  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.
