Skip to main content

Thank you for visiting nature.com. 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.

  • Article
  • Published:

Deep lead optimization enveloped in protein pocket and its application in designing potent and selective ligands targeting LTK protein

Nature Machine Intelligence volume 7, pages 448–458 (2025)Cite this article

Subjects

A preprint version of the article is available at arXiv.

Abstract

Optimizing the chemical structure of promising drug candidates through systematic modifications to improve potency and physiochemical properties is a vital step in the drug discovery pipeline. In contrast to the well-established de novo generation schemes, computational methods specifically tailored for lead optimization remain largely underexplored. Prior models are often limited to addressing specific subtasks, such as generating two-dimensional molecular structures, while neglecting crucial protein–ligand interactions in three-dimensional space. To overcome these challenges, we propose Delete (Deep lead optimization enveloped in protein pocket), a one-stop solution for lead optimization by combining generative artificial intelligence and structure-based approaches. Our model can handle all subtasks of lead optimization through a unified deleting (masking) strategy, and it accounts for intricate pocket–ligand interactions through an equivariant network design. Statistical assessments and retrospective studies across individual subtasks demonstrate that Delete has an outstanding ability to craft molecules with superior protein-binding energy and reasonable drug-likeness using given fragments or atoms. Subsequently, we utilize Delete to design inhibitors targeting the previously identified LTK protein. Among the ligands designed by Delete, CA-B-1 is successfully validated as a potent (1.36 nM) and selective inhibitor by in vitro and in vivo experiments. This work represents a successful implementation of the powerful structure-based lead optimization model, Delete, for rapid and controllable rational drug design.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Delete is a one-for-all solution for lead optimization.
Fig. 2: Retrospective studies and transfer-learning technique.
Fig. 3: Scaffold-hopping case study of Eg5.
Fig. 4: Application of Causal-Delete on the LTK target.
Fig. 5: CA-B-1 exhibits antitumor efficacy in vitro and in vivo.

Similar content being viewed by others

Data availability

The datasets used in this study are available via Zenodo at https://doi.org/10.5281/zenodo.14586176 (ref. 64). Source data are provided with this paper.

Code availability

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

References

  1. Grabley, S. & Thiericke, R. Drug Discovery from Nature (Springer Science & Business Media, 1998).

  2. Wong, F. et al. Discovery of a structural class of antibiotics with explainable deep learning. Nature 626, 177–185 (2024).

    Article  MATH  Google Scholar 

  3. Liu, G. et al. Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii. Nat. Chem. Biol. 19, 1342–1350 (2023).

    Article  MATH  Google Scholar 

  4. Stokes, J. M. et al. A deep learning approach to antibiotic discovery. Cell 180, 688–702.e613 (2020).

    Article  MATH  Google Scholar 

  5. Godinez, W. J. et al. Design of potent antimalarials with generative chemistry. Nat. Mach. Intell. 4, 180–186 (2022).

    Article  MATH  Google Scholar 

  6. Zhavoronkov, A. et al. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat. Biotechnol. 37, 1038–1040 (2019).

    Article  MATH  Google Scholar 

  7. Li, Y. et al. Generative deep learning enables the discovery of a potent and selective RIPK1 inhibitor. Nat.Com. 13, 6891 (2022).

    Article  MATH  Google Scholar 

  8. Aronson, J. K. & Green, A. R. Me-too pharmaceutical products: History, definitions, examples, and relevance to drug shortages and essential medicines lists. Br. J. Clin. Pharmacol. 86, 2114–2122 (2020).

    Article  MATH  Google Scholar 

  9. Zhang, O. et al. ResGen is a pocket-aware 3D molecular generation model based on parallel multiscale modelling. Nat. Mach. Intell. 5, 1020–1030 (2023).

    Article  MATH  Google Scholar 

  10. Lin, H. et al. DiffBP: generative diffusion of 3D molecules for target protein binding. Chem. Sci. 16, 1417–1431 (2025).

    Article  MATH  Google Scholar 

  11. Schneuing, A. et al. Structure-based drug design with equivariant diffusion models. Nat. Comput. Sci. 4, 899–909 (2024).

    Article  MATH  Google Scholar 

  12. Peng, X. et al. Pocket2mol: efficient molecular sampling based on 3d protein pockets. In Proc. 39th International Conference on Machine Learning 17644–17655 (PMLR, 2022).

  13. Davidson, M. et al. Comparison of one-year efficacy and safety of atorvastatin versus lovastatin in primary hypercholesterolemia. Am. J. Cardiol. 79, 1475–1481 (1997).

    Article  MATH  Google Scholar 

  14. Becnel Boyd, L. et al. Relationships among ciprofloxacin, gatifloxacin, levofloxacin, and norfloxacin MICs for fluoroquinolone-resistant escherichia coli clinical isolates. Antimicrob. Agents Chemother. 53, 229–234 (2009).

    Article  Google Scholar 

  15. Bethke, E. et al. From type I to type II: design, synthesis, and characterization of potent pyrazin-2-ones as DFG-out inhibitors of PDGFRβ. ChemMedChem 11, 2664–2674 (2016).

    Article  MATH  Google Scholar 

  16. Green, H., Koes, D. R. & Durrant, J. D. DeepFrag: a deep convolutional neural network for fragment-based lead optimization. Chem. Sci. 12, 8036–8047 (2021).

    Article  MATH  Google Scholar 

  17. Igashov, I. et al. Equivariant 3D-conditional diffusion model for molecular linker design. Nat. Mach. Intell. 6, 417–427 (2024).

    Article  MATH  Google Scholar 

  18. Jin, J. et al. FFLOM: a flow-based autoregressive model for fragment-to-lead optimization. J. Med. Chem. 66, 10808–10823 (2023).

    Article  Google Scholar 

  19. Hu, C. et al. ScaffoldGVAE: scaffold generation and hopping of drug molecules via a variational autoencoder based on multi-view graph neural networks. J. Cheminform. 15, 91 (2023).

    Article  MATH  Google Scholar 

  20. Loeffler, H. H. et al. Reinvent 4: modern AI–driven generative molecule design. J. Cheminform. 16, 20 (2024).

    Article  MATH  Google Scholar 

  21. Liu, X., Ye, K., van Vlijmen, H. W. T., Ijzerman, A. P. & van Westen, G. J. P. DrugEx v3: scaffold-constrained drug design with graph transformer-based reinforcement learning. J. Cheminf. 15, 24 (2023).

    Article  Google Scholar 

  22. Neves, M. A. C., Totrov, M. & Abagyan, R. Docking and scoring with ICM: the benchmarking results and strategies for improvement. J. Comput. Aided Mol. Des. 26, 675–686 (2012).

    Article  MATH  Google Scholar 

  23. Adeshina, Y. O., Deeds, E. J. & Karanicolas, J. Machine learning classification can reduce false positives in structure-based virtual screening. Proc. Natl Acad. Sci. USA 117, 18477–18488 (2020).

    Article  Google Scholar 

  24. Deng, C. et al. Vector neurons: a general framework for SO(3)-equivariant networks. In Proc. IEEE/CVF International Conference on Computer Vision 12200–12209 (IEEE, 2021).

  25. Jing, B., Eismann, S., Suriana, P., Townshend, R. J. & Dror, R. Learning from protein structure with geometric vector perceptrons. Preprint at https://arxiv.org/abs/2009.01411 (2020).

  26. Mahmood, O., Mansimov, E., Bonneau, R. & Cho, K. Masked graph modeling for molecule generation. Nat. Commun. 12, 3156 (2021).

    Article  MATH  Google Scholar 

  27. Hu, Z., Dong, Y., Wang, K., Chang, K.-W. & Sun Y. Gpt-gnn: generative pre-training of graph neural networks. In Proc. 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 1857–1867 (ACM, 2020).

  28. Izumi, H. et al. The CLIP1–LTK fusion is an oncogenic driver in non‐small‐cell lung cancer. Nature 600, 319–323 (2021).

    Article  MATH  Google Scholar 

  29. Ferla, M. P. et al. Fragmenstein: predicting protein–ligand structures of compounds derived from known crystallographic fragment hits using a strict conserved-binding–based methodology. J. Cheminform. 17, 4 (2025).

    Article  Google Scholar 

  30. Imrie, F., Bradley, A. R., van der Schaar, M. & Deane, C. M. Deep generative models for 3D linker design. J. Chem. Inf. Model. 60, 1983–1995 (2020).

    Article  Google Scholar 

  31. Langevin, M., Minoux, H., Levesque, M. & Bianciotto, M. Scaffold-constrained molecular generation. J. Chem. Inf. Model. 60, 5637–5646 (2020).

    Article  Google Scholar 

  32. Zhang, O. et al. Learning on topological surface and geometric structure for 3D molecular generation. Nat. Comput. Sci. 3, 849–859 (2023).

    Article  MATH  Google Scholar 

  33. Clark, D. E. & Pickett, S. D. Computational methods for the prediction of ‘drug-likeness’. Drug Discov. Today 5, 49–58 (2000).

    Article  MATH  Google Scholar 

  34. Ertl, P. & Schuffenhauer, A. Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. J. Cheminform. 1, 8 (2009).

    Article  MATH  Google Scholar 

  35. Ganesan, A. The impact of natural products upon modern drug discovery. Curr. Opin. Chem. Biol. 12, 306–317 (2008).

    Article  MATH  Google Scholar 

  36. Sangster, J. Octanol‐water partition coefficients of simple organic compounds. J. Phy. Chem. Ref. Data 18, 1111–1229 (1989).

    Article  MATH  Google Scholar 

  37. Gaulton, A. et al. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 40, D1100–D1107 (2012).

    Article  Google Scholar 

  38. Irwin, J. J. & Shoichet, B. K. ZINC - a free database of commercially available compounds for virtual screening. J. Chem. Inf. Model. 45, 177–182 (2005).

    Article  MATH  Google Scholar 

  39. Francoeur, P. G. et al. Three-dimensional convolutional neural networks and a cross-docked data set for structure-based drug design. J. Chem. Inf. Model. 60, 4200–4215 (2020).

    Article  MATH  Google Scholar 

  40. Trott, O. & Olson, A. J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 31, 455–461 (2010).

    Article  MATH  Google Scholar 

  41. Alexandar, S. P., Yennamalli, R. M. & Ulaganathan, V. Coarse grained modelling highlights the binding differences in the two different allosteric sites of the human kinesin EG5 and its implications in inhibitor design. Comput. Biol. Chem. 99, 107708 (2022).

    Article  MATH  Google Scholar 

  42. Zheng, S. et al. Deep scaffold hopping with multimodal transformer neural networks. J. Cheminform. 13, 87 (2021).

    Article  MATH  Google Scholar 

  43. Cooper, A. J., Sequist, L. V. & Lin, J. J. Third-generation EGFR and ALK inhibitors: mechanisms of resistance and management. Nat. Rev. Clin. Oncol. 19, 499–514 (2022).

    Article  Google Scholar 

  44. Roll, J. D. & Reuther, G. W. ALK-activating homologous mutations in LTK induce cellular transformation. PLoS ONE 7, e31733 (2012).

    Article  MATH  Google Scholar 

  45. Roskoski, R. ROS1 protein-tyrosine kinase inhibitors in the treatment of ROS1 fusion protein-driven non-small cell lung cancers. Pharmacol. Res. 121, 202–212 (2017).

    Article  Google Scholar 

  46. Luo, S., Guan, J., Ma, J. & Peng, J. A 3D generative model for structure-based drug design. Preprint at https://arxiv.org/abs/2203.10446 (2022).

  47. Ragoza, M., Masuda, T. & Koes, D. R. Generating 3D molecules conditional on receptor binding sites with deep generative models. Chem. Sci. 13, 2701–2713 (2022).

    Article  Google Scholar 

  48. Hussain, J. & Rea, C. Computationally efficient algorithm to identify matched molecular pairs (MMPs) in large data sets. J. Chem. Inf. Model. 50, 339–348 (2010).

    Article  MATH  Google Scholar 

  49. Bemis, G. W. & Murcko, M. A. The properties of known drugs. 1. Molecular frameworks. J. Med. Chem. 39, 2887–2893 (1996).

    Article  MATH  Google Scholar 

  50. Degen, J., Wegscheid-Gerlach, C., Zaliani, A. & Rarey, M. On the art of compiling and using ‘drug-like’ chemical fragment spaces. ChemMedChem 3, 1503–1507 (2008).

    Article  MATH  Google Scholar 

  51. Varin, T., Schuffenhauer, A., Ertl, P. & Renner, S. Mining for bioactive scaffolds with scaffold networks: improved compound set enrichment from primary screening data. J. Chem. Inf. Model. 51, 1528–1538 (2011).

    Article  Google Scholar 

  52. Gainza, P. et al. Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning. Nat. Methods 17, 184–192 (2020).

    Article  Google Scholar 

  53. Liu, T., Lin, Y., Wen, X., Jorissen, R. N. & Gilson, M. K. BindingDB: a web-accessible database of experimentally determined protein–ligand binding affinities. Nucleic Acids Res. 35, D198–D201 (2007).

    Article  Google Scholar 

  54. Svetnik, V. et al. Boosting: an ensemble learning tool for compound classification and QSAR modeling. J. Chem. Inf. Model. 45, 786–799 (2005).

    Article  MATH  Google Scholar 

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

    Article  MATH  Google Scholar 

  56. Ho, T. K. Random decision forests. In Proc. 3rd International Conference on Document Analysis and Recognition 278–282 (IEEE, 1995).

  57. Chen, T. Xgboost: extreme gradient boosting. R package version 0.4-2 (2015).

  58. Dorogush, A. V., Ershov, V. & Gulin, A. CatBoost: gradient boosting with categorical features support. Preprint at https://arxiv.org/abs/1810.11363 (2018).

  59. Ke, G. et al. Lightgbm: a highly efficient gradient boosting decision tree. In Proc. Advances in Neural Information Processing Systems 3149–3157 (ACM, 2017).

  60. Guo, G., Wang, H., Bell, D., Bi, Y. & Greer, K. KNN model-based approach in classification. In Proc. On The Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE (eds Meersman, R. et al.) 986–996 (Springer, 2003).

  61. Devillers, J. Neural Networks in QSAR and Drug Design (Academic, 1996).

  62. Mordelet, F. & Vert, J. P. A bagging SVM to learn from positive and unlabeled examples. Pattern Recog. Lett. 37, 201–209 (2014).

    Article  MATH  Google Scholar 

  63. Hu, Y. et al. Silicon photonic MEMS switches based on split waveguide crossings. Nat.Commun. 16, 331 (2025).

    Article  MATH  Google Scholar 

  64. Zhang, H. Delete code. Zenodo https://doi.org/10.5281/zenodo.14586175 (2025).

Download references

Acknowledgements

This study was supported by the National Key Research and Development Program of China (grant no. 2021YFE0206400) (to T.H.), the National Natural Science Foundation of China (grant nos. 92370130 (to T.H.), 22220102001 (to T.H.), 82204279 (to P.P.) and 82373791 (to Y.K.)), the Fundamental Research Funds for the Central Universities (grant no. 226-2022-00220) (to P.P.) and the Shenzhen Science and Technology Innovation Commission (grant no. KCXFZ20201221173404013) (to J.C.). This work was financially supported by Baidu Scholarship. We thank C. Guo from the Core Facilities, Zhejiang University School of Medicine for their technical support.

Author information

Author notes

  1. These authors contributed equally: Shicheng Chen, Odin Zhang, Chenran Jiang, Huifeng Zhao, Xujun Zhang.

Authors and Affiliations

  1. College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China

    Shicheng Chen, Odin Zhang, Huifeng Zhao, Xujun Zhang, Qun Su, Zhenxing Wu, Xinyue Wang, Wanglin Qu, Yuanyi Ye, Xin Chai, Ning Wang, Tianyue Wang, Wei Xie, Dan Li, Chang-Yu Hsieh, Yu Kang, Tingjun Hou & Peichen Pan

  2. Pingshan Translational Medicine Center, Shenzhen Bay Laboratory, Shenzhen, Guangdong, China

    Chenran Jiang, Yun Liu, Yuan An, Guanlin Wu, Qianqian Yang & Jiean Chen

  3. Department of Chemistry, The Hong Kong University of Science and Technology, Hong Kong, China

    Mengting Chen & Yong Huang

  4. Westlake University, Hangzhou, China

    Haitao Lin

Authors
  1. Shicheng Chen
    View author publications

    Search author on:PubMed Google Scholar

  2. Odin Zhang
    View author publications

    Search author on:PubMed Google Scholar

  3. Chenran Jiang
    View author publications

    Search author on:PubMed Google Scholar

  4. Huifeng Zhao
    View author publications

    Search author on:PubMed Google Scholar

  5. Xujun Zhang
    View author publications

    Search author on:PubMed Google Scholar

  6. Mengting Chen
    View author publications

    Search author on:PubMed Google Scholar

  7. Yun Liu
    View author publications

    Search author on:PubMed Google Scholar

  8. Qun Su
    View author publications

    Search author on:PubMed Google Scholar

  9. Zhenxing Wu
    View author publications

    Search author on:PubMed Google Scholar

  10. Xinyue Wang
    View author publications

    Search author on:PubMed Google Scholar

  11. Wanglin Qu
    View author publications

    Search author on:PubMed Google Scholar

  12. Yuanyi Ye
    View author publications

    Search author on:PubMed Google Scholar

  13. Xin Chai
    View author publications

    Search author on:PubMed Google Scholar

  14. Ning Wang
    View author publications

    Search author on:PubMed Google Scholar

  15. Tianyue Wang
    View author publications

    Search author on:PubMed Google Scholar

  16. Yuan An
    View author publications

    Search author on:PubMed Google Scholar

  17. Guanlin Wu
    View author publications

    Search author on:PubMed Google Scholar

  18. Qianqian Yang
    View author publications

    Search author on:PubMed Google Scholar

  19. Jiean Chen
    View author publications

    Search author on:PubMed Google Scholar

  20. Wei Xie
    View author publications

    Search author on:PubMed Google Scholar

  21. Haitao Lin
    View author publications

    Search author on:PubMed Google Scholar

  22. Dan Li
    View author publications

    Search author on:PubMed Google Scholar

  23. Chang-Yu Hsieh
    View author publications

    Search author on:PubMed Google Scholar

  24. Yong Huang
    View author publications

    Search author on:PubMed Google Scholar

  25. Yu Kang
    View author publications

    Search author on:PubMed Google Scholar

  26. Tingjun Hou
    View author publications

    Search author on:PubMed Google Scholar

  27. Peichen Pan
    View author publications

    Search author on:PubMed Google Scholar

Contributions

S.C. designed and performed the in vitro and in vivo experiments. O.Z. contributed to the Delete idea and code. H.Z. contributed to the retrospective studies. C.J., M.C., Y.L., Y.A., G.W., Q.Y., J.C. and Y.H. contributed to the synthesis of molecules. X.Z. contributed to the comparative model reproduction. Z.W. contributed to the ADMET analysis of generated molecules. Q.S., X.W., W.Q., Y.Y., X.C., N.W., T.W., H.L. and D.L. provided advice on experimental design. W.X., C.-Y.H. and Y.K. contributed to the manuscript revision and experimental design. T.H. and P.P. contributed to the essential financial support and conception and were responsible for the overall quality.

Corresponding authors

Correspondence to Chang-Yu Hsieh, Yong Huang, Yu Kang, Tingjun Hou or Peichen Pan.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Machine Intelligence thanks Ramil Nugmanov 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.

Supplementary information

Supplementary Information (download PDF )

Supplementary Discussion, Figs. 1–11, Tables 1–3 and Methods.

Reporting Summary (download PDF )

Source data

Source Data Figs. 4 and 5 (download XLSX )

Souce data, unprocessed western blots, statistical source data.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, S., Zhang, O., Jiang, C. et al. Deep lead optimization enveloped in protein pocket and its application in designing potent and selective ligands targeting LTK protein. Nat Mach Intell 7, 448–458 (2025). https://doi.org/10.1038/s42256-025-00997-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s42256-025-00997-w

This article is cited by

Search

Advanced search

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