论文标题
口袋内3D图可增强生成小分子创建中的配体目标兼容性
In-Pocket 3D Graphs Enhance Ligand-Target Compatibility in Generative Small-Molecule Creation
论文作者
论文摘要
与小分子配体复合物中的蛋白质代表了基于结构的药物发现的核心。但是，大多数基于深度学习的生成模型都不存在三维表示。我们在这里提出了一种基于图的生成建模技术，该技术编码了关系图架构中的显式3D蛋白质 - 配体触点。这些模型结合了有条件的变异自动编码器，该变异自动编码器允许具有活动特异性分子的产生和推定的接触生成，从而预测了目标结合袋中分子相互作用的预测。我们表明，与我们的3D过程产生的分子相比，与多巴胺D2受体的结合袋更兼容，与通过对接得分，预期的立体化学和商业化学数据库中的可相当基于配体的2D生成方法产生的分子相比，基于同类配体的2D生成方法产生的分子。在高回收率高的最高对接姿势中发现了预测的蛋白质 - 配体接触。这项工作表明了如何使用蛋白质靶标的结构环境来增强分子的产生。
Proteins in complex with small molecule ligands represent the core of structure-based drug discovery. However, three-dimensional representations are absent from most deep-learning-based generative models. We here present a graph-based generative modeling technology that encodes explicit 3D protein-ligand contacts within a relational graph architecture. The models combine a conditional variational autoencoder that allows for activity-specific molecule generation with putative contact generation that provides predictions of molecular interactions within the target binding pocket. We show that molecules generated with our 3D procedure are more compatible with the binding pocket of the dopamine D2 receptor than those produced by a comparable ligand-based 2D generative method, as measured by docking scores, expected stereochemistry, and recoverability in commercial chemical databases. Predicted protein-ligand contacts were found among highest-ranked docking poses with a high recovery rate. This work shows how the structural context of a protein target can be used to enhance molecule generation.
