AI for Biology: Progress, Characteristics,and Future

Abstract

Abstract: Since the 21st century, artificial intelligence (AI) technologies, particularly those exemplified by deep learning, have significantly accelerated the interdisciplinary integration of various fields. This integration has led to a surge of AI-driven scientific research, especially in the application of AI within the realm of biology (AI4B). Over the past seven decades, AI and biology have mutually influenced one another; while AI has enabled the precise resolution of complex biological problems, it has simultaneously fostered its own continuous iteration and advancement. AI4B has established a transformative model for the production of scientific knowledge through human-machine collaboration, which can be delineated across three dimensions: in the tool empowerment dimension, AI enhances task efficiency; in the deep embedding dimension, AI reshapes the scientific innovation chain; and in the general platform dimension, AI addresses multimodal tasks. The development of a human-machine symbiotic AI4B knowledge ecosystem is of paramount importance for effectively mitigating AI-related risks, formulating pertinent policies, and shaping the "emerging science and technology" that AI represents.

摘要： 自21世纪以来，以深度学习为代表的人工智能技术（AI）加速学科交叉融合，催生了人工智能驱动科学研究热潮，尤以在生物学领域的应用为典型（AI4B）。在过去70余年汇聚历程中，AI与生物学之间双向互驱，AI在促进生物学问题精确求解的同时，也推动了自身的不断迭代与发展。AI4B通过人机协作形成了一种变革性的科学知识生产模式，具体体现在三个维度：在工具赋能维度，AI提升了确定任务效率；在深度嵌入维度，AI形塑科学创新环链；在通用平台维度，AI求解多模态任务。构建人机共生的AI4B知识生态，对有效面对AI风险、制定相关政策、塑造以AI为代表的“形成中的科学技术”具有重要意义。

高璐舒心雨王彦雨. 人工智能驱动生物学研究:历程、特征及展望[J]. 科学学研究, 2025, 43(12): 2631-2641.

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