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ResGen is a pocket-aware 3D molecular generation model based on parallel multiscale modelling

Nature Machine Intelligence volume 5, pages 1020–1030 (2023)Cite this article

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Abstract

Most molecular generative models based on artificial intelligence for de novo drug design are ligand-centric and do not consider the detailed three-dimensional geometries of protein binding pockets. Pocket-aware three-dimensional molecular generation is challenging due to the need to impose physical equivariance and to evaluate protein–ligand interactions when incrementally growing partially built molecules. Inspired by multiscale modelling in condensed matter and statistical physics, we present a three-dimensional molecular generative model conditioned on protein pockets, termed ResGen, for designing organic molecules inside of a given target. ResGen is built on the principle of parallel multiscale modelling, which can capture higher-level interaction and achieve higher computational efficiency (about eight-times faster than the previous best art). The generation process is formulated as a hierarchical autoregression, that is, a global autoregression for learning protein–ligand interactions and atomic component autoregression for learning each atom’s topology and geometry distributions. We demonstrate that ResGen has a higher success rate than existing state-of-the-art approaches in generating novel molecules that can bind to unseen targets more tightly than the original ligands. Moreover, retrospective computational experiments on de novo drug design in real-world scenarios show that ResGen successfully generates drug-like molecules with lower binding energy and higher diversity than state-of-the-art approaches.

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Fig. 1: Workflow and architecture of ResGen.
Fig. 2: Evaluation of generated molecules.

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Data availability

The train and test data of this study is available at Zenodo (https://doi.org/10.5281/zenodo.7759114).

Code availability

The source code of this study is freely available at GitHub (https://github.com/HaotianZhangAI4Science/ResGen) to allow replication of the results.

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Acknowledgements

This study was supported by the National Key Research and Development Program of China (grant no. 2022YFF1203000), the National Natural Science Foundation of China (grant no. 22220102001), the Fundamental Research Funds for the Central Universities (grant no. 226-2022-00220) and the Hong Kong Innovation and Technology Fund (project no. ITS/241/21).

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Authors and Affiliations

  1. Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China

    Odin Zhang, Jintu Zhang, Jieyu Jin, Xujun Zhang, RenLing Hu, Chao Shen, Hongyan Du, Yu Kang, Chang-Yu Hsieh & Tingjun Hou

  2. Hangzhou Carbonsilicon AI Technology Co., Ltd, Hangzhou, China

    Odin Zhang & Yafeng Deng

  3. Department of Mathematics, Chinese University of Hong Kong, Hong Kong, China

    Hanqun Cao

  4. Zhejiang Lab, Zhejiang University, Hangzhou, China

    Furui Liu & Guangyong Chen

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  1. Odin Zhang
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Contributions

O.Z. contributed to the main idea and code. J.Z. contributed to the manuscript writing and code reorganization. X.Z. contributed to the collection of the dataset and the corresponding experiment. R.H. and J.J. contributed to the curation of the real-world dataset. C.S. and H.D. contributed to the data analysis and drawing. H.C. and Y.K. contributed to the instruction in physical concepts. Y.D. contributed to the visualization and technical support. F.L. contributed to the suggestion of the geometry analysis metric. G.C. and C.-Y.H. contributed to manuscript revision and experimental design. T.H. contributed to the essential financial support, the conceptualization, and was responsible for the overall quality.

Corresponding authors

Correspondence to Furui Liu, Guangyong Chen, Chang-Yu Hsieh or Tingjun Hou.

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Nature Machine Intelligence thanks Arne Elofsson and Guo-Wei Wei for their contribution to the peer review of this work.

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Zhang, O., Zhang, J., Jin, J. et al. ResGen is a pocket-aware 3D molecular generation model based on parallel multiscale modelling. Nat Mach Intell 5, 1020–1030 (2023). https://doi.org/10.1038/s42256-023-00712-7

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