AI 中文总结
该研究针对科学假设生成效率低的问题，提出协同溯因框架，构建HALO系统用于药物发现中的分子假设生成，经专家研究验证，此系统能促进溯因推理，帮助产生更高质量、更多样的候选分子。
AI 中文摘要
科学发现至关重要但效率低下，主要是因为在巨大搜索空间中生成假设阻碍了突破。当前人工智能系统虽能协助生成新假设候选，但缺乏对用户将这些输出发展为有前景假设的推理过程的交互式支持，导致表面层次的假设。为解决此问题，我们提出协同溯因，一种用于科学假设生成中溯因推理的人机协作框架。为实现协同溯因，我们构建了HALO，一个用于药物发现中分子假设生成的人机协作系统，能改进候选聚类、策略识别和多策略合成。在涉及10位药物化学家的专家研究中，HALO显著促进了用于假设生成的溯因推理，使参与者能产生更高质量、更多样的候选分子。
英文摘要
Scientific discovery is essential yet inefficient, primarily because generating hypotheses within a vast search space hinders breakthroughs. While current AI systems assist in generating new hypothesis candidates, they lack interactive support for the reasoning process by which users develop these outputs into promising hypotheses, resulting in surface-level hypotheses. To address this issue, we present co-abduction, a human-AI collaborative framework for abductive reasoning in scientific hypothesis generation. To operationalize co-abduction, we build HALO, a human-AI collaborative system for molecular hypothesis generation in drug discovery, enabling improved candidate clustering, strategy identification, and multi-strategy synthesis. In expert studies involving 10 medicinal chemists, HALO significantly facilitated abductive reasoning for hypothesis generation -- efficient candidate observation, systematic strategy identification, and coherent multi-strategy composition -- and enabled participants to produce higher-quality, more diverse candidate molecules.
Comments22 pages
