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| Format: | Recurso digital |
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Zenodo
2025
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| Online-Zugang: | https://doi.org/10.5281/zenodo.17672148 |
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Inhaltsangabe:
- <p><span>Processes that were once time-consuming, costly, and inefficient. Integrating Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP) enables rapid identification of drug targets, prediction of pharmacokinetic and toxicological properties, and optimization of lead compounds. AI-driven models facilitate de novo drug design, virtual screening, biomarker discovery, and drug repurposing, thereby enhancing precision and reducing failure rates in preclinical and clinical phases. Additionally, AI supports personalized medicine by analyzing genomic and clinical data to tailor therapies. Despite its transformative potential, challenges such as data bias, limited transparency, and ethical and regulatory concerns remain. Addressing these issues through interdisciplinary collaboration and robust data governance is vital. Overall, AI continues to reshape the pharmaceutical landscape by improving efficiency, accuracy, and innovation in developing safer and more effective therapeutics. </span></p>