Integrating AI into environmental chemistry: predicting and preventing toxicity in chemical products

This review critically examines the current state of AI in environmental chemistry, highlighting both its potential and limitations in predicting and preventing toxicity in chemical products.

Integrating Artificial Intelligence (AI) into environmental chemistry for predicting and preventing toxicity in chemical products is an emerging field that
promises to revolutionize toxicological assessments and sustainable chemical design.

AI techniques, particularly machine learning (ML) and deep learning (DL), offer the potential to predict the toxicity of chemical substances rapidly, reducing reliance on traditional experimental methods that are often time-consuming, expensive, and ethically problematic.

By utilizing large datasets on chemical properties, molecular structures and biological effects, AI models can forecast the environmental and health impacts of chemicals at an early stage, enabling more efficient risk assessments.

However, there are several critical challenges and limitations to consider. The accuracy of AI predictions is dependent on the availability of high-quality, comprehensive data, which is often lacking, especially for new or untested chemicals. Furthermore, the interpretability of AI models remains a significant issue, as many models function as "black boxes," making it difficult to understand the rationale behind their predictions. This lack of transparency may hinder trust in AI-driven decision-making. Additionally, AI tools may lead to biases if the data is incomplete.

Despite these challenges, AI presents opportunities for the design of greener chemicals by optimizing molecular structures to reduce environmental harm and enhance biodegradability. However, integrating AI into environmental chemistry requires careful responsible use of technology and consideration of ethical, legal and regulatory frameworks to ensure the responsible use of technology.