The provided sources explore the transformative role of artificial intelligence and transformer models in modern drug discovery and protein informatics. These technologies address the historical inefficiencies of pharmaceutical development by accelerating the design–make–test–analyse cycle and improving success probabilities. Key applications include using deep learning for protein structure prediction, identifying disease targets from multi-omic data, and the generative design of novel therapeutic molecules. Technical reviews highlight the shift from traditional methods to self-attention mechanisms that model complex biological relationships with unprecedented accuracy. While these advancements offer significant economic benefits, researchers emphasize that persistent challenges in data quality, model interpretability, and regulatory validation remain. Ultimately, the literature portrays AI as a foundational driver of innovation that is reshaping the global healthcare landscape.
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