Datasets:

jang1563
/

NegBioDB

\section{Database Schema}
\label{app:schema}

NegBioDB uses three separate SQLite databases, one per domain, sharing common design patterns: WAL journal mode, foreign key enforcement, COALESCE-based deduplication indexes, and a four-tier confidence system (gold/silver/bronze/copper). Full DDL for all migrations is available in the repository. Below we summarize the key tables.

\subsection{DTI Domain Schema}

Two migrations: \texttt{001\_initial\_schema} (core tables) and \texttt{002\_target\_variants} (variant support).

\begin{small}
\begin{verbatim}
-- Core entity tables
compounds       (compound_id PK, canonical_smiles, inchikey UNIQUE,
                 inchikey_connectivity, pubchem_cid, chembl_id,
                 molecular_weight, logp, hbd, hba, tpsa, qed, ...)

targets         (target_id PK, uniprot_accession UNIQUE,
                 chembl_target_id, gene_symbol, target_family,
                 development_level CHECK IN (Tclin/Tchem/Tbio/Tdark), ...)

assays          (assay_id PK, source_db, source_assay_id,
                 assay_format CHECK IN (biochemical/cell-based/in_vivo),
                 screen_type, z_factor, pubmed_id, ...)

-- Core fact table (30.5M rows)
negative_results (result_id PK, compound_id FK, target_id FK, assay_id FK,
                  result_type CHECK IN (hard_negative/conditional_negative/
                    methodological_negative/dose_time_negative/
                    hypothesis_negative),
                  confidence_tier CHECK IN (gold/silver/bronze/copper),
                  activity_type, activity_value, pchembl_value,
                  source_db, source_record_id, extraction_method, ...)

-- Dedup: UNIQUE(compound_id, target_id, COALESCE(assay_id,-1),
--               source_db, source_record_id)

-- Aggregation (for ML export)
compound_target_pairs (pair_id PK, compound_id FK, target_id FK,
                       num_assays, num_sources, best_confidence,
                       compound_degree, target_degree, ...)

-- Variant support (migration 002)
target_variants (variant_id PK, target_id FK, variant_label,
                 source_db, UNIQUE(target_id, variant_label, ...))
\end{verbatim}
\end{small}

\subsection{CT Domain Schema}

Two migrations: \texttt{001\_ct\_initial\_schema} (core tables) and \texttt{002\_schema\_fixes} (expert review fixes).

\begin{small}
\begin{verbatim}
-- Entity tables
interventions   (intervention_id PK, intervention_type CHECK IN
                  (drug/biologic/device/...),
                 intervention_name, chembl_id, canonical_smiles,
                 inchikey, molecular_type, ...)

conditions      (condition_id PK, condition_name, mesh_id,
                 icd10_code, therapeutic_area, ...)

clinical_trials (trial_id PK, source_trial_id UNIQUE,
                 overall_status, trial_phase, enrollment_actual,
                 primary_endpoint, why_stopped,
                 termination_type CHECK IN (clinical_failure/
                   administrative/external_event/unknown), ...)

-- Core fact table (132,925 rows)
trial_failure_results (result_id PK, intervention_id FK,
                       condition_id FK, trial_id FK,
  failure_category CHECK IN (efficacy/safety/pharmacokinetic/
    enrollment/strategic/regulatory/design/other),
  confidence_tier CHECK IN (gold/silver/bronze/copper),
  p_value_primary, effect_size, serious_adverse_events,
  highest_phase_reached, result_interpretation CHECK IN
    (definitive_negative/inconclusive_underpowered/
     mixed_endpoints/futility_stopped/safety_stopped/
     administrative),
  source_db, extraction_method, ...)

-- Dedup: UNIQUE(intervention_id, condition_id,
--               COALESCE(trial_id,-1), source_db, source_record_id)

-- Junction tables
trial_interventions (trial_id FK, intervention_id FK, arm_role)
trial_conditions    (trial_id FK, condition_id FK)
intervention_targets (intervention_id FK, uniprot_accession, ...)
\end{verbatim}
\end{small}

\subsection{PPI Domain Schema}

Two migrations: \texttt{001\_ppi\_initial\_schema} (core tables) and \texttt{002\_llm\_annotations} (protein annotations for LLM benchmark).

\begin{small}
\begin{verbatim}
-- Entity table
proteins        (protein_id PK, uniprot_accession UNIQUE,
                 gene_symbol, amino_acid_sequence, sequence_length,
                 subcellular_location,
                 function_description, go_terms,
                 domain_annotations, ...)  -- migration 002

-- Core fact table (2.23M rows)
ppi_negative_results (result_id PK, protein1_id FK, protein2_id FK,
  experiment_id FK,
  evidence_type CHECK IN (experimental_non_interaction/
    ml_predicted_negative/low_score_negative/
    compartment_separated/literature_reported),
  confidence_tier CHECK IN (gold/silver/bronze/copper),
  interaction_score, detection_method,
  source_db, extraction_method, ...,
  CHECK (protein1_id < protein2_id))  -- canonical ordering

-- Dedup: UNIQUE(protein1_id, protein2_id,
--               COALESCE(experiment_id,-1),
--               source_db, source_record_id)

-- Aggregation
protein_protein_pairs (pair_id PK, protein1_id FK, protein2_id FK,
                       num_experiments, num_sources, best_confidence,
                       protein1_degree, protein2_degree, ...,
                       CHECK (protein1_id < protein2_id))

-- LLM support (migration 002)
ppi_publication_abstracts (pmid PK, title, abstract, ...)
\end{verbatim}
\end{small}

\subsection{Common Design Patterns}

\begin{itemize}[nosep,leftmargin=*]
    \item \textbf{Deduplication:} All fact tables use \texttt{COALESCE(fk, -1)} in UNIQUE indexes to handle NULL foreign keys (SQLite treats NULLs as distinct in UNIQUE constraints).
    \item \textbf{Confidence tiers:} Four-level system across all domains: gold (systematic screens, multiple confirmations) $>$ silver (ML-derived, p-value based) $>$ bronze (computational, NLP-detected) $>$ copper (label-only).
    \item \textbf{Aggregation tables:} Pre-computed pair-level statistics for ML export, avoiding expensive JOINs during dataset construction.
    \item \textbf{Symmetric pairs (PPI):} \texttt{CHECK (protein1\_id $<$ protein2\_id)} enforces canonical ordering, preventing duplicate pair representations.
    \item \textbf{Schema migrations:} All databases track applied migrations in a \texttt{schema\_migrations} table for reproducible upgrades.
\end{itemize}