Guangzhou National Laboratory, No. 9 XingDaoHuanBei Road, Guangzhou International Bio Island, Guangzhou, Guangdong 510005, China.
J Chem Inf Model. 2023 Nov 27;63(22):7067-7082. doi: 10.1021/acs.jcim.3c01626. Epub 2023 Nov 14.
De novo molecular design plays an important role in drug discovery. Here, a novel generative model, Tree-Invent, was proposed to integrate topological constraints in the generation of a molecular graph. In this model, a molecular graph is represented as a topological tree in which a ring system, a nonring atom, and a chemical bond are regarded as the ring node, single node, and edge, respectively. The molecule generation is driven by three independent submodels for carrying out operations of node addition, ring generation, and node connection. One unique feature of the generative model is that the topological tree structure can be specified as a constraint for structure generation, which provides more precise control of structure generation. Combined with reinforcement learning, the Tree-Invent model could efficiently explore targeted chemical space. Moreover, the Tree-Invent model is flexible enough to be used in versatile molecule design settings such as scaffold decoration, scaffold hopping, and linker generation.
从头分子设计在药物发现中起着重要作用。在这里，提出了一种新的生成模型 Tree-Invent，用于在生成分子图时集成拓扑约束。在该模型中，分子图表示为拓扑树，其中环系统、非环原子和化学键分别被视为环节点、单节点和边。分子生成由三个独立的子模型驱动，用于执行节点添加、环生成和节点连接操作。生成模型的一个独特特征是可以将拓扑树结构指定为结构生成的约束，从而提供对结构生成的更精确控制。结合强化学习，Tree-Invent 模型可以有效地探索目标化学空间。此外，Tree-Invent 模型足够灵活，可以用于支架装饰、支架跳跃和接头生成等多种分子设计场景。
