3D Equivariant Diffusion for Target-Aware Molecule Generation and
Affinity Prediction
- URL: http://arxiv.org/abs/2303.03543v1
- Date: Mon, 6 Mar 2023 23:01:43 GMT
- Title: 3D Equivariant Diffusion for Target-Aware Molecule Generation and
Affinity Prediction
- Authors: Jiaqi Guan, Wesley Wei Qian, Xingang Peng, Yufeng Su, Jian Peng,
Jianzhu Ma
- Abstract summary: The inclusion of 3D structures during targeted drug design shows superior performance to other target-free models.
We develop a 3D equivariant diffusion model to solve the above challenges.
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.
- Score: 9.67574543046801
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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