-SA 3.0 |
-| BindingDB (Kd/Ki > 10 uM) | **~30K+** | Bulk TSV download + filter | CC BY |
-| DAVIS complete matrix (pKd ≤ 5) | **~27K** | TDC Python download | Public/academic |
-**NOT bundled (license issues):**
-- HCDT 2.0 (CC BY-NC-ND) — Use as validation reference only; we use 10 uM threshold (not 100 uM) to differentiate
-- InertDB (CC BY-NC) — Optional download script for users
-**PubChem FTP extraction pipeline (< 1 day):**
-```
-1. bioassays.tsv.gz �� filter confirmatory AIDs with target annotations → ~260K AIDs
-2. bioactivities.tsv.gz (stream) → filter AID ∈ confirmatory, Outcome=Inactive → ~61M records
-3. Prioritize MLPCN/MLSCN assays (~4,500 AIDs, genuine HTS dose-response) for Silver tier
-4. Map SID→CID via Sid2CidSMILES.gz, targets via Aid2GeneidAccessionUniProt.gz
-```
-- [x] Download PubChem FTP files (bioactivities.tsv.gz + bioassays.tsv.gz + mapping files)
-- [x] Download ChEMBL v36 SQLite via chembl_downloader
-- [x] Download BindingDB bulk TSV
-- [x] Build PubChem FTP extraction script (**streaming with chunksize=100K** — 12GB uncompressed)
-- [x] Build ChEMBL extraction SQL: inactive (activity_comment + pChEMBL < 5) **AND active (pChEMBL ≥ 6)** for positive data
-- [x] Build BindingDB extraction script (filter Kd/Ki > 10 uM, human targets)
-- [x] Integrate DAVIS matrix from TDC (both actives pKd ≥ 7 and inactives pKd ≤ 5)
-- [x] Run compound/target standardization on all extracted data (multiprocessing for RDKit)
-- [x] Run cross-DB deduplication + **overlap analysis** (vs DAVIS, TDC, DUD-E, LIT-PCBA)
-- [x] Assign confidence tiers (gold/silver/bronze/copper — lowercase, matching DDL CHECK constraint)
-- [x] **Extract ChEMBL positives**: 883K → 863K after 21K overlap removal (pChEMBL ≥ 6, shared targets only)
-- [x] **Positive-negative pairing**: M1 balanced (1.73M, 1:1) + M1 realistic (9.49M, 1:10). Zero compound-target overlap verified.
-- [x] **Borderline exclusion**: pChEMBL 4.5–5.5 removed from both pools
-- [x] Spot-check top 100 most-duplicated compounds (manual QC checkpoint)
-- [x] Run data leakage check: cold split leaks = 0, cross-source overlaps documented
-### Week 3-5: Benchmark Construction (ML + LLM)
-**ML Track:**
-- [x] Implement 3 must-have splits (Random, Cold-Compound, Cold-Target) + DDB for Exp 4
-- [x] Implement ML evaluation metrics: LogAUC[0.001,0.1], BEDROC, EF@1%, EF@5%, AUPRC, MCC, AUROC
-- [x] (Should have) Add Cold-Both, Temporal, Scaffold splits (all 6 implemented)
-**LLM Track:** ✅ INFRASTRUCTURE COMPLETE (2026-03-12)
-- [x] Design prompt templates for L1, L2, L4 (priority tasks) → `llm_prompts.py`
-- [x] Construct L1 dataset: 2,000 MCQ from NegBioDB entries → `build_l1_dataset.py`
-- [x] Construct L2 dataset: 116 candidates (semi-automated) → `build_l2_dataset.py`
-- [x] Construct L4 dataset: 500 tested/untested pairs → `build_l4_dataset.py`
-- [x] Implement automated evaluation scripts → `llm_eval.py` (L1: accuracy/F1, L2: entity F1, L4: classification F1)
-- [x] Build compound name cache → `compound_names.parquet` (144,633 names from ChEMBL)
-- [x] Construct L3 dataset: 50 pilot reasoning examples → `build_l3_dataset.py`
-- [x] LLM client (vLLM + Gemini) → `llm_client.py`
-- [x] SLURM templates + batch submission → `run_llm_local.slurm`, `run_llm_gemini.slurm`, `submit_llm_all.sh`
-- [x] Results aggregation → `collect_llm_results.py` (Table 2)
-- [x] 54 new tests (29 eval + 25 dataset), 329 total pass
-- [ ] **L2 gold annotation**: 15–20h human review needed for `l2_gold.jsonl`
-**Shared:**
-- [ ] Generate Croissant machine-readable metadata (mandatory for submission)
-- [ ] **Validate Croissant** with `mlcroissant` library. Gate: `mlcroissant.Dataset('metadata.json')` runs without errors
-- [ ] Write Datasheet for Datasets (Gebru et al. template)
-### Week 5-7: Baseline Experiments (ML + LLM)
-**ML Baselines:**
-| Model | Type | Priority | Runs (3 splits) | Status |
-|-------|------|----------|-----------------|--------|
-| DeepDTA | Sequence CNN | Must have | 3 | ✅ Implemented |
-| GraphDTA | Graph neural network | Must have | 3 | ✅ Implemented |
-| DrugBAN | Bilinear attention | Must have | 3 | ✅ Implemented |
-| Random Forest | Traditional ML | Should have | 3 | Planned |
-| XGBoost | Traditional ML | Should have | 3 | Planned |
-| DTI-LM | Language model-based | Nice to have | 3 | Planned |
-| EviDTI | Evidential/uncertainty | Nice to have | 3 | Planned |
-**Must-have ML: 9 baseline runs (3 models × 3 splits) + 6 Exp 1 (2 random conditions) + 3 Exp 4 (DDB split) = 18 total (~36-72 GPU-hours, 3-4 days)**
-> **Status (2026-03-13):** All 18/18 ML baseline runs COMPLETE on Cayuga HPC. Results in `results/baselines/`. 3 timed-out DrugBAN jobs recovered via `eval_checkpoint.py`. Key findings: degree-matched negatives inflate LogAUC by +0.112 avg; cold-target LogAUC drops to 0.15–0.33; DDB ≈ random (≤0.010 diff).
-**LLM Baselines (all free):**
-| Model | Access | Priority |
-|-------|--------|----------|
-| Gemini 2.5 Flash | Free API (250 RPD) | Must have |
-| Llama 3.3 70B | Ollama local | Must have |
-| Mistral 7B | Ollama local | Must have |
-| Phi-3.5 3.8B | Ollama local | Should have |
-| Qwen2.5 7B | Ollama local | Should have |
-**Must-have LLM: 3 models × 3 tasks (L1,L2,L4) × 2 configs (zero-shot, 3-shot) = 18 eval runs (all automated)**
-**Flagship models (post-stabilization):**
-- GPT-4/4.1, Claude Sonnet/Opus, Gemini Pro — added to leaderboard later
-**Must-have experiments (minimum for paper):**
-- [x] **Exp 1: NegBioDB vs. random negatives** ✅ COMPLETE — degree-matched avg +0.112 over negbiodb → benchmark inflation confirmed
-- [x] **Exp 4: Node degree bias** ✅ COMPLETE — DDB ≈ random (≤0.010 diff) → degree balancing alone not harder
-- [ ] **Exp 9: LLM vs. ML comparison** (L1 vs. M1 on matched test set — reuses baseline results; awaiting LLM runs)
-- [ ] **Exp 10: LLM extraction quality** (L2 entity F1 — awaiting LLM runs)
-**Should-have experiments (strengthen paper, no extra training):**
-- [ ] Exp 5: Cross-database consistency (analysis only, no training)
-- [ ] Exp 7: Target class coverage analysis (analysis only)
-- [ ] Exp 11: Prompt strategy comparison (add CoT config to LLM baselines)
-- [ ] L3 task + Exp 12: LLM-as-Judge reliability (1,530 judge calls = 6 days)
-**Nice-to-have experiments (defer to camera-ready):**
-- [ ] Exp 2: Confidence tier discrimination
-- [ ] Exp 3: Assay context dependency (with assay format stratification)
-- [ ] Exp 6: Temporal generalization
-- [ ] Exp 8: LIT-PCBA recapitulation
-### Week 8-10: Paper Writing
-- [ ] Write benchmark paper (**9 pages** + unlimited appendix)
-- [ ] Create key figures (see `paper/scripts/generate_figures.py`)
-- [ ] **Paper structure (9 pages)**: Intro (1.5) → DB Design (1.5) → Benchmark (1.5) → Experiments (3) → Discussion (1.5)
-- [ ] **Appendix contents**: Full schema DDL, all metric tables, L2 annotation details, few-shot examples, Datasheet
-- [ ] Python download script: `pip install negbiodb` or simple wget script
-- [ ] Host dataset (HuggingFace primary + Zenodo DOI for archival)
-- [ ] Author ethical statement
-- [ ] **Dockerfile** for full pipeline reproducibility: Python 3.11, rdkit, torch, chembl_downloader, pyarrow, mlcroissant. Must reproduce full pipeline from raw data → final benchmark export
-### Week 10-11: Review & Submit
-- [ ] Internal review and polish
-- [ ] Submit abstract (~May 1)
-- [ ] Submit full paper (~May 15)
-- [ ] Post ArXiv preprint (same day or before submission)
----
-## Phase 1-CT: Clinical Trial Failure Domain
-> Initiated: 2026-03-17 | Pipeline code + data loading complete, benchmark design complete
-### Step CT-1: Infrastructure ✅ COMPLETE
-- [x] CT schema design (2 migrations: 001 initial + 002 expert review fixes)
-- [x] 5 pipeline modules: etl_aact, etl_classify, drug_resolver, etl_outcomes, ct_db
-- [x] 138 tests passing
-- [x] Data download scripts for all 4 sources
-### Step CT-2: Data Loading ✅ COMPLETE
-- [x] AACT ETL: 216,987 trials, 476K trial-interventions, 372K trial-conditions
-- [x] Failure classification (3-tier): 132,925 results (bronze 60K / silver 28K / gold 23K / copper 20K)
-- [x] Open Targets: 32,782 intervention-target mappings
-- [x] Pair aggregation: 102,850 intervention-condition pairs
-### Step CT-3: Enrichment & Resolution ✅ COMPLETE
-- [x] Outcome enrichment: +66 AACT p-values, +31,969 Shi & Du SAE records
-- [x] Drug resolution Steps 1-2: ChEMBL exact (18K) + PubChem API
-- [x] Drug resolution Step 3: Fuzzy matching — 15,616 resolved
-- [x] Drug resolution Step 4: Manual overrides — 291 resolved (88 entries used)
-- [x] Pair aggregation refresh (post-resolution) — 102,850 pairs
-- [x] Post-run coverage analysis — 36,361/176,741 (20.6%) ChEMBL, 27,534 SMILES, 66,393 targets
-### Step CT-4: Analysis & Benchmark Design ✅ COMPLETE
-- [x] Data quality analysis script (`scripts_ct/analyze_ct_data.py`) — 16 queries, JSON+MD output
-- [x] Data quality report (`results/ct/ct_data_quality.md`)
-- [x] ML benchmark design
-  - 3 tasks: CT-M1 (binary), CT-M2 (7-way category), CT-M3 (phase transition, deferred)
-  - 6 split strategies, 3 models (XGBoost, MLP, GNN+Tabular)
-  - 3 experiments: negative source, generalization, temporal
-- [x] LLM benchmark design
-  - 4 levels: CT-L1 (5-way MCQ), CT-L2 (extraction), CT-L3 (reasoning), CT-L4 (discrimination)
-  - 5 models, anti-contamination analysis
-### Step CT-5: ML Export & Splits ✅ COMPLETE
-- [x] CT export module (`src/negbiodb_ct/ct_export.py`)
-- [x] CTO success trials extraction (CT-M1 positive class)
-- [x] Feature engineering (drug FP + mol properties + condition one-hot + trial design)
-- [x] 6 split strategies implementation
-### Step CT-6: ML Baseline Experiments ✅ COMPLETE (108/108 runs)
-- [x] XGBoost baseline (CT-M1 + CT-M2)
-- [x] MLP baseline
-- [x] GNN+Tabular baseline
-- [x] Key finding: CT-M1 trivially separable on NegBioDB negatives (AUROC=1.0); M2 XGBoost macro-F1=0.51
-### Step CT-7: LLM Benchmark Execution ✅ COMPLETE (80/80 runs)
-- [x] CT-L1/L2/L3/L4 dataset construction
-- [x] CT prompt templates + evaluation functions
-- [x] Inference runs on Cayuga HPC (5 models × 4 levels × 4 configs)
-- [x] Key finding: CT L4 MCC 0.48–0.56 — highest discrimination across domains
----
-## Phase 1b: Post-Submission Expansion (Months 3-6)
-### Data Expansion (if not at 10K+ for submission)
-- [ ] Complete PubChem BioAssay extraction (full confirmatory set)
-- [ ] LLM text mining pipeline activation (PubMed abstracts)
-- [ ] Supplementary materials table extraction (pilot)
-### Benchmark Refinement
-- [ ] Add remaining ML and LLM baseline models
-- [ ] Complete all 12 validation experiments (8 ML + 4 LLM)
-- [ ] Complete LLM tasks L5, L6 datasets
-- [ ] Add flagship LLM evaluations (GPT-4, Claude)
-- [ ] Build public leaderboard (simple GitHub-based, separate ML and LLM tracks)
----
-## Phase 2: Community & Platform (Months 6-18)
-### 2.1 Platform Development
-- [ ] Web interface (search, browse, download)
-- [ ] Python library: `pip install negbiodb`
-- [ ] REST API with tiered access
-- [ ] Community submission portal with controlled vocabularies
-- [ ] Leaderboard system
-### 2.2 Community Building
-- [ ] GitHub repository with documentation and tutorials
-- [ ] Partner with SGC and Target 2035/AIRCHECK for data access
-- [ ] Engage with DREAM challenge community
-- [ ] Tutorial at relevant workshop
-- [ ] Researcher incentive design (citation credit, DOI per submission)
----
-## Schema Design
-### Common Layer
-```
-NegativeResult {
-  id: UUID
-  compound_id: InChIKey + ChEMBL ID + PubChem CID
-  target_id: UniProt ID + ChEMBL Target ID
-  // Core negative result
-  result_type: ENUM [hard_negative, conditional_negative, methodological_negative,
-                     hypothesis_negative, dose_time_negative]
-  confidence_tier: ENUM [gold, silver, bronze, copper]
-  // Quantitative evidence
-  activity_value: FLOAT (IC50, Kd, Ki, EC50)
-  activity_unit: STRING
-  activity_type: STRING
-  pchembl_value: FLOAT
-  inactivity_threshold: FLOAT
-  max_concentration_tested: FLOAT
-  // Assay context (BAO-based)
-  assay_type: BAO term
-  assay_format: ENUM [biochemical, cell-based, in_vivo]
-  assay_technology: STRING
-  detection_method: STRING
-  cell_line: STRING (if cell-based)
-  organism: STRING
-  // Quality metrics
-  z_factor: FLOAT
-  ssmd: FLOAT
-  num_replicates: INT
-  screen_type: ENUM [primary_single_point, confirmatory_dose_response,
-                     counter_screen, orthogonal_assay]
-  // Provenance
-  source_db: STRING (PubChem, ChEMBL, literature, community)
-  source_id: STRING (assay ID, paper DOI)
-  extraction_method: ENUM [database_direct, text_mining, llm_extracted,
-                           community_submitted]
-  curator_validated: BOOLEAN
-  // Target context (DTO-based)
-  target_type: DTO term
-  target_family: STRING (kinase, GPCR, ion_channel, etc.)
-  target_development_level: ENUM [Tclin, Tchem, Tbio, Tdark]
-  // Metadata
-  created_at: TIMESTAMP
-  updated_at: TIMESTAMP
-  related_positive_results: [UUID] (links to known actives for same target)
-}
-```
-### Biology/DTI Domain Layer
-```
-DTIContext {
-  negative_result_id: UUID (FK)
-  binding_site: STRING (orthosteric, allosteric, unknown)
-  selectivity_data: BOOLEAN (part of selectivity panel?)
-  species_tested: STRING
-  counterpart_species_result: STRING (active in other species?)
-  cell_permeability_issue: BOOLEAN
-  compound_solubility: FLOAT
-  compound_stability: STRING
-}
-```
----
-## Benchmark Design (NegBioBench) — Dual ML + LLM Track
-### Track A: Traditional ML Tasks
-| Task | Input | Output | Primary Metric |
-|------|-------|--------|----------------|
-| **M1: DTI Binary Prediction** | (compound SMILES, target sequence) | Active / Inactive | LogAUC[0.001,0.1], AUPRC |
-| **M2: Negative Confidence Prediction** | (SMILES, sequence, assay features) | gold/silver/bronze/copper | Weighted F1, MCC |
-| **M3: Activity Value Regression** | (SMILES, sequence) | pIC50 / pKd | RMSE, R², Spearman ρ |
-**ML Baselines:** DeepDTA, GraphDTA, DrugBAN, RF, XGBoost, DTI-LM, EviDTI
-### Track B: LLM Tasks
-| Task | Input | Output | Metric | Eval Method |
-|------|-------|--------|--------|-------------|
-| **L1: Negative DTI Classification** | Natural language description | Active/Inactive/Inconclusive/Conditional (MCQ) | Accuracy, F1, MCC | Automated |
-| **L2: Negative Result Extraction** | Paper abstract | Structured JSON (compound, target, outcome) | Schema compliance, Entity F1, STED | Automated |
-| **L3: Inactivity Reasoning** | Confirmed negative + context | Scientific explanation | 4-dim rubric (accuracy, reasoning, completeness, specificity) | LLM-as-Judge + human sample |
-| **L4: Tested-vs-Untested Discrimination** | Compound-target pairs | Tested/Untested + evidence | Accuracy, F1, evidence quality | Automated + spot-check |
-| **L5: Assay Context Reasoning** | Negative result + condition changes | Prediction + reasoning per scenario | Prediction accuracy, reasoning quality | LLM-as-Judge |
-| **L6: Evidence Quality Assessment** | Negative result + metadata | Confidence tier + justification | Tier F1, justification quality | Automated + LLM-judge |
-**LLM Baselines (Phase 1 — Free):** Gemini 2.5 Flash, Llama 3.3, Mistral 7B, Phi-3.5, Qwen2.5
-**LLM Baselines (Phase 2 — Flagship):** GPT-4, Claude Sonnet/Opus, Gemini Pro
-**LLM-as-Judge:** Gemini 2.5 Flash free tier (validated against human annotations)
-### Track C: Cross-Track (Future)
-| Task | Description |
-|------|-------------|
-| **C1: Ensemble Prediction** | Combine ML model scores + LLM reasoning — does LLM improve ML? |
-### Splitting Strategies (7 total, for Track A)
-1. Random (stratified 70/10/20)
-2. Cold compound (Butina clustering on Murcko scaffolds)
-3. Cold target (by UniProt accession)
-4. Cold both (compound + target unseen)
-5. Temporal (train < 2020, val 2020-2022, test > 2022)
-6. Scaffold (Murcko scaffold cluster-based)
-7. DDB — Degree Distribution Balanced (addresses node degree bias)
-### Evaluation Metrics (Track A)
-| Metric | Type | Role |
-|--------|------|------|
-| **LogAUC[0.001,0.1]** | Enrichment | **Primary ranking metric** |
-| **BEDROC (α=20)** | Enrichment | Early enrichment |
-| **EF@1%, EF@5%** | Enrichment | Top-ranked performance |
-| **AUPRC** | Ranking | **Secondary ranking metric** |
-| **MCC** | Classification | Balanced classification |
-| **AUROC** | Ranking | Backward compatibility only (not for ranking) |
-### LLM Evaluation Configuration
-- **Full benchmark** (5 configs): zero-shot, 3-shot, 5-shot, CoT, CoT+3-shot
-- **Must-have** (2 configs): zero-shot, 3-shot only (see research/08 §3)
-- **Should-have** (add CoT): 3 configs total for Exp 11 (prompt strategy comparison)
-- 3 runs per evaluation, report mean ± std
-- Temperature = 0, prompts version-controlled
-- Anti-contamination: temporal holdout + paraphrased variants + contamination detection
----
-## Phase 3: Scale & Sustainability (Months 18-36)
-### 3.1 Data Expansion
-- [ ] Expand to 100K+ curated negative DTIs
-- [ ] Full LLM-based literature mining pipeline (PubMed/PMC)
-- [ ] Supplementary materials table extraction (Table Transformer)
-- [ ] Integrate Target 2035 AIRCHECK data as it becomes available
-- [ ] Begin Gene Function (KO/KD) negative data collection
-### 3.2 Benchmark Evolution (NegBioBench v1.0)
-- [ ] Track A expansion: multi-modal integration (protein structures, assay images)
-- [ ] Track B expansion: additional tasks — Failure Diagnosis, Experimental Design Critique, Literature Contradiction Detection
-- [ ] Track C: Cross-track ensemble evaluation (ML + LLM combined prediction)
-- [ ] Specialized bio-LLM evaluations (LlaSMol, BioMedGPT, DrugChat)
-- [ ] Regular leaderboard updates (both ML and LLM tracks)
----
-## Phase 4: Domain Expansion (Months 36+)
-```
-DTI (Phase 1 — COMPLETE)
-  │
-  ├── Clinical Trial Failure (Phase 1-CT — COMPLETE ✅)
-  │     └── 132,925 failure results loaded, benchmarks designed
-  │
-  ├── Gene Function (CRISPR KO/KD negatives)
-  │     └── Leverage CRISPR screen data, DepMap
-  │
-  ├── Chemistry Domain Layer
-  │     └── Failed reactions, yield = 0 data
-  │
-  └── Materials Science Domain Layer
-        └── HTEM DB integration, failed synthesis conditions
-```
----
-## Key Milestones (Revised)
-| Milestone | Target Date | Deliverable | Status |
-|-----------|------------|-------------|--------|
-| Schema v1.0 finalized | Week 2 (Mar 2026) | SQLite schema + standardization pipeline | ✅ Done |
-| Data extraction complete | Week 3-4 (Mar 2026) | **30.5M** negative results (far exceeded 10K target) | ✅ Done |
-| ML export & splits | Week 3 (Mar 2026) | 6 split strategies + M1 benchmark datasets | ✅ Done |
-| ML evaluation metrics | Week 3 (Mar 2026) | 7 metrics, 329 tests | ✅ Done |
-| ML baseline infrastructure | Week 4 (Mar 2026) | 3 models + SLURM harness | ✅ Done |
-| ML baseline experiments | Week 5 (Mar 2026) | 18/18 runs complete, key findings confirmed | ✅ Done |
-| LLM benchmark infrastructure | Week 5 (Mar 2026) | L1–L4 datasets, prompts, eval, SLURM templates | ✅ Done |
-| LLM benchmark execution | Week 5-6 (Mar 2026) | 81/81 runs complete (9 models × 4 tasks + configs) | ✅ Done |
-| Python library v0.1 | Month 8 | `pip install negbiodb` |
-| Web platform launch | Month 12 | Public access + leaderboard |
-| 100K+ entries | Month 24 | Scale milestone |
----
----
----

+# 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)