赋予化学家在药物设计中的权力:通过企业级别的ELN框架提供AI解决方案
Ting Qin1, Aparna Chandrasekaran1, Jack Hoffman1
1Sygnature Discovery Ltd., BioCity, Pennyfoot Street, Nottingham, United Kingdom.
SLAS technology
|February 2, 2026
概括
我们使用电子实验室笔记本 (ELN) 开发了一个新的框架,将人工智能 (AI) 整合到药物发现中. 这种方法简化了化学家对AI的采用,增强了数据共享和加速药物设计过程.
科学领域:
- 药用化学 医学化学
- 计算化学计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 人工智能 (AI) 为药物发现提供了变革性的潜力.
- 人工智能在科学研究中的广泛采用受到用户友好性,工作流管理和数据集成方面的挑战的阻碍.
- 现有的实验室实践往往缺乏与先进的计算工具的无集成.
研究的目的:
- 提出一个新的框架,将AI整合到药物发现工作流程中.
- 通过利用电子实验室笔记本 (ELN) 范式来应对人工智能采用的挑战.
- 赋予银行化学家可访问的人工智能工具,以加强药物设计和开发.
主要方法:
- 将人工智能工作流正式化为ELN协议.
- 在熟悉的实验室界面中,将AI执行视为ELN实验.
- 遵守FAIR数据原则 (可查找性,可访问性,可互操作性,可重复使用性).
主要成果:
- 一个可扩展,可追踪和以用户为导向的AI部署策略,与实验室实践保持一致.
- 通过基于ELN的框架提高了数据的可查,可访问性,互操作性和可重复使用性.
- 赋予银行化学家使用人工智能工具的权力,使他们成为完全投入的药物化学家.
结论:
- 基于ELN的AI框架促进了AI与药物发现的整合,克服了采用障碍.
- 这种方法使化学家能够实时考虑合成可行性,加速化合物设计.
- 通过使化学家能够积极为药物设计过程贡献实用专业知识,最大限度地提高人工智能投资的回报率.
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