Leveraging AI and Big Data for Drug Development: Transforming Pharmaceutical R&D

Abstract

Drug development is expensive, complicated, and plagued by high failure rates, averaging over $2.6 billion for each approved drug. This study assessed advanced big data and AI methodologies to reduce the time and cost of drug discovery with higher accuracy. Multiple AI approaches were designed and trained, including CNNs for drug-target interactions, GANs for generating de novo molecules, reinforcement learning (RL) to traverse chemical spaces, and federated learning and explainable AI (XAI) for transparency while maintaining privacy, all using pre-curated multi-omics, electronic health record (EHR), and clinical trial data sets. The comparative analysis showed that CNNs had a drug-target prediction accuracy of 92% and a toxicity prediction accuracy of 87%, and GANs showed a 91 % novel compound generation rate. RL model's accuracy was evenly distributed across metrics, while federated learning + XAI only produced 75% interpretability/explainability but maintained user data privacy. All AI approaches outperformed the traditional Random Forest baseline with at least a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 2 \%}$</tex> improvement on key metrics. The authors propose that integrating the explainable AI framework with privacy-preserving AI models will significantly reduce drug development timelines while improving the trust users have in AI models, and overall potential to revolutionise pharmaceutical research.

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