PREDICTION OF API IMPURITIES USING ARTIFICIAL INTELLIGENCE

Ms. Dhanashri Bhonde; Dr. S.G.Jawarkar; Ms. Eshika Deshmukh; Ms. Diksha Patil

PREDICTION OF API IMPURITIES USING ARTIFICIAL INTELLIGENCE

February 2026

Vol-12, Issue-1

Paper ID: 28097

ISSN: 2395-4396

Downloads: 0

Abstract & Details

Research Area

pharmacy

Keywords

Impurities API QSAR Machine learning AI ICH guidelines Impurity profiling Impurity prediction

Abstract

Controlling impurities in Active Pharmaceutical Ingredients (APIs) remains a key challenge due to complex synthesis routes, diverse contamination sources, and limitations of traditional analytical methods. With increasing demand for safer medicines, Artificial Intelligence (AI) is becoming a valuable tool for predicting, identifying, and monitoring impurities more efficiently. Techniques such as machine learning, deep learning, QSAR modeling, and reaction-prediction algorithms are now integrated with analytical platforms like HPLC, LC–MS, GC–MS, NMR, and PAT to improve impurity profiling. AI can analyze large datasets, detect hidden patterns, and predict degradation pathways and genotoxic risks in advance. Applications including retention-time prediction, ICH M7-based mutagenicity assessment, and AI-assisted stability studies demonstrate its growing practicality. Despite challenges such as limited data quality, model transparency, and regulatory adaptation, the integration of AI with automation and digital technologies offers strong potential for real-time impurity control and improved quality-by-design approaches in pharmaceutical development.

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Author Information

#	Name	Institute / Affiliation
1	Ms. Dhanashri Bhonde	Vidyabharati college of pharmacy, Amravati
2	Dr. S.G.Jawarkar	Vidyabharati college of pharmacy, Amravati
3	Ms. Eshika Deshmukh	Vidyabharati college of pharmacy, Amravati
4	Ms. Diksha Patil	Vidyabharati college of pharmacy, Amravati

How to Cite

Use the following formats to cite this article in your research.

APA Style

Bhonde, Ms. Dhanashri, S.G.Jawarkar, Dr., Deshmukh, Ms. Eshika, & Patil, Ms. Diksha (2026). PREDICTION OF API IMPURITIES USING ARTIFICIAL INTELLIGENCE. International Journal of Advance Research and Innovative Ideas In Education, 12(1), 1208-1220.

MLA Style

Bhonde, Ms. Dhanashri, et al. "PREDICTION OF API IMPURITIES USING ARTIFICIAL INTELLIGENCE." International Journal of Advance Research and Innovative Ideas In Education, vol. 12, no. 1, 2026, pp. 1208-1220.

IEEE Style

Ms. Dhanashri Bhonde, Dr. S.G.Jawarkar, Ms. Eshika Deshmukh, and Ms. Diksha Patil, "PREDICTION OF API IMPURITIES USING ARTIFICIAL INTELLIGENCE," International Journal of Advance Research and Innovative Ideas In Education, vol. 12, no. 1, pp. 1208-1220, 2026.

Vancouver Style

Bhonde Ms. Dhanashri, S.G.Jawarkar Dr., Deshmukh Ms. Eshika, Patil Ms. Diksha. PREDICTION OF API IMPURITIES USING ARTIFICIAL INTELLIGENCE. International Journal of Advance Research and Innovative Ideas In Education. 2026;12(1):1208-1220.

Harvard Style

Bhonde, Ms. Dhanashri, S.G.Jawarkar, Dr., Deshmukh, Ms. Eshika, & Patil, Ms. Diksha (2026) 'PREDICTION OF API IMPURITIES USING ARTIFICIAL INTELLIGENCE', International Journal of Advance Research and Innovative Ideas In Education, 12(1), pp. 1208-1220.

Chicago Style

Bhonde, Ms. Dhanashri, et al. "PREDICTION OF API IMPURITIES USING ARTIFICIAL INTELLIGENCE." International Journal of Advance Research and Innovative Ideas In Education 12, no. 1 (2026): 1208-1220.

Turabian Style

Bhonde, Ms. Dhanashri, et al. "PREDICTION OF API IMPURITIES USING ARTIFICIAL INTELLIGENCE." International Journal of Advance Research and Innovative Ideas In Education 12, no. 1 (2026): 1208-1220.