Automation Workflow for AI Model Governance and Version Control in R&D

Manual model documentation for FDA or EMA submissions is error-prone and slow. This workflow automates the creation of an immutable audit trail for every model—capturing lineage, training data, hyperparameters, and validation results—ensuring submission packages are complete, consistent, and inspection-ready. This cuts the manual compilation effort from weeks to days and drastically reduces the risk of costly regulatory queries or delays.

Gated approvals for moving models from development to validation to production create bottlenecks. The workflow automates approval routing based on pre-defined criteria, integrates with electronic document management systems (e.g., Veeva Vault), and provides stakeholders with a single dashboard for review. This eliminates email chains and manual checklists, shrinking model promotion cycles and getting validated AI into the discovery pipeline faster.

Preparing for internal or external audits of AI models typically requires scrambling through disparate logs, notebooks, and spreadsheets. This workflow serves as a single source of truth, where every model decision and change is automatically recorded and indexed. Auditors can be granted self-service access to trace any model output back to its source data and configuration, turning a multi-person-week effort into a query executed in minutes.

Fear of regulatory exposure often limits AI use to non-GxP research. This governance layer provides the controlled environment required for GLP/GMP contexts. It enforces SOPs, manages electronic signatures (21 CFR Part 11 compliant), and ensures all AI-driven decisions supporting candidate selection or trial design are fully traceable. This unlocks the use of high-throughput AI for critical path activities without introducing compliance risk.

Beyond compliance, the aggregated, structured metadata on thousands of model versions becomes a powerful asset. Data scientists can analyze what hyperparameters worked best for specific target classes. Portfolio managers can assess model performance trends. This transforms governance from a cost center into a knowledge base that actively improves future model development and portfolio strategy.

The architecture is built on an orchestration layer (e.g., LangGraph) that triggers governance agents upon model registration. Agents extract metadata, run validation checks, and post artifacts to a versioned registry (e.g., MLflow, DVC). Approval workflows are managed via integrated platforms like ServiceNow or Jira, with final, signed records pushed to an EDMS like Veeva or Documentum. Monitoring agents track for data drift and trigger re-validation cycles.

This agent automatically captures and logs the complete lineage of every model used in discovery: the exact training data version, hyperparameters, code commits, and the computational environment (e.g., Docker image hash). It creates an immutable, timestamped record in a tamper-evident ledger (like a blockchain-based registry or a write-once database). This is the foundational audit trail required for regulatory inspection, enabling teams to reproduce any model's training run and defend its use in a submission.

An orchestrated validation suite that runs automatically upon model creation or update. It executes a battery of tests: statistical performance against hold-out sets, bias detection across protected variables, and robustness checks against data drift. Results are compared against predefined acceptance criteria (SOPs). The agent routes models that pass to the next approval stage and flags failures for human review, preventing non-compliant models from entering the production discovery pipeline.

This component integrates the governance workflow with electronic document management systems (EDMS) like Veeva Vault or Documentum. It automatically generates model validation reports, change control documents, and summary data sheets in the required format. Upon final approval, it publishes the finalized, version-controlled documents to the designated regulatory folders within the EDMS, ensuring the AI asset's documentation is inspection-ready and aligned with submission-ready artifacts.

The central orchestration layer (built on tools like LangGraph or Camunda) that manages the multi-stage approval process. It defines the routing logic based on model risk classification: a high-impact target identification model may require sign-off from Computational Science, Therapeutic Area Head, and Quality Assurance, while a lower-risk filtering agent may only need lead scientist approval. The orchestrator manages task assignments, sends notifications, escalates delays, and enforces segregation of duties, creating a defensible, traceable decision chain.

A persistent monitoring agent that tracks deployed models in the discovery environment. It analyzes prediction distributions over time, comparing them to the validation baseline to detect concept or data drift. It also monitors for performance degradation against new experimental data. Upon detecting significant drift, it automatically creates an incident ticket in the quality management system (QMS), triggers a retraining pipeline, or downgrades the model's status to 'under review,' ensuring models remain valid as chemical space and project data evolve.

This agent manages deviations and exceptions from the standard governance path. If a model fails validation but is needed for a critical, time-sensitive experiment, it routes a formal risk-based justification for a temporary 'use-as-is' authorization to the appropriate governance board. It logs all such exceptions with full context and mandates a defined review period. This component balances strict compliance with operational pragmatism, ensuring flexibility is managed, documented, and does not compromise the overall integrity of the system.