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An adaptive graph learning method for automated molecular interactions and properties predictions

Nature Machine Intelligence volume 4, pages 645–651 (2022)Cite this article

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A preprint version of the article is available at Research Square.

Abstract

Improving drug discovery efficiency is a core and long-standing challenge in drug discovery. For this purpose, many graph learning methods have been developed to search potential drug candidates with fast speed and low cost. In fact, the pursuit of high prediction performance on a limited number of datasets has crystallized their architectures and hyperparameters, making them lose advantage in repurposing to new data generated in drug discovery. Here we propose a flexible method that can adapt to any dataset and make accurate predictions. The proposed method employs an adaptive pipeline to learn from a dataset and output a predictor. Without any manual intervention, the method achieves far better prediction performance on all tested datasets than traditional methods, which are based on hand-designed neural architectures and other fixed items. In addition, we found that the proposed method is more robust than traditional methods and can provide meaningful interpretability. Given the above, the proposed method can serve as a reliable method to predict molecular interactions and properties with high adaptability, performance, robustness and interpretability. This work takes a solid step forward to the purpose of aiding researchers to design better drugs with high efficiency.

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Fig. 1: Overview of GLAM and the traditional method.
Fig. 2: GLAM pipeline details.
Fig. 3: The general architectures for molecular interactions and properties prediction in GLAM.

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Data availability

All data used in this paper are publicly available and can be accessed as follows: LIT-PCBA39 (ALDH1, ESR1_ant, KAT2A, MAPK1), BindingDB40, DrugBank41, MoleculeNet42 (ESOL, Lipophilicity, FreeSolv, BACE, BBBP, SIDER, Tox21, ToxCast) and Perturbed PhysProp43.

Code availability

All code of GLAM is freely available at https://github.com/yvquanli/GLAM with an MIT licence. The version used for this publication is available at https://doi.org/10.5281/zenodo.637116443.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (22173038 and 21775060). We thank the Supercomputing Center of Lanzhou University for providing high-performance computing resources. We acknowledge help from J. Xu, the author of RaptorX22, as well as help from M. Jiang, the author of DGraphDTA16.

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Author notes

  1. These authors contributed equally: Yuquan Li, Chang-Yu Hsieh.

Authors and Affiliations

  1. College of Chemistry and Chemical Engineering, Lanzhou University, Lanzhou, China

    Yuquan Li, Ruiqiang Lu, Xiaoqing Gong & Xiaojun Yao

  2. Tencent Quantum Laboratory, Tencent, Shenzhen, China

    Yuquan Li, Chang-Yu Hsieh & Shengyu Zhang

  3. State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macau, China

    Xiaorui Wang & Xiaojun Yao

  4. School of Computer Science and Technology, Xidian University, Xian, China

    Pengyong Li

  5. School of Pharmacy, Lanzhou University, Lanzhou, China

    Shuo Liu, Yanan Tian & Huanxiang Liu

  6. College of Computer Science and Technology, Zhejiang University, Hangzhou, China

    Dejun Jiang

  7. School of Data Science, University of Science and Technology of China, Hefei, China

    Jiaxian Yan

  8. School of Basic Medical Sciences, Lanzhou University, Lanzhou, China

    Qifeng Bai

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Contributions

Y.L., C.-Y.H. and X.Y. conceived the project. Y.L., C.-Y.H., R.L., X.G., X.W. and P.L. designed and conducted the experiments. C.-Y.H., S.L., Y.T., D.J., J.Y., Q.B. and H.L. evaluated the experiments and contributed ideas. S.Z., C.-Y.H. and X.Y. managed and supervised the project. All authors co-wrote the manuscript.

Corresponding author

Correspondence to Xiaojun Yao.

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The authors declare no competing interests.

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Nature Machine Intelligence thanks William McCorkindale and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Design space for blocks of the architectures.

a, Feed-forward Block. It takes a tensor as input and outputs a tensor. Abbreviations and their full name correspond as follows: Norm(Normalization), ReLU(Rectified linear units), CeLU(Continuously differentiable exponential linear units). b, Message Passing Block. It takes a graph as input and outputs a graph. Abbreviations and their full name correspond as follows: GCN(Graph convolutional networks), GAT(Graph attention networks), MPN(Message-passing neural networks), Tri-MPN(Triplet message-passing neural networks), Light Tri-MPN(Light triplet message-passing neural networks). c, Fusion Block. It takes a graph as input and outputs a tensor. Dot means the dot multiplication operation. d, Global Pooling Block. It takes a graph as input and outputs a tensor.

Extended Data Fig. 2 Cases of node-level interpretation.

a, Case studies of solubility prediction. The atoms in the hydrophilic group tend to be bluer in our visualization, which means their weights are closer to 1. In contrast, the atoms in the lipophilic group tend to be redder in our visualization, which means their weights are closer to −1. b, Case studies of drug-drug interactions. The visualization results show the models in predictor pay more attention to the nitrates of isosorbide dinitrate and nicorandil, and pay more attention to the N-methyl of sildenafil and udenafil.

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Li, Y., Hsieh, CY., Lu, R. et al. An adaptive graph learning method for automated molecular interactions and properties predictions. Nat Mach Intell 4, 645–651 (2022). https://doi.org/10.1038/s42256-022-00501-8

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