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Artificial intelligence in impurity prediction: current landscape, challenges, and future directions
Shreehari Thombre1, Chandrakant Bonde2, Ritesh Bhole3,4
1SSR College of Pharmacy, Silvassa, UT of DNH and DD, Silvassa, India. shrithombre0@gmai.com.
Journal of Computer-Aided Molecular Design
|July 22, 2026
Summary
Predicting pharmaceutical impurities early is crucial for drug quality and safety. Advanced computational methods, including machine learning and deep learning, offer faster, more accurate impurity detection than traditional techniques.
Area of Science:
- Pharmaceutical Science
- Computational Chemistry
- Drug Development
Background:
- Early identification and regulation of pharmaceutical impurities are vital for drug quality, safety, and regulatory approval.
- Traditional methods (LC-MS, NMR) are labor-intensive, time-consuming, and limited in detecting trace or unknown impurities.
- Advancements in cheminformatics and computational modeling now enable forecasting impurity formation before experimental analysis.
Purpose of the Study:
- To review advanced computational techniques for predicting impurities in pharmaceutical products.
- To highlight recent progress in machine learning and deep learning algorithms for impurity forecasting.
- To discuss the integration of these methods into a predictive and preventive impurity assessment strategy.
Main Methods:
- Exploration of advanced chemical representation techniques, data generation, and curation.
- Application and examination of traditional Quantitative Structure-Activity Relationship (QSAR) models.
- Investigation of contemporary deep learning models, including graph neural networks, transformer architectures, and generative frameworks.
Main Results:
- Computational methods, particularly machine learning and deep learning, show significant promise in predicting synthetic byproducts, degradation products, and contaminants.
- Explainable AI models are increasingly important for gaining regulatory body acceptance.
- Emerging techniques like digital twins and automated impurity profiling are transforming impurity assessment.
Conclusions:
- Computational approaches are shifting pharmaceutical impurity assessment from a reactive to a predictive and preventive paradigm.
- These advanced methods enhance the efficiency and accuracy of impurity identification throughout drug development.
- The integration of explainable AI and real-time monitoring promises to further revolutionize drug safety and quality control.
