The integration of computational models and AI/ML in early-stage drug formulation is revolutionizing pharmaceutical development, addressing the 90% of drug candidates facing solubility issues. Advanced AI techniques, including molecular dynamics simulations, help predict optimal solubilization methods—like amorphous solid dispersions—by analyzing drug-excipient interactions and minimizing empirical trials. Furthermore, AI-powered digital twins enhance preclinical evaluations, utilizing extensive ex-vivo data to simulate organ responses with over 90% accuracy. This enables personalized counterfactual analyses, revealing overlooked therapeutic effects while streamlining studies. In bioanalytics and manufacturing, AI boosts quality and efficiency, automating report writing, quality checks, and optimizing assay conditions. However, barriers such as the need for data digitalization, high-quality data availability, and regulatory challenges hinder broader AI adoption in pharmaceuticals. Emerging regulatory frameworks emphasize risk assessments to ensure AI models maintain drug quality and safety, signaling the industry’s cautious approach to AI integration.
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