The Continually Learning QSAR System under Chemical-Space Drift, Endpoint Redefinition, Model Change, and Requalification throughout Deployment
Year: 2026
Type: article
Abstract: Quantitative structure–activity relationship models are commonly developed as bounded predictive artefacts, yet their evidential conditions may continue to change after deployment. Incoming compounds can occupy unfamiliar chemical space, assay procedures and biological contexts can alter endpoint meaning, labels can be corrected or reinterpreted, molecular representations and software environments can change, and prediction consequences can expand beyond the original context of use. Treating the
DOI: 10.51847/ajdbgzrfdc
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7203690907