ROADMAP.md

NegBioDB — Execution Roadmap

Last updated: 2026-03-30 (v19 — DTI ✅ CT ✅ PPI ✅ GE near-complete: ML seed 42 done, LLM 4/5 models done)

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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).

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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

-- 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

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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

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Phase 1: Implementation Sprint (Weeks 0-11)

Week 1: Scaffolding + Download + Schema ✅ COMPLETE

• Project scaffolding: Create src/negbiodb/, scripts/, tests/, migrations/, config.yaml, Makefile, pyproject.toml
• Dependency management: pyproject.toml with Python 3.11+, rdkit, pandas, pyarrow, mlcroissant, tqdm, scikit-learn
• Makefile skeleton: Define target structure (full pipeline encoding in Week 2)
• Finalize database schema (SQLite for MVP) — apply migrations/001_initial_schema.sql
• Download all source data (see below — < 1 day total)
• Verify ChEMBL v36 (Sep 2025) downloaded, not v35
• [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

• Implement compound standardization pipeline (RDKit: salt removal, normalization, InChIKey)
• Implement target standardization pipeline (UniProt accession as canonical ID)
• Set up cross-DB deduplication (InChIKey[0:14] connectivity layer)
• 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

• Download PubChem FTP files (bioactivities.tsv.gz + bioassays.tsv.gz + mapping files)
• Download ChEMBL v36 SQLite via chembl_downloader
• Download BindingDB bulk TSV
• Build PubChem FTP extraction script (streaming with chunksize=100K — 12GB uncompressed)
• Build ChEMBL extraction SQL: inactive (activity_comment + pChEMBL < 5) AND active (pChEMBL ≥ 6) for positive data
• Build BindingDB extraction script (filter Kd/Ki > 10 uM, human targets)
• Integrate DAVIS matrix from TDC (both actives pKd ≥ 7 and inactives pKd ≤ 5)
• Run compound/target standardization on all extracted data (multiprocessing for RDKit)
• Run cross-DB deduplication + overlap analysis (vs DAVIS, TDC, DUD-E, LIT-PCBA)
• Assign confidence tiers (gold/silver/bronze/copper — lowercase, matching DDL CHECK constraint)
• Extract ChEMBL positives: 883K → 863K after 21K overlap removal (pChEMBL ≥ 6, shared targets only)
• Positive-negative pairing: M1 balanced (1.73M, 1:1) + M1 realistic (9.49M, 1:10). Zero compound-target overlap verified.
• Borderline exclusion: pChEMBL 4.5–5.5 removed from both pools
• Spot-check top 100 most-duplicated compounds (manual QC checkpoint)
• Run data leakage check: cold split leaks = 0, cross-source overlaps documented

Week 3-5: Benchmark Construction (ML + LLM)

ML Track:

• Implement 3 must-have splits (Random, Cold-Compound, Cold-Target) + DDB for Exp 4
• Implement ML evaluation metrics: LogAUC[0.001,0.1], BEDROC, EF@1%, EF@5%, AUPRC, MCC, AUROC
• (Should have) Add Cold-Both, Temporal, Scaffold splits (all 6 implemented)

LLM Track: ✅ INFRASTRUCTURE COMPLETE (2026-03-12)

• Design prompt templates for L1, L2, L4 (priority tasks) → llm_prompts.py
• Construct L1 dataset: 2,000 MCQ from NegBioDB entries → build_l1_dataset.py
• Construct L2 dataset: 116 candidates (semi-automated) → build_l2_dataset.py
• Construct L4 dataset: 500 tested/untested pairs → build_l4_dataset.py
• Implement automated evaluation scripts → llm_eval.py (L1: accuracy/F1, L2: entity F1, L4: classification F1)
• Build compound name cache → compound_names.parquet (144,633 names from ChEMBL)
• Construct L3 dataset: 50 pilot reasoning examples → build_l3_dataset.py
• LLM client (vLLM + Gemini) → llm_client.py
• SLURM templates + batch submission → run_llm_local.slurm, run_llm_gemini.slurm, submit_llm_all.sh
• Results aggregation → collect_llm_results.py (Table 2)
• 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):

• Exp 1: NegBioDB vs. random negatives ✅ COMPLETE — degree-matched avg +0.112 over negbiodb → benchmark inflation confirmed
• 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)

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Phase 1-CT: Clinical Trial Failure Domain

Initiated: 2026-03-17 | Pipeline code + data loading complete, benchmark design complete

Step CT-1: Infrastructure ✅ COMPLETE

• CT schema design (2 migrations: 001 initial + 002 expert review fixes)
• 5 pipeline modules: etl_aact, etl_classify, drug_resolver, etl_outcomes, ct_db
• 138 tests passing
• Data download scripts for all 4 sources

Step CT-2: Data Loading ✅ COMPLETE

• AACT ETL: 216,987 trials, 476K trial-interventions, 372K trial-conditions
• Failure classification (3-tier): 132,925 results (bronze 60K / silver 28K / gold 23K / copper 20K)
• Open Targets: 32,782 intervention-target mappings
• Pair aggregation: 102,850 intervention-condition pairs

Step CT-3: Enrichment & Resolution ✅ COMPLETE

• Outcome enrichment: +66 AACT p-values, +31,969 Shi & Du SAE records
• Drug resolution Steps 1-2: ChEMBL exact (18K) + PubChem API
• Drug resolution Step 3: Fuzzy matching — 15,616 resolved
• Drug resolution Step 4: Manual overrides — 291 resolved (88 entries used)
• Pair aggregation refresh (post-resolution) — 102,850 pairs
• Post-run coverage analysis — 36,361/176,741 (20.6%) ChEMBL, 27,534 SMILES, 66,393 targets

Step CT-4: Analysis & Benchmark Design ✅ COMPLETE

• Data quality analysis script (scripts_ct/analyze_ct_data.py) — 16 queries, JSON+MD output
• Data quality report (results/ct/ct_data_quality.md)
• 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
• 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

• CT export module (src/negbiodb_ct/ct_export.py)
• CTO success trials extraction (CT-M1 positive class)
• Feature engineering (drug FP + mol properties + condition one-hot + trial design)
• 6 split strategies implementation

Step CT-6: ML Baseline Experiments ✅ COMPLETE (108/108 runs)

• XGBoost baseline (CT-M1 + CT-M2)
• MLP baseline
• GNN+Tabular baseline
• 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)

• CT-L1/L2/L3/L4 dataset construction
• CT prompt templates + evaluation functions
• Inference runs on Cayuga HPC (5 models × 4 levels × 4 configs)
• Key finding: CT L4 MCC 0.48–0.56 — highest discrimination across domains

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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)

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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)

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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
}

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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

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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)

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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

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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

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