Empowering biomedical discovery with AI agents

IF 5.7 1区 化学 Q2 CHEMISTRY, PHYSICAL Journal of Chemical Theory and Computation Pub Date : 2024-10-31 DOI:10.1016/j.cell.2024.09.022
Shanghua Gao, Ada Fang, Yepeng Huang, Valentina Giunchiglia, Ayush Noori, Jonathan Richard Schwarz, Yasha Ektefaie, Jovana Kondic, Marinka Zitnik
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Abstract

We envision “AI scientists” as systems capable of skeptical learning and reasoning that empower biomedical research through collaborative agents that integrate AI models and biomedical tools with experimental platforms. Rather than taking humans out of the discovery process, biomedical AI agents combine human creativity and expertise with AI’s ability to analyze large datasets, navigate hypothesis spaces, and execute repetitive tasks. AI agents are poised to be proficient in various tasks, planning discovery workflows and performing self-assessment to identify and mitigate gaps in their knowledge. These agents use large language models and generative models to feature structured memory for continual learning and use machine learning tools to incorporate scientific knowledge, biological principles, and theories. AI agents can impact areas ranging from virtual cell simulation, programmable control of phenotypes, and the design of cellular circuits to developing new therapies.
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利用人工智能代理增强生物医学发现能力
在我们的设想中,"人工智能科学家 "是一种能够进行怀疑式学习和推理的系统,通过将人工智能模型和生物医学工具与实验平台整合在一起的协作代理,增强生物医学研究的能力。生物医学人工智能代理将人类的创造力和专业知识与人工智能分析大型数据集、浏览假设空间和执行重复性任务的能力结合起来,而不是将人类排除在探索过程之外。人工智能代理将精通各种任务,规划发现工作流程,并进行自我评估,以发现和缩小知识差距。这些代理使用大型语言模型和生成模型,具有可持续学习的结构化记忆功能,并使用机器学习工具纳入科学知识、生物原理和理论。人工智能代理可影响的领域包括虚拟细胞模拟、表型的可编程控制、细胞电路设计以及新疗法的开发。
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来源期刊
Journal of Chemical Theory and Computation
Journal of Chemical Theory and Computation 化学-物理:原子、分子和化学物理
CiteScore
9.90
自引率
16.40%
发文量
568
审稿时长
1 months
期刊介绍: The Journal of Chemical Theory and Computation invites new and original contributions with the understanding that, if accepted, they will not be published elsewhere. Papers reporting new theories, methodology, and/or important applications in quantum electronic structure, molecular dynamics, and statistical mechanics are appropriate for submission to this Journal. Specific topics include advances in or applications of ab initio quantum mechanics, density functional theory, design and properties of new materials, surface science, Monte Carlo simulations, solvation models, QM/MM calculations, biomolecular structure prediction, and molecular dynamics in the broadest sense including gas-phase dynamics, ab initio dynamics, biomolecular dynamics, and protein folding. The Journal does not consider papers that are straightforward applications of known methods including DFT and molecular dynamics. The Journal favors submissions that include advances in theory or methodology with applications to compelling problems.
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