Data-driven design is making headway into a number of application areas, including protein, small-molecule, and materials engineering. The design goal is to construct an object with desired properties, such as a protein that binds to a target more tightly than previously observed. To that end, costly experimental measurements are being replaced with calls to a high-capacity regression model trained on labeled data, which can be leveraged in an in silico search for promising design candidates. However, the design goal necessitates moving into regions of the input space beyond where such models were trained. Therefore, one can ask: should the regression model be altered as the design algorithm explores the input space, in the absence of new data acquisition? Herein, we answer this question in the affirmative. In particular, we (i) formalize the data-driven design problem as a non-zero-sum game, (ii) leverage this formalism to develop a strategy for retraining the regression model as the design algorithm proceeds---what we refer to as autofocusing the model, and (iii) demonstrate the promise of autofocusing empirically.

数据驱动设计正在广泛应用于蛋白质、小分子和材料工程等领域。然而，设计目标需要进入超出模型训练范围的设计空间， 因此需要针对式样寻找新的候选设计。本文提出了一种自动对焦方法来调整回归模型，以适应训练范围之外的新式样，从而更好地实现数据驱动设计目标。

基于模型的设计的自动聚焦神谕