人工智能和化学信息学工具:对药物开发和化学科学的贡献
Ifra Saifi1, Basharat Ahmad Bhat2, Syed Suhail Hamdani2
1Chaudhary Charan Singh University, Meerut, Uttar Pradesh, India.
Journal of biomolecular structure & dynamics
|July 12, 2023
概括
人工智能 (AI) 和机器学习 (ML) 与化学信息学相结合,加速了药物发现. 本综述强调了从2021-2022年开始的新AI/ML化学信息资源,用于开发新疗法.
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
- 药物发现 药物发现
背景情况:
- 化学信息学整合了计算机科学和化学,用于化学数据分析.
- 人工智能和机器学习通过识别命中药物,优化合成和预测药物特性来增强药物发现.
- 人工智能,机器学习和化学信息学的协同作用已经为70多种药物批准做出了贡献.
研究的目的:
- 呈现一个精心策划的新型化学信息学资源列表.
- 支持计算机辅助药物开发的研究人员.
- 为突出AI/ML应用在药物发现中的进步.
主要方法:
- 汇编数据库,数据集,预测和生成模型.
- 包括2021年至2022年间推出的评分功能和网络平台.
- 专注于与化学信息学中的AI和ML相关的资源.
主要成果:
- 对药物发现研究人员的新工具和数据的全面概述.
- 确定促进人工智能驱动药物开发的关键资源.
- 展示了该领域的快速发展,以及最近的技术进步.
结论:
- 人工智能,机器学习和化学信息学的整合显著推进了药物发现.
- 新可用的资源为识别和开发新药提供了有价值的工具.
- 持续的进步有望在制药研究中取得进一步的突破.
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