A review on AI-enabled drug design in medicinal chemistry: Analytical validation, challenges, and regulatory...

Artificial intelligence, particularly machine learning, has profoundly reshaped drug discovery, addressing longstanding challenges such as exorbitant costs and protracted timelines. Conventional approaches often exceed $2.6 billion per drug over 12-15 years, with attrition rates nearing 90%; AI mitigates these through advanced target identification, high-throughput virtual screening, and generative molecule design. The present review synthesizes pivotal studies of several years drawn from PubMed, Scopus, and leading journals, including ACS Omega and Nature Reviews. It encompasses supervised quantitative structure-activity relationship models, neural networks, graph convolutional networks, and generative adversarial networks for de novo drug design. Emphasis is placed on machine learning's capacity to process vast omics and cheminformatics datasets, with critical attention to how data quality, measurement uncertainty, and analytical method variability fundamentally constrain predictive accuracy. In practice, AI empowers scientists by automating hypothesis generation, exemplified by AlphaFold's structural predictions and enabling early toxicity forecasting or drug repurposing, yet these computational advances remain dependent on rigorous experimental validation through orthogonal analytical techniques. A distinctive contribution of this review lies in its systematic integration of analytical chemistry as the foundational discipline underpinning reliable AI predictions. We present a conceptual framework, the Analytical Integrity Spectrum, that traces the bidirectional relationship between analytical measurements and computational models, emphasizing how measurement uncertainty, data quality, and experimental validation collectively determine the trustworthiness of AI-driven discoveries. The chemistry-focused synthesis distinguishes the present work from computational reviews by critically examining representative case studies of AI-discovered compounds, including their molecular structures, scaffolds, and experimental outcomes. This review provides a tangible assessment of AI's impact on medicinal chemistry. The review further examines AI's emerging application to climate-resilient supply chains, forecasting disruptions from environmental events while emphasizing the analytical monitoring essential for maintaining pharmaceutical quality during transport. Persistent challenges, including dataset biases, activity cliff insensitivity, and validation uncertainty, are traced to their analytical origins. Future prospects encompass federated learning, quantum-accelerated simulations, and standardized analytical data formats that preserve measurement integrity for machine learning. Ultimately, AI equips researchers with transformative tools for accelerated, equitable therapeutic innovation, provided that computational predictions remain grounded in the experimental reality that analytical chemistry provides.