标题： 主动学习在分子生成模型中的外推作用 显示英文标题

标题： Active Learning Enables Extrapolation in Molecular Generative Models

摘要： 尽管生成模型在发现具有优化期望特性的分子方面具有潜力，但它们常常无法提出可合成的分子，这些分子能够优于训练中看到的已知分子。 我们发现，主要限制不是在分子生成过程本身，而是在分子性质预测器的泛化能力较差。 我们通过创建一个主动学习的闭环分子生成流程来解决这一挑战，其中分子生成模型通过量子化学模拟的反馈进行迭代优化，以提高对新化学空间的泛化能力。 与其他生成模型方法相比，只有我们的主动学习方法生成的分子其性质能够超越训练数据（达到训练数据范围之外0.44个标准差），并且分布外分子分类准确率提高了79%。 通过在主动学习循环中的热力学稳定性数据上对分子生成进行条件约束，生成的稳定分子比例比下一个最佳模型高3.5倍。 显示英文摘要

摘要： Although generative models hold promise for discovering molecules with optimized desired properties, they often fail to suggest synthesizable molecules that improve upon the known molecules seen in training. We find that a key limitation is not in the molecule generation process itself, but in the poor generalization capabilities of molecular property predictors. We tackle this challenge by creating an active-learning, closed-loop molecule generation pipeline, whereby molecular generative models are iteratively refined on feedback from quantum chemical simulations to improve generalization to new chemical space. Compared against other generative model approaches, only our active learning approach generates molecules with properties that extrapolate beyond the training data (reaching up to 0.44 standard deviations beyond the training data range) and out-of-distribution molecule classification accuracy is improved by 79%. By conditioning molecular generation on thermodynamic stability data from the active-learning loop, the proportion of stable molecules generated is 3.5x higher than the next-best model.