Kondinski Aleksandar, Menon Angiras, Nurkowski Daniel, Farazi Feroz, Mosbach Sebastian, Akroyd Jethro, Kraft Markus
Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, U.K.
CMCL Innovations, Sheraton House, Castle Park, Cambridge CB3 0AX, U.K.
J Am Chem Soc. 2022 Jul 6;144(26):11713-11728. doi: 10.1021/jacs.2c03402. Epub 2022 Jun 22.
Metal-organic polyhedra (MOPs) are hybrid organic-inorganic nanomolecules, whose rational design depends on harmonious consideration of chemical complementarity and spatial compatibility between two or more types of chemical building units (CBUs). In this work, we apply knowledge engineering technology to automate the derivation of MOP formulations based on existing knowledge. For this purpose we have (i) curated relevant MOP and CBU data; (ii) developed an assembly model concept that embeds rules in the MOP construction; (iii) developed an OntoMOPs ontology that defines MOPs and their key properties; (iv) input agents that populate The World Avatar (TWA) knowledge graph; and (v) input agents that, using information from TWA, derive a list of new constructible MOPs. Our result provides rapid and automated instantiation of MOPs in TWA and unveils the immediate chemical space of known MOPs, thus shedding light on new MOP targets for future investigations.
金属有机多面体(MOPs)是有机-无机杂化纳米分子,其合理设计依赖于对两种或更多种化学构建单元(CBUs)之间化学互补性和空间兼容性的协调考虑。在这项工作中,我们应用知识工程技术,基于现有知识自动推导MOP配方。为此,我们(i)整理了相关的MOP和CBU数据;(ii)开发了一种在MOP构建中嵌入规则的组装模型概念;(iii)开发了一个定义MOP及其关键属性的OntoMOPs本体;(iv)向世界阿凡达(TWA)知识图谱中填充信息的输入代理;以及(v)利用来自TWA的信息推导新的可构建MOP列表的输入代理。我们的结果在TWA中提供了MOP的快速自动实例化,并揭示了已知MOP的直接化学空间,从而为未来研究的新MOP目标提供了线索。