Despite growing interest and success in automated in-silico molecular design, questions remain regarding the ability of goal-directed generation algorithms to perform unbiased exploration of novel chemical spaces. A specific phenomenon has recently been highlighted: goal-directed generation guided with machine learning models produce molecules with high scores according to the optimization model, but low scores according to control models, even when trained on the same data distribution and the same target. In this work, we show that this worrisome behavior is actually due to issues with the predictive models and not the goal-directed generation algorithms. We show that with appropriate predictive models, this issue can be resolved, and molecules generated have high scores according to both the optimization and the control models.

中文翻译：

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解释和避免以目标为导向的小分子生成中的故障模式

尽管对自动化计算机内分子设计的兴趣和成功越来越多，但目标导向生成算法对新化学空间进行无偏见探索的能力仍然存在问题。最近强调了一个具体现象：使用机器学习模型引导的目标导向生成根据优化模型产生高分分子，但根据控制模型产生低分分子，即使在相同数据分布和相同目标上进行训练时也是如此。在这项工作中，我们表明这种令人担忧的行为实际上是由于预测模型的问题，而不是目标导向的生成算法。我们表明，通过适当的预测模型，可以解决这个问题，并且根据优化和控制模型生成的分子具有高分。