Study and modeling by artificial intelligence of occupational exposure limits to certain pharmaceutical products using chemoinformatics

Abstract

Artificial intelligence is increasingly being utilized to all field and specially to enhance occupational health within the pharmaceutical industry, where the rising potency of compounds contributes to elevated exposure risks. Due to limited toxicity data for many emerging drug candidates, traditional methods often fall short in accurately estimating safe handling thresholds. In this study, an AI-driven approach has been developed to predict Occupational Exposure Bands (OEBs) based on molecular structures. This method combines cheminformatics descriptors and molecular fingerprints with deep learning techniques to extract significant features, which are then classified using various machine learning algorithms. The resulting models exhibit strong predictive performance, although challenges remain in accurately identifying less-represented high-risk categories. Additionally, a practical software tool was created to facilitate real-time OEB predictions and molecular visualization, providing an accessible interface for researchers and safety professionals. Overall, this approach offers an innovative solution for early hazard assessment and highlights the potential of AI to improve workplace safety in pharmaceutical development.