Predicting Chemical End-of-Life Scenarios Using Structure-Based Classification Models.

ABSTRACT: Analyzing chemicals and their effects on the environment from a life cycle viewpoint can produce a thorough analysis that takes end-of-life (EoL) activities into account. Chemical risk assessment, predicting environmental discharges, and finding EoL paths and exposure scenarios all depend on chemical flow data availability. However, it is challenging to gain access to such data and systematically determine EoL activities and potential chemical exposure scenarios. As a result, this work creates quantitative structure-transfer relationship (QSTR) models for aiding environmental managment decision-making based on chemical structure-based machine learning (ML) models to predict potential industrial EoL activities, chemical flow allocation, environmental releases, and exposure routes. Further m

SUBMITTER: Hernandez-Betancur JD 

PROVIDER: S-EPMC9993395 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature