新颖性是否可预测？
Is novelty predictable?
AI总结：
本文探讨了在基于机器学习的设计中，如何在过度信任模型导致失败与不进行推断无法发现新颖性之间取得平衡，特别关注蛋白质设计，并广泛讨论了机器学习设计问题。
摘要：
基于机器学习的设计在科学领域获得了广泛关注，尤其是在小分子、材料和蛋白质的设计中，其社会影响涵盖药物开发与制造、塑料降解以及碳封存。当利用机器学习设计具有新颖属性值的对象时，人们面临一个根本挑战：如何以合理控制失败风险的方式，突破从训练数据中提炼到模型中的当前知识前沿。如果在推断中过度信任已学习的模型，很可能会设计出垃圾。相反，如果不进行推断，就无法发现新颖性。在本文中，我们探讨了如何在这两个极端之间取得有用的平衡。我们特别关注设计具有新颖属性值的蛋白质，尽管我们的讨论大部分更广泛地涉及基于机器学习的设计。
英文摘要：
Machine learning-based design has gained traction in the sciences, most notably in the design of small molecules, materials, and proteins, with societal implications spanning drug development and manufacturing, plastic degradation, and carbon sequestration. When designing objects to achieve novel property values with machine learning, one faces a fundamental challenge: how to push past the frontier of current knowledge, distilled from the training data into the model, in a manner that rationally controls the risk of failure. If one trusts learned models too much in extrapolation, one is likely to design rubbish. In contrast, if one does not extrapolate, one cannot find novelty. Herein, we ponder how one might strike a useful balance between these two extremes. We focus in particular on designing proteins with novel property values, although much of our discussion addresses machine learning-based design more broadly.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
