At present, sequence-based and graph-based models are two of popular used molecular generative models. In this study, we introduce a general-purposed, fragment-based, hierarchical molecular representation named t-SMILES (tree-based SMILES) which describes molecules using a SMILES-type string obtained by doing breadth first search (BFS) on full binary molecular tree formed from fragmented molecular graph. The proposed t-SMILES combines the advantages of graph model paying more attention to molecular topology structure and language model possessing powerful learning ability. Experiments with feature tree rooted JTVAE and chemical reaction-based BRICS molecular decomposing algorithms using sequence-based autoregressive generation models on three popular molecule datasets including Zinc, QM9 and ChEMBL datasets indicate that t-SMILES based models significantly outperform previously proposed fragment-based models and being competitive with classical SMILES based and graph-based approaches. Most importantly, we proposed a new perspective for fragment based molecular designing. Hence, SOTA powerful sequence-based solutions could be easily applied for fragment based molecular tasks.
翻译:目前,基于序列和基于图形的模型是两种流行使用的分子基因模型。在本研究中,我们引入了一种通用的、基于碎片的、等级的分子代表,名为t-SMILES(基于树木的SMILES),它描述了通过对由碎裂分子图组成的完整的二进制分子树进行宽度第一次搜索(BFS)而获得的SMILES类型字符串的分子。拟议的t-SMILES将图模型的优势结合起来,更多地关注分子表层结构和具有强大学习能力的语言模型。我们用基于地貌树的JTVAE实验和基于化学反应的金砖分子分解算法,利用基于顺序的自动递增生成模型,在Zinc、QM9和CEEMBL数据集等三种受欢迎的分子数据集中,用SMILES类型来描述分子的分子。显示,基于TMILES的模型大大超出先前提议的碎片模型,与基于典型的SMILES和基于图表的方法具有竞争力。最重要的是,我们提出了基于碎片分子设计的新视角。因此,基于STOA的强大序列的分子模型的解决方案可以很容易应用于分子制造的解决方案。