In-Pocket 3D Graphs Enhance Ligand–Target Compatibility in Generative Small-Molecule Creation: A Dopamine D2 Receptor Model System

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. Here,
we present a graph-based generative modeling technology that encodes
explicit 3D protein–ligand contacts within a relational graph
architecture and evaluate its behavior using the dopamine D2 receptor
(DD2R) as a model system. 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 DD2R-binding
pocket 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 to be among the highest-ranked docking poses with
a high recovery rate. Overall, this work shows how the structural
context of a protein target can enhance the generation of small molecules
within a realistic binding environment.

与小分子配体结合的蛋白质复合物，是基于结构药物发现的核心研究对象。然而，当前绝大多数基于深度学习的生成模型，均未采用三维结构表征方式。本研究提出一种基于图结构的生成建模技术，该技术在关系图架构中编码显式的三维蛋白质-配体接触相互作用，并以多巴胺D2受体（dopamine D2 receptor，DD2R）作为模型体系对其性能进行评估。该模型结合了条件变分自编码器（conditional variational autoencoder），该模块可实现活性特异性的分子生成，同时集成了推定接触生成模块，可对靶点结合口袋内的分子相互作用进行预测。研究结果表明，相较于基于配体的同类二维生成方法所得到的分子，通过本研究提出的三维流程生成的分子与DD2R结合口袋的兼容性更强——该结论通过对接打分、预期立体化学特性以及商业化学数据库中的可回收性三项指标得以验证。预测得到的蛋白质-配体接触相互作用，在具备高回收率的前提下，可被列为排名最高的对接构象之一。总体而言，本研究证实了蛋白质靶点的结构环境，可在真实结合环境中有效优化小分子的生成过程。