Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg 43183, Sweden.
Department of Pharmaceutical Biosciences, Uppsala University, Uppsala 75237, Sweden.
J Chem Inf Model. 2022 May 9;62(9):2046-2063. doi: 10.1021/acs.jcim.1c00469. Epub 2021 Aug 30.
Because of the strong relationship between the desired molecular activity and its structural core, the screening of focused, core-sharing chemical libraries is a key step in lead optimization. Despite the plethora of current research focused on methods for molecule generation, to our knowledge, no tool capable of designing such libraries has been proposed. In this work, we present a novel tool for drug design called LibINVENT. It is capable of rapidly proposing chemical libraries of compounds sharing the same core while maximizing a range of desirable properties. To further help the process of designing focused libraries, the user can list specific chemical reactions that can be used for the library creation. LibINVENT is therefore a flexible tool for generating virtual chemical libraries for lead optimization in a broad range of scenarios. Additionally, the shared core ensures that the compounds in the library are similar, possess desirable properties, and can also be synthesized under the same or similar conditions. The LibINVENT code is freely available in our public repository at https://github.com/MolecularAI/Lib-INVENT. The code necessary for data preprocessing is further available at: https://github.com/MolecularAI/Lib-INVENT-dataset.
由于所需分子活性与其结构核心之间存在很强的关系，因此有针对性地共享核心的化学文库筛选是先导优化的关键步骤。尽管目前有大量研究集中在分子生成方法上，但据我们所知，还没有能够设计此类文库的工具。在这项工作中，我们提出了一种名为 LibINVENT 的新药设计工具。它能够快速提出共享同一核心的化合物化学文库，同时最大限度地提高一系列理想特性。为了进一步帮助设计有针对性的文库，用户可以列出可用于库创建的特定化学反应。因此，LibINVENT 是一种灵活的工具，可用于在广泛的场景中生成虚拟化学文库以进行先导优化。此外，共享核心确保了库中的化合物具有相似性、具有理想的特性，并且可以在相同或相似的条件下进行合成。LibINVENT 代码可在我们的公共存储库 https://github.com/MolecularAI/Lib-INVENT 上免费获取。数据预处理所需的代码可进一步在：https://github.com/MolecularAI/Lib-INVENT-dataset 上获取。
