从算法到系统:将计算集成到药物发现中
Anthony R Bradley1,2, Adrian Rossall2, Garry Pairaudeau2
1Department of Chemistry, University of Liverpool, Liverpool, UK.
Expert opinion on drug discovery
|December 25, 2025
概括
计算药物发现面临着成本和时间方面的挑战. 采用现代数据基础设施,自动化和人工智能 (AI) 可以加速临床前研究并提高效率.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 生物信息学是一种生物信息学.
背景情况:
- 临床前药物发现受到日益增加的时间表和成本的阻碍,尽管计算技术的进步.
- 虽然软件,数据和自动化提供了强大的工具,但它们降低成本和时间的潜力尚未完全实现.
研究的目的:
- 讨论药物发现能力的演变.
- 探索现代数据基础设施,包括云原生平台,主动学习和实验室自动化.
- 涵盖新兴技术,如基于LLM的编排和仿真,说明成功和挑战.
主要方法:
- 审查现代数据基础设施 (云原生平台,主动学习).
- 实验室自动化和新兴技术的探索 (基于LLM的编排,仿真).
- 分析实施实例,突出成功和挑战.
主要成果:
- 人工智能为药物发现提供了一个新的范式,需要文化和技术转变.
- 需要可扩展和强大的计算药物发现工具.
- 专注于从数据和自动化中学习效率是加速设计周期的关键.
结论:
- 更广泛地采用具有更好的经济性模块化,可互操作的自动化单元至关重要.
- 建议在统计方法中优先考虑学习效率而不是绝对的预测准确性.
- 整合人工智能和自动化可以显著改善临床前药物发现.
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