Structure-based drug design aims at generating high affinity ligands with
prior knowledge of 3D target structures. Existing methods either use
conditional generative model to learn the distribution of 3D ligands given
target binding sites, or iteratively modify molecules to optimize a
structure-based activity estimator. The former is highly constrained by data
quantity and quality, which leaves optimization-based approaches more promising
in practical scenario. However, existing optimization-based approaches choose
to edit molecules in 2D space, and use molecular docking to estimate the
activity using docking predicted 3D target-ligand complexes. The misalignment
between the action space and the objective hinders the performance of these
models, especially for those employ deep learning for acceleration. In this
work, we propose MolEdit3D to combine 3D molecular generation with optimization
frameworks. We develop a novel 3D graph editing model to generate molecules
using fragments, and pre-train this model on abundant 3D ligands for learning
target-independent properties. Then we employ a target-guided self-learning
strategy to improve target-related properties using self-sampled molecules.
MolEdit3D achieves state-of-the-art performance on majority of the evaluation
metrics, and demonstrate strong capability of capturing both target-dependent
and -independent properties.

基于结构的药物设计通过利用三维靶点结构的先验知识生成高亲和力配体。本研究提出了 MolEdit3D 方法，将三维分子生成与优化框架相结合，通过使用片段开发了一种新的三维图形编辑模型来生成分子，并在丰富的三维配体上进行了预训练，然后采用目标引导的自学习策略来改进与目标相关的性质。MolEdit3D 在大多数评估指标上达到了最先进水平，并且展示了对目标相关和目标无关性质的强大能力。