We study the problem of extrapolative controlled generation, i.e., generating sequences with attribute values beyond the range seen in training. This task is of significant importance in automated design, especially drug discovery, where the goal is to design novel proteins that are \textit{better} (e.g., more stable) than existing sequences. Thus, by definition, the target sequences and their attribute values are out of the training distribution, posing challenges to existing methods that aim to directly generate the target sequence. Instead, in this work, we propose Iterative Controlled Extrapolation (ICE) which iteratively makes local edits to a sequence to enable extrapolation. We train the model on synthetically generated sequence pairs that demonstrate small improvement in the attribute value. Results on one natural language task (sentiment analysis) and two protein engineering tasks (ACE2 stability and AAV fitness) show that ICE considerably outperforms state-of-the-art approaches despite its simplicity. Our code and models are available at: https://github.com/vishakhpk/iter-extrapolation.

翻译：我们研究外推式可控生成问题，即生成属性值超出训练数据范围的序列。该任务在自动化设计领域具有重大意义，尤其在药物发现中，目标是设计比现有序列更优（例如更稳定）的新型蛋白质。因此，目标序列及其属性值本质上属于训练分布外数据，这对旨在直接生成目标序列的现有方法构成挑战。为此，本文提出迭代可控外推方法（ICE），通过序列的局部迭代编辑实现外推。我们使用合成生成的序列对训练模型，这些序列对展示出属性值的渐进提升。在自然语言任务（情感分析）和两项蛋白质工程任务（ACE2稳定性与AAV适应度）上的实验表明，尽管ICE方法简洁，但其性能显著超越现有最先进方法。我们的代码和模型已开源：https://github.com/vishakhpk/iter-extrapolation。