De Novo Molecule Design Using Molecular Generative Models Constrained by Ligand–Protein Interactions,Journal of Chemical Information and Modeling

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De Novo Molecule Design Using Molecular Generative Models Constrained by Ligand–Protein Interactions
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2022-07-06 , DOI: 10.1021/acs.jcim.2c00177
Jie Zhang _{1,

2,

3} , Hongming Chen _{3,

4}

Affiliation

In recent years, molecular deep generative models have attracted much attention for its application in de novo 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.

中文翻译：

使用受配体-蛋白质相互作用约束的分子生成模型进行从头分子设计

近年来，分子深度生成模型因其在de novo中的应用而备受关注。药物设计。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逼近化学空间的高维分布。到目前为止，大多数分子生成模型在结构生成中依赖于纯二维配体信息。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和配体-蛋白质相互作用指纹作为约束条件。指纹是在配体对接姿势上构建的，代表了蛋白质口袋中配体的 3D 结合模式。在目前的工作中，训练了受交互指纹约束的生成模型，并与正常的 RNN 模型进行了比较。已经表明，受配体-蛋白质相互作用指纹约束训练的模型具有生成保持相似结合模式的化合物的明显趋势。我们的结果证明了相互作用指纹约束生成模型在靶向分子生成和药物样化学空间引导探索中的潜在应用。

更新日期：2022-07-06

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