在酸设计中:人工智能的方法,资源和作用
Priyanka Ray Choudhury1, Sai Kumar Mishra1, Siddharth Yadav1
1Amity Institute of Biotechnology, Amity University Uttar Pradesh, Noida, UP, India.
概括
计算方法通过探索广的化学空间和虚拟选候选人来加速的设计. 像机器学习 (ML) 这样的先进技术克服了传统合成和治疗查方面的局限性.
科学领域:
- 生物化学和分子生物学
- 计算化学的计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 在生物系统,疗法和生物材料中至关重要.
- 传统的酸设计面临着诸如降解,生物可用性差,合成/选效率低下等挑战.
- 计算方法为的探索和发现提供了强大的替代方案.
研究的目的:
- 为设计提供计算策略的全面审查.
- 突出专业数据库,工具和先进方法的作用.
- 讨论机器学习和人工智能在加速发现方面的影响.
主要方法:
- 基于结构的设计设计.
- 分子动力学 (MD) 模拟
- 基于连接体的方法.
- 机器学习 (ML) 和深度学习 (DL) 模型.
- 生成型的人工智能 (AI)
主要成果:
- 计算方法可以探索大型化学空间,并对众多进行虚拟选.
- 机器学习,深度学习和生成人工智能模型显著加快了新的发现.
- 专门的基数据库和先进的计算工具对于有效的设计至关重要.
结论:
- 计算设计,特别是人工智能驱动的方法,为克服类研究的局限性提供了一个变革性的路线图.
- 关键的挑战包括数据不一致,模型可解释性,以及需要改进的力场.
- 利用这些计算进步对于未来的疗法和生物材料至关重要.
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