Guangdong Provincial Key Laboratory of Laboratory Animals, Guangdong Laboratory Animals Monitoring Institute, Guangzhou 510663, P. R. China.
State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, P. R. China.
J Chem Inf Model. 2022 Jul 25;62(14):3291-3306. doi: 10.1021/acs.jcim.2c00177. Epub 2022 Jul 6.
In recent years, molecular deep generative models have attracted much attention for its application in drug design. The data-driven molecular deep generative model approximates the high dimensional distribution of the chemical space through learning from a large number of molecular structural data. So far, most of the molecular generative models rely on purely 2D ligand information in structure generation. Here, we propose a novel molecular deep generative model which adopts a recurrent neural network architecture coupled with a ligand-protein interaction fingerprint as constraints. The fingerprint was constructed on ligand docking poses and represents the 3D binding mode of ligands in the protein pocket. In the current work, generative models constrained with interaction fingerprints were trained and compared with normal RNN models. It has been shown that models trained with constraints of ligand-protein interaction fingerprint have a clear tendency to generating compounds maintaining similar binding modes. Our results demonstrate the potential application of the interaction fingerprint-constrained generative model for the targeted molecule generation and guided exploration on the drug-like chemical space.
近年来，分子深度生成模型因其在药物设计中的应用而受到广泛关注。数据驱动的分子深度生成模型通过从大量分子结构数据中学习来近似化学空间的高维分布。到目前为止，大多数分子生成模型都依赖于结构生成中纯粹的 2D 配体信息。在这里，我们提出了一种新的分子深度生成模型，该模型采用递归神经网络架构，并结合配体-蛋白相互作用指纹作为约束。该指纹是基于配体对接构象构建的，代表了配体在蛋白口袋中的 3D 结合模式。在目前的工作中，我们对受相互作用指纹约束的生成模型进行了训练，并与正常 RNN 模型进行了比较。结果表明，受配体-蛋白相互作用指纹约束的模型在生成化合物时具有明显的倾向，使其保持相似的结合模式。我们的结果表明，受相互作用指纹约束的生成模型在靶向分子生成和引导药物样化学空间探索方面具有潜在的应用。
