论文标题
目标定向分子使用蒙特卡洛树搜索
Goal directed molecule generation using Monte Carlo Tree Search
论文作者
论文摘要
生物化学中的一个具有挑战性和重要的任务是具有所需特性的新分子的产生。由于很难通过分子空间进行导航，因此新型分子的产生仍然是一个挑战，产生的分子应遵守化学价值规则。通过这项工作，我们提出了一种新的方法，即我们称之为unitmcts，通过使用蒙特卡洛树搜索在每个步骤中对分子进行单位更改，以执行分子的产生。我们表明，该方法的表现优于最近发表的关于基准分子优化任务（例如QED和惩罚LOGP）的技术。我们还证明了该方法在改善分子特性的同时与起始分子相似的有用性。鉴于没有学习涉及，我们的方法在较短的时间内找到了所需的分子。
One challenging and essential task in biochemistry is the generation of novel molecules with desired properties. Novel molecule generation remains a challenge since the molecule space is difficult to navigate through, and the generated molecules should obey the rules of chemical valency. Through this work, we propose a novel method, which we call unitMCTS, to perform molecule generation by making a unit change to the molecule at every step using Monte Carlo Tree Search. We show that this method outperforms the recently published techniques on benchmark molecular optimization tasks such as QED and penalized logP. We also demonstrate the usefulness of this method in improving molecule properties while being similar to the starting molecule. Given that there is no learning involved, our method finds desired molecules within a shorter amount of time.
