Abstract

Embracing the potential of AI-driven pharmacology, this study addresses the challenge of bridging toxicity screening and drug efficacy predictions by leveraging a multi-task deep learning framework tailored for personalized medicine. We integrated patient genomic data with extensive chemical descriptors, employing attention-based interpretability modules to enhance model transparency and systematically evaluate both adverse effects and binding affinity within a single network architecture. Experimental results on real-world patient records and a curated compound library revealed a 12% increase in classification accuracy over traditional baselines, a mean squared error of 0.18 in affinity predictions, and clear functional group insights explaining toxicity risks. These findings suggest that a unified approach to pharmacological modeling can not only expedite drug development but also improve patient-specific outcomes, with implications for streamlined research pipelines and more effective precision therapies.

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