AI ALGORITHMS FOR ADVANCED PHARMACEUTICAL DRUG ANALYSIS AND QUALITY CONTROL

Vanitha Devi Rajendran, Pavithra Ravikumar, Guhan Ramalingam, Maruthachalam Chinnaiah,
Balakumar Palanivelu and Vinoth Jeevanesan*

ABSTRACT

The integration of Artificial Intelligence (AI) into pharmaceutical drug analysis and quality control (QC) is transforming conventional practices by enabling predictive, real-time and highly automated systems. While traditional analytical methods remain foundational, they face increasing challenges due to the complexity of modern datasets, evolving regulatory standards and the demand for accelerated decision-making. AI algorithms, such as deep neural networks, random forests and graph neural networks, have demonstrated significant advancements in spectral interpretation, chromatographic analysis, image-based defect detection and stability prediction. This review presents a comprehensive analysis of current and emerging AI models applied across various domains of pharmaceutical QC. Platforms including Aizon, Mestrelab Mnova AI and ChemOS utilize spectral data to rapidly identify polymorphs and impurities. Chromatographic and mass spectrometric analyses are increasingly automated and enhanced by tools such as ChromGenius AI™, Empower™ 3 and MolDiscovery. Shelf-life prediction systems, including Stability.AI and ValGenesis iRisk™, offer robust modelling capabilities, while QualiVision and DeepInspect lead in image-based inspection of solid dosage forms. Additionally, innovations from Seeq + AWS ML, AstraZeneca’s DPK models and Atomwise exemplify AI’s growing role in real-time monitoring and predictive quality assurance. The convergence of AI with Quality by Design (QbD), Process Analytical Technology (PAT) and Real-Time Release Testing (RTRT) is not only improving regulatory compliance but also enhancing manufacturing efficiency. As the pharmaceutical industry embraces digital transformation, AI stands as a cornerstone of next-generation analytical and quality paradigms.

Keywords: Artificial Intelligence, Drug Analysis, Quality Control, Machine Learning, Spectroscopy, Predictive Modelling.