In regulated R&D, every AI model used for virtual screening or molecular design must have a defensible lineage. Manual tracking of training data, hyperparameters, and validation results is error-prone and fails audit scrutiny. This workflow automates the capture of all model artifacts—from initial training jobs in cloud HPC to performance validations against holdout sets—into a versioned, immutable ledger. It directly addresses the governance bottleneck that stalls AI deployment in GxP or quality-critical contexts, turning compliance from a manual burden into an automated byproduct of the model lifecycle.




