3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction
By Jiaqi Guan, Wesley Wei Qian, Xingang Peng, Yufeng Su, Jian Peng, Jianzhu Ma. 2023
326 citations recorded by the source corpus.
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.
Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology
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Citations in the corpus
- Structure-based drug design with equivariant diffusion models (2022, unknown): “Very recently, a number of diffusion models have been put forward for target-specific molecule design [18–22].”
- Diffusion Models: A Comprehensive Survey of Methods and Applications (2022, methodology): “TargetDiff [88] uses target proteins as guidance information and generates molecules step by step by explicitly modeling the interaction between proteins and molecules in 3D space.”