在药物查和优化方面,人工智能驱动的改进.
Adam Serghini1,2, Stephanie Portelli3, David B Ascher4,5
1School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia, QLD, Australia.
Methods in molecular biology (Clifton, N.J.)
|September 7, 2023
概括
人工智能 (AI) 通过预测化合物成功潜力来简化药物发现,减少昂贵的磨损. 本研究介绍了用于小分子开发的in silico工具,提高了制药管道中的效率和成本效益.
科学领域:
- 制药科学 制药科学
- 计算化学计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 药物发现中的高消耗率每年造成数十亿美元的损失.
- 确保所有开发阶段的安全性,质量和有效性至关重要.
- 精简对高潜能化合物的努力是提高效率的关键.
研究的目的:
- 为小分子开发提供in silico工具.
- 为了证明AI应用在增强药物发现管道中的应用.
- 为了降低消耗率和提高成本效益.
主要方法:
- 一系列in silico计算工具的开发.
- 人工智能的应用用于临床前查和物业优化.
- 在现有的制药开发工作流程中整合工具.
主要成果:
- 人工智能工具针对生物活性选和药物动力学优化.
- 早期毒性预测和基因查可以提高治疗寿命.
- 在 silico 工具的设计是为了无地集成到当前的管道.
结论:
- 人工智能提供了一种强大的方法来缓解药物发现的磨损.
- 呈现的in silico工具提高了效率和成本效益.
- 实施这些人工智能解决方案可以彻底改变小分子开发.
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