摩尔代理:在代理时代的生物分子性质估计.
Jose Carlos Gómez-Tamayo1, Joris Tavernier2, Roy Aerts3
1Johnson & Johnson, Beerse 2340, Belgium.
Journal of chemical information and modeling
|October 16, 2025
概括
像MolAgent这样的代理人工智能系统可以自动估计药物发现的分子性质. 这一框架允许自主,专家级建模,加速新药的开发.
科学领域:
- 人工智能的人工智能
- 计算化学计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 由大型语言模型 (LLM) 和先进推理驱动的代理人工智能系统正在改变科学计算.
- 计算机辅助药物设计 (CADD) 涉及复杂的任务,可以由AI自动化.
- 一个关键的挑战是开发符合人类专家可靠性的财产估计人工智能模型,阻碍自主药物发现管道.
研究的目的:
- 介绍MolAgent,一个系统不可知的代理人工智能框架,用于在早期药物发现中高准确度的分子特性建模.
- 使代理系统能够自主构建和部署CADD的专家级分类和回归模型.
- 通过提供可靠的人工智能驱动的财产估计来克服自主药物发现的瓶.
主要方法:
- MolAgent集成了自动化功能工程,模型选择,集合方法和验证框架.
- 该平台可以接受对联体和受体的二维和三维结构数据.
- 它将传统的分子描述符与预训练的编码器的深度学习功能相结合.
主要成果:
- MolAgent自主实施专家级管道用于分子性质建模.
- 该框架确保模型构建和部署的最佳准确性和稳定性.
- 它协调了各种数据类型,并提供了用于全面分析的提取方法.
结论:
- 在CADD中,MolAgent促进了端到端工作流程的自主执行.
- 该框架增强了代理人工智能系统在药物发现中的能力.
- 遵守模型上下文协议 (MCP) 确保了未来管道的各种AI基础设施的互操作性.
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