在药物发现中释放生成性AI的力量
Amit Gangwal1, Antonio Lavecchia2
1Department of Natural Product Chemistry, Shri Vile Parle Kelavani Mandal's Institute of Pharmacy, Dhule 424001, Maharashtra, India.
Drug discovery today
|April 25, 2024
概括
人工智能 (AI) 使用深度生成模型 (DGM) 加速药物发现,用于新药设计. 本综述探讨了DGM,它们的挑战,以及未来的优化,以实现更快,更具成本效益的开发.
科学领域:
- 计算化学和化学信息学
- 药理学和制药科学 药理学和制药科学
- 人工智能和机器学习
背景情况:
- 人工智能 (AI) 正在改变制药研究和开发.
- 传统药物发现往往是漫长的,昂贵的,并且失败率很高.
- 人工智能驱动的方法,特别是计算机辅助药物设计,提供了显著的改进.
研究的目的:
- 审查用于新药设计的深度生成模型 (DGM) 的最新进展.
- 分析DGM在药物发现中的影响,挑战和潜力.
- 为优化DGM提出战略和未来前景.
主要方法:
- 专注于深度生成模型 (DGM) 及其多样化的算法.
- 在药物设计中对人工智能相关挑战进行批判性分析.
- 包括关于人工智能辅助药物开发成功和失败的案例研究.
主要成果:
- DGM在提高药物发现的精度和缩短药物发现时间表方面显示出显著的希望.
- 在DGM中,各种算法已经证明了对de novo设计的各种能力.
- 人工智能协助导致了成功和不成功的结果,使药物进入临床试验.
结论:
- 深度生成模型正在彻底改变新的药物设计.
- 应对当前的挑战对于释放DGM的全部潜力至关重要.
- 优化DGM将促进更快,更具成本效益的药物开发.
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