[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"tag-ai-for-science":3},{"tag":4,"articles":11},{"id":5,"name":6,"slug":7,"article_count":8,"description_zh":9,"description_en":10},"f7319f69-219a-40ab-b613-85a77fefaa61","AI for science","ai-for-science",3,"AI for science 指的是把模型、資料與自動化流程用在科研工作中，從文獻整理、假說生成到蛋白質設計、藥物研發與實驗規劃都涵蓋在內。它的重要性在於縮短研究迭代時間，並讓科學軟體與實驗室工作更可程式化。","AI for science refers to using models, data pipelines, and automation in research workflows, from literature review and hypothesis generation to protein design, drug discovery, and experiment planning. It matters because it shortens iteration cycles and makes scientific work more programmable.",[12],{"id":13,"slug":14,"title":15,"summary":16,"category":17,"image_url":18,"cover_image":18,"language":19,"created_at":20},"010539a1-4c3a-4bd3-937a-26616422ee0d","awesome-ai-for-science-research-tools-map-en","Awesome AI for Science Is Becoming a Real Research Map","This GitHub list pulls together AI tools, datasets, papers, and frameworks for science, giving researchers a practical starting point.","tools","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1774619185536-iewn.png","en","2026-03-27T01:46:50.89513+00:00"]