Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

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

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


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.

This is a preview of subscription content, access via your institution

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

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.

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 with an MIT licence. The version used for this publication is available at


  1. Schneider, G. Automating drug discovery. Nat. Rev. Drug Discov. 17, 97–113 (2018).

    Article  Google Scholar 

  2. Schneider, P. et al. Rethinking drug design in the artificial intelligence era. Nat. Rev. Drug Discov. 19, 353–364 (2020).

    Article  Google Scholar 

  3. Inglese, J. & Auld, D. S. in Wiley Encyclopedia of Chemical Biology (ed. Begley, T. P.) (Wiley, 2008);

  4. Sliwoski, G., Kothiwale, S., Meiler, J. & Lowe, E. W. Computational methods in drug discovery. Pharmacol. Rev. 66, 334–395 (2014).

    Article  Google Scholar 

  5. Fleming, N. How artificial intelligence is changing drug discovery. Nature 557, S55–S57 (2018).

    Article  Google Scholar 

  6. Zheng, S., Li, Y., Chen, S., Xu, J. & Yang, Y. Predicting drug–protein interaction using quasi-visual question answering system. Nat. Mach. Intell. 2, 134–140 (2020).

    Article  Google Scholar 

  7. Shen, W. X. et al. Out-of-the-box deep learning prediction of pharmaceutical properties by broadly learned knowledge-based molecular representations. Nat. Mach. Intell. 3, 334–343 (2021).

    Article  Google Scholar 

  8. Kotsias, P.-C. et al. Direct steering of de novo molecular generation with descriptor conditional recurrent neural networks. Nat. Mach. Intell. 2, 254–265 (2020).

    Article  Google Scholar 

  9. Méndez-Lucio, O., Baillif, B., Clevert, D. A., Rouquié, D. & Wichard, J. De novo generation of hit-like molecules from gene expression signatures using artificial intelligence. Nat. Commun. 11, 10 (2020).

    Article  Google Scholar 

  10. Chen, H., Engkvist, O., Wang, Y., Olivecrona, M. & Blaschke, T. The rise of deep learning in drug discovery. Drug Discov. Today 23, 1241–1250 (2018).

    Article  Google Scholar 

  11. Jiang, S. & Balaprakash, P. Graph neural network architecture search for molecular property prediction. In Proc. IEEE International Conference on Big Data 1346–1353 (IEEE, 2020).

  12. Cai, S., Li, L., Deng, J., Zhang, B., Zha, Z. J., Su, L., & Huang, Q. Rethinking Graph Neural Architecture Search from Message-passing. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 6653–6662. (2021).

  13. Zhang, Z., Wang, X., & Zhu, W. Automated Machine Learning on Graphs: A Survey. IJCAI International Joint Conference on Artificial Intelligence, 4704–4712. (2021)

  14. Ekins, S. et al. Exploiting machine learning for end-to-end drug discovery and development. Nat. Mater. 18, 435–441 (2019).

    Article  Google Scholar 

  15. Sculley, D. et al. Hidden technical debt in machine learning systems. In Proc. Advances in Neural Information Processing SystemsVol. 2015-January, 2503–2511 (NIPS, 2015).

  16. Jiang, M. et al. Drug–target affinity prediction using graph neural network and contact maps. RSC Adv. 10, 20701–20712 (2020).

    Article  Google Scholar 

  17. Kipf, T. N. & Welling, M. Semi-supervised classification with graph convolutional networks. In Proc. 2017 International Conference on Learning Representations (ICLR, 2017).

  18. Veličković, P. et al. Graph attention networks. In Proc. 2018 International Conference on Learning Representations 1–12 (ICLR, 2018).

  19. Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O. & Dahl, G. E. Neural message passing for quantum chemistry. In Proc. International Conference on Machine Learning Vol. 3, 2053–2070 (ACM, 2017).

  20. Xiong, Z. et al. Pushing the boundaries of molecular representation for drug discovery with graph attention mechanism. J. Med. Chem. (2019).

  21. Xu, K., Jegelka, S., Hu, W. & Leskovec, J. How powerful are graph neural networks? In Proc. 7th International Conference on Learning Representations, ICLR 2019 (ICLR, 2019).

  22. Källberg, M. et al. Template-based protein structure modeling using the RaptorX web server. Nat. Protoc. 7, 1511–1522 (2012).

    Article  Google Scholar 

  23. Li, H., Leung, K. S., Wong, M. H. & Ballester, P. J. Improving AutoDock Vina using Random Forest: the growing accuracy of binding affinity prediction by the effective exploitation of larger data sets. Mol. Informatics 34, 115–126 (2015).

    Article  Google Scholar 

  24. Chen, L. et al. TransformerCPI: improving compound-protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments. Bioinformatics 36, 4406–4414 (2020).

    Article  Google Scholar 

  25. Huang, K., Xiao, C., Hoang, T., Glass, L. & Sun, J. CASTER: predicting drug interactions with chemical substructure representation. Proc. AAAI Conf. Artif. Intell. 34, 702–709 (2020).

    Google Scholar 

  26. Yang, Y.-Y., Rashtchian, C., Zhang, H., Salakhutdinov, R. & Chaudhuri, K. A closer look at accuracy vs. robustness. In Proc. 34th International Conference on Neural Information Processing Systems Vol. 720, 8588–8601 (NIPS, 2020).

  27. Tetko, I. V., Tanchuk, V. Y. & Villa, A. E. P. Prediction of n-octanol/water partition coefficients from PHYSPROP database using artificial neural networks and E-state indices. J. Chem. Inf. Comput. Sci. 41, 1407–1421 (2001).

    Article  Google Scholar 

  28. Zeng, Y., Chen, X., Luo, Y., Li, X. & Peng, D. Deep drug–target binding affinity prediction with multiple attention blocks. Briefings Bioinform. 22, bbab117 (2021).

    Article  Google Scholar 

  29. Withnall, M., Lindelöf, E., Engkvist, O. & Chen, H. Building attention and edge message passing neural networks for bioactivity and physical-chemical property prediction. J. Cheminform. 12, 1–18 (2020).

    Article  Google Scholar 

  30. Feurer, M., Eggensperger, K., Falkner, S., Lindauer, M. & Hutter, F. Auto-Sklearn 2.0: the next generation (2020);

  31. Erickson, N., Mueller, J., Shirkov, A., Zhang, H., Larroy, P., Li, M., & Smola, A. AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data. ICML Workshop on Automated Machine Learning (2020).

  32. Rogers, D. & Hahn, M. Extended-connectivity fingerprints. J. Chem. Inf. Model. 50, 742–754 (2010).

    Article  Google Scholar 

  33. Xiong, J., Xiong, Z., Chen, K., Jiang, H. & Zheng, M. Graph neural networks for automated de novo drug design. Drug Discov. Today 26, 1382–1393 (2021).

    Article  Google Scholar 

  34. Dai, H. et al. Retrosynthesis prediction with conditional graph logic network. In Proc. 33rd International Conference on Neural Information Processing Systems Vol. 796, 8872–8882 (NIPS, 2020).

  35. Wang, X. et al. RetroPrime: a diverse, plausible and transformer-based method for single-step retrosynthesis predictions. Chem. Eng. J. 420, 129845 (2021).

    Article  Google Scholar 

  36. Kuznetsov, M. & Polykovskiy, D. MolGrow: a graph normalizing flow for hierarchical molecular generation. In Proc. AAAI Conference on Artificial Intelligence Vol. 35, 8226–8234 (AAAI, 2021).

  37. Luo, Y., Yan, K. & Ji, S. GraphDF: a discrete flow model for molecular graph generation. In Proc. 38th International Conference on Machine Learning, PMLR Vol. 139, 7192–7203 (PMLR, 2021).

  38. Liu, M., Yan, K., Oztekin, B. & Ji, S. GraphEBM: molecular graph generation with energy-based models. Proc. ILCR Workshop on Energy Based Models 1–16 (2021).

  39. Tran-Nguyen, V. K., Jacquemard, C. & Rognan, D. LIT-PCBA: an unbiased data set for machine learning and virtual screening. J. Chem. Inf. Model. 60, 4263–4273 (2020).

    Article  Google Scholar 

  40. Gilson, M. K. et al. BindingDB in 2015: a public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res. 44, D1045–D1053 (2016).

    Article  Google Scholar 

  41. Wishart, D. S. et al. DrugBank: a knowledge base for drugs, drug actions and drug targets. Nucleic Acids Res. 36, D901–D906 (2008).

    Article  Google Scholar 

  42. Wu, Z. et al. MoleculeNet: a benchmark for molecular machine learning. Chem. Sci. 9, 513–530 (2018).

    Article  Google Scholar 

  43. Li, Y. Code for ‘An adaptive graph learning method for automated molecular interactions and properties predictions’ (Zenodo, 2022);

  44. Halgren, T. A. et al. Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J. Med. Chem. 47, 1750–1759 (2004).

    Article  Google Scholar 

  45. Fey, M. & Lenssen, J. E. Fast graph representation learning with PyTorch Geometric. In Proc. ICLR 2019 Workshop on Representation Learning on Graphs and Manifolds (ICLR, 2019);

Download references


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.

Author information

Authors and Affiliations



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.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

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

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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.

Supplementary information

Supplementary Information

Supplementary Tables 1–5.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing