计算机辅助药物设计和药物发现:前性分析
Sarfaraz K Niazi1, Zamara Mariam2
1College of Pharmacy, University of Illinois, Chicago, IL 60012, USA.
Pharmaceuticals (Basel, Switzerland)
|January 23, 2024
概括
计算机辅助药物设计 (CADD) 通过整合生物学和技术,彻底改变了药物发现. 人工智能,个性化医疗和新兴技术的进步承诺加速,道德的治疗解决方案.
科学领域:
- 计算化学和化学信息学
- 药理学和药物开发领域
- 生物信息学和计算生物学
背景情况:
- 计算机辅助药物设计 (CADD) 是现代药物发现的一个关键方法.
- 它将计算方法与生物和化学数据相结合.
- CADD加速了候选药物的识别和优化.
研究的目的:
- 为计算机辅助药物设计 (CADD) 提供全面的概述.
- 讨论CADD在药物发现中的历史演变,分类和作用.
- 探索CADD当前的挑战,进展和未来的方向.
主要方法:
- 对CADD方法 (基于结构和基于连接体) 的历史发展和分类进行审查.
- 在CADD中分析机器学习 (ML) 和人工智能 (AI) 的集成.
- 探索影响CADD的新兴技术 (量子计算,沉浸式技术,绿色化学).
主要成果:
- CADD显著合理化和加快了药物发现过程.
- 结合ML/AI增强了预测能力,但也引发了伦理和可扩展性的担忧.
- 协作平台和个性化医疗整合显示了民主化和定制药物发现的前景.
结论:
- 解决数据隐私,算法优化和道德框架对于CADD进步至关重要.
- 导航人工智能偏见,可持续性和可访问性对于未来的CADD开发至关重要.
- 积极参与伦理,技术和教育方面的积极参与将塑造更有效和公平的药物发现未来.
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