论文标题
图形抛光剂:用于分子优化的新型图生成范式
Graph Polish: A Novel Graph Generation Paradigm for Molecular Optimization
论文作者
论文摘要
分子优化将给定的输入分子X转化为具有理想特性的另一个y,对于分子药物发现至关重要。传统的翻译方法,通过逐步添加一些子结构来从头开始生成分子图,由于大量候选子结构以大量的步骤到达最终目标,因此很容易出错。在这项研究中,我们提出了一种新型的分子优化范式图形抛光剂,该范式将分子优化从传统的“两语言翻译”任务更改为“单语言抛光”任务。这种优化范式的关键是要找到一个优化中心,但应根据其周围保留区域应最大化的条件,然后将其删除和添加的区域最小化。然后,我们提出了一个有效,有效的学习框架T&S PORIS,以在优化步骤中捕获长期依赖性。 T分量自动识别和注释优化中心以及分子某些部分的保存,去除和添加,S分量了解这些行为并将这些动作应用于新分子。此外,提出的范式可以为每个分子优化结果提供直观的解释。具有多个优化任务的实验是在四个基准数据集上进行的。拟议的T&S波兰方法比所有任务上的五个最先进的基线方法具有显着优势。此外,进行了广泛的研究,以验证新型优化范式的有效性,解释性和时间节省。
Molecular optimization, which transforms a given input molecule X into another Y with desirable properties, is essential in molecular drug discovery. The traditional translating approaches, generating the molecular graphs from scratch by adding some substructures piece by piece, prone to error because of the large set of candidate substructures in a large number of steps to the final target. In this study, we present a novel molecular optimization paradigm, Graph Polish, which changes molecular optimization from the traditional "two-language translating" task into a "single-language polishing" task. The key to this optimization paradigm is to find an optimization center subject to the conditions that the preserved areas around it ought to be maximized and thereafter the removed and added regions should be minimized. We then propose an effective and efficient learning framework T&S polish to capture the long-term dependencies in the optimization steps. The T component automatically identifies and annotates the optimization centers and the preservation, removal and addition of some parts of the molecule, and the S component learns these behaviors and applies these actions to a new molecule. Furthermore, the proposed paradigm can offer an intuitive interpretation for each molecular optimization result. Experiments with multiple optimization tasks are conducted on four benchmark datasets. The proposed T&S polish approach achieves significant advantage over the five state-of-the-art baseline methods on all the tasks. In addition, extensive studies are conducted to validate the effectiveness, explainability and time saving of the novel optimization paradigm.