Toward Comprehensive Scientific Information on Plastic-Related Chemicals Powered by Artificial Intelligence

Abstract

Understanding the core scientific information of plastic-related chemicals is critical for addressing chemical risks in plastic pollution. However, existing databases exhibit substantial information gaps in key dimensions such as chemical composition, toxicity, and functional properties. Bridging these gaps is essential for enabling robust scientific assessment and evidence-based policy making. This study establishes an integrated artificial intelligence (AI)-based technical framework to address these deficiencies. First, a large language model (LLM)-based workflow was developed to parse chemical composition data (resulting in a high-granularity dataset of 20,618 chemical entries), enhancing both granularity and coverage. Second, lightweight machine learning (ML) models were developed to efficiently impute missing toxicity labels for seven toxicity indicators. Third, fuzzy search (common sequence method) and exact search (rule-based additive identifier) methods were implemented to enable bottom-up identification of functional labels for plastic additives. Finally, the relationship between functional attributes and toxicity was systematically analyzed, offering new analytical perspectives and methodological support for identifying “chemicals of concern” among plastic additives. To establish an effective science–policy interface, sustained efforts from a broad range of stakeholders are still needed to enhance the development of high-quality data across the full life cycle of plastic-related chemicals.