在药物发现中整合QSAR建模和深度学习:深度QSAR的出现
Alexander Tropsha1, Olexandr Isayev2, Alexandre Varnek3
1University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. alex_tropsha@unc.edu.
深度定量结构-活动关系 (QSAR) 建模利用人工智能来设计药物. 进步包括生成模型,合成规划和虚拟查,加速新药发现.
科学领域:
- 计算化学是一种计算化学.
- 人工智能在药物发现中的作用
- 药品化学 药品化学 是一个
背景情况:
- 六十年来,定量结构-活性关系 (QSAR) 建模一直是计算机辅助药物设计的基石.
- 人工智能 (AI),计算能力和分子数据库的近期进步推动了QSAR进入一个新的时代,称为"深度QSAR".
研究的目的:
- 审查过去十年深度QSAR应用的关键进展.
- 突出深度学习对分子设计,合成规划和虚拟选的影响.
- 讨论量子计算和开源资源在加速药物发现方面的未来潜力.
主要方法:
- 关于QSAR中的深度学习应用的最新文献的审查.
- 对分子设计的深度生成和强化学习的分析.
- 检查用于合成规划和基于结构的虚拟选的深度学习模型.
主要成果:
- 深度QSAR模型在分子设计和属性预测方面取得了重大进展.
- 人工智能驱动的方法提高了合成规划和虚拟选过程的效率.
- 像量子计算这样的新兴技术准备进一步彻底改变深层QSAR.
结论:
- 深度QSAR代表了计算机辅助药物设计的重大演变,由AI驱动.
- 持续开发和对资源的开放获取对于最大限度地发挥深层QSAR的潜力至关重要.
- 量子计算的整合有望在药物发现管道中取得前所未有的加速.
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