Rich data and powerful machine learning models allow us to design drugs for a
specific protein target \textit{in silico}. Recently, the inclusion of 3D
structures during targeted drug design shows superior performance to other
target-free models as the atomic interaction in the 3D space is explicitly
modeled. However, current 3D target-aware models either rely on the voxelized
atom densities or the autoregressive sampling process, which are not
equivariant to rotation or easily violate geometric constraints resulting in
unrealistic structures. In this work, we develop a 3D equivariant diffusion
model to solve the above challenges. To achieve target-aware molecule design,
our method learns a joint generative process of both continuous atom
coordinates and categorical atom types with a SE(3)-equivariant network.
Moreover, we show that our model can serve as an unsupervised feature extractor
to estimate the binding affinity under proper parameterization, which provides
an effective way for drug screening. To evaluate our model, we propose a
comprehensive framework to evaluate the quality of sampled molecules from
different dimensions. Empirical studies show our model could generate molecules
with more realistic 3D structures and better affinities towards the protein
targets, and improve binding affinity ranking and prediction without
retraining.

本研究基于 3D 等变扩散模型实现有标的分子设计，并提出了一种综合的评估框架来评估样本分子的质量，实验结果表明，该模型能够更好地生成具有更逼真的 3D 结构和更好的与蛋白靶点的亲和力，而且在不重新训练的情况下能够提高结合亲和力排名和预测质量。