<![CDATA[Artificial Intelligence (AI) is significantly transforming drug discovery by enhancing efficiency and reducing costs. Traditional drug development has been slow and expensive, but AI's integration accelerates the process by predicting molecular interactions, identifying drug candidates, and optimizing formulations. Recent advancements highlight AI's role in molecular interaction prediction, target identification, lead optimization, and toxicity prediction. AI models, particularly deep learning algorithms, improve drug efficacy predictions and streamline virtual screening. They also address challenges in toxicity prediction by analyzing historical data to foresee adverse reactions, thus reducing late-stage failures. Despite its potential, AI faces challenges such as data quality and model interpretability. Future developments include advancements in explainable AI and the integration with personalized medicine, promising a revolution in creating more effective, tailored treatments while minimizing side effects. This short communication emphasizes AI's growing impact and the transformative opportunities it presents in modern medicine.]]>
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