计算机辅助药物设计 (CADD) 中的人工智能 寻找有力生物活性小分子的工具:传统到现代的方法
Benjamin Siddiqui1, Chandra Shekhar Yadav1,2, Mohd Akil1
1Department of Chemistry, Integral University, Lucknow, India.
Combinatorial chemistry & high throughput screening
|January 17, 2025
概括
人工智能 (AI) 正通过加速小分子设计来彻底改变药物发现. 由人工智能驱动的方法提高了效率,降低了成本,并改善了针对性治疗的开发.
科学领域:
- 计算化学是一种计算化学.
- 药品化学 药品化学 是一个
- 药物的发现和开发.
背景情况:
- 计算机辅助药物设计 (CADD) 是一个长期存在的领域,但计算方法在学术界和工业界正在获得显著的引力.
- 越来越多的分子数据,3D结构和绑定信息的可用性推动了AI在药物发现中的接受.
- 人工智能 (AI),生物信息学和数据科学是加速治疗开发,降低成本和尽量减少动物试验的关键驱动因素.
研究的目的:
- 审查人工智能驱动的药物发现和开发 (DDD) 的最新进展.
- 检查人工智能的潜力,以重塑整个DDD过程.
- 讨论人工智能在识别强大,针对特定目标的小分子连接体方面所带来的挑战和机遇.
主要方法:
- 关于AI在药物发现中的应用现有文献的综述.
- 分析AI在优化药理动力学,药理动力学和临床特性方面的作用.
- 使用虚拟图书馆和深度学习预测,探索人工智能驱动的连接物选.
主要成果:
- 人工智能正在成为药物发现的基本组成部分,特别是对于小分子.
- 人工智能加快了多样化,强效,针对特定目标和类似药物的配体的识别.
- 人工智能驱动的方法提高了开发新疗法的效率和成本效益.
结论:
- 人工智能显著重塑药物发现领域,为新药提供更快,更经济的途径.
- 人工智能通过优化分子特性和目标相互作用,促进了更安全,更有效的治疗方法的开发.
- 人工智能的持续进步有望进一步彻底改变制药行业和患者的治疗结果.
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