Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

AI Deep Learning Generative Models for Drug Discovery

  • Chapter
  • First Online:
Applications of Generative AI

  • 2533 Accesses

Abstract

Artificial intelligence (AI) deep learning generative models play an increasingly important role in drug design. Developments of different drug generative models can save capital and time to promote new drug discovery. AI deep learning generative models can be divided into different generative models based on the different levels of dimensional features of receptors and ligands such as SMILES generative models, molecular graph generative models, and 3D molecule generative models. Besides, based on the different algorithms, AI deep learning generative models for drug discovery can be roughly classified as variational autoencoder generative model, generative adversarial network generative model, and flow based generative model, and diffusion generative model. In this chapter, the classification, general mathematical methods, and research reports of AI deep learning generative models are summarized based on the different levels of dimensional features and algorithms. This chapter proposes an interesting topic and a deep understanding of AI deep learning generative models for the scientific community.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Cherkasov, A., Muratov, E. N., Fourches, D., Varnek, A., Baskin, I. I., Cronin, M., et al. (2014). QSAR modeling: Where have you been? Where are you going to? Journal of Medicinal Chemistry, 57(12), 4977–5010. https://doi.org/10.1021/jm4004285

    Article  Google Scholar 

  2. Schneider, G., & Fechner, U. (2005). Computer-based de novo design of drug-like molecules. Nature Reviews. Drug Discovery, 4(8), 649–663. https://doi.org/10.1038/nrd1799

    Article  Google Scholar 

  3. Bai, Q., Tan, S., Xu, T., Liu, H., Huang, J., & Yao, X. (2021). MolAICal: A soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm. Briefings in bioinformatics, 22(3), bbaa161.

    Google Scholar 

  4. Wang, R., Gao, Y., & Lai, L. (2000). LigBuilder: A multi-purpose program for structure-based drug design. Molecular Modeling Annual, 6(7), 498–516. https://doi.org/10.1007/s0089400060498

    Article  Google Scholar 

  5. Cheron, N., Jasty, N., & Shakhnovich, E. I. (2016). OpenGrowth: An automated and rational algorithm for finding new protein ligands. Journal of Medicinal Chemistry, 59(9), 4171–4188. https://doi.org/10.1021/acs.jmedchem.5b00886

    Article  Google Scholar 

  6. Bai, Q., Ma, J., Liu, S., Xu, T., Banegas-Luna, A. J., Pérez-Sánchez, H., et al. (2021). WADDAICA: A webserver for aiding protein drug design by artificial intelligence and classical algorithm. Computational and Structural Biotechnology Journal, 19, 3573–3579. https://doi.org/10.1016/j.csbj.2021.06.017

    Article  Google Scholar 

  7. Bai, Q., Liu, S., Tian, Y., Xu, T., Banegas-Luna, A. J., Pérez-Sánchez, H., et al. (2022). Application advances of deep learning methods for de novo drug design and molecular dynamics simulation. Wiley Interdisciplinary Reviews: Computational Molecular Science, 12(3), e1581. https://doi.org/10.1002/wcms.1581

    Article  Google Scholar 

  8. Yang, S.-Q., Ye, Q., Ding, J.-J., Yin, M.-Z., Lu, A.-P., Chen, X., et al. (2021). Current advances in ligand-based target prediction. WIREs Computational Molecular Science, 11(3), e1504. https://doi.org/10.1002/wcms.1504

    Article  Google Scholar 

  9. Polishchuk, P. G., Madzhidov, T. I., & Varnek, A. (2013). Estimation of the size of drug-like chemical space based on GDB-17 data. Journal of Computer-Aided Molecular Design, 27(8), 675–679. https://doi.org/10.1007/s10822-013-9672-4

    Article  Google Scholar 

  10. Stumpfe, D., & Bajorath, J. (2012). Exploring activity cliffs in medicinal chemistry. Journal of Medicinal Chemistry, 55(7), 2932–2942. https://doi.org/10.1021/jm201706b

    Article  Google Scholar 

  11. Wiswesser, W. J. (1985). Historic development of chemical notations. Journal of Chemical Information and Computer Sciences, 25(3), 258–263. https://doi.org/10.1021/ci00047a023

    Article  Google Scholar 

  12. Wang, Y., Li, Z., & Farimani, A. B. (2022). Graph neural networks for molecules. arXiv preprint arXiv:220905582.

    Google Scholar 

  13. Xu, Y., Lin, K., Wang, S., Wang, L., Cai, C., Song, C., et al. (2019). Deep learning for molecular generation. Future Medicinal Chemistry, 11(6), 567–597. https://doi.org/10.4155/fmc-2018-0358

    Article  Google Scholar 

  14. Li, Y., Vinyals, O., Dyer, C., Pascanu, R., & Battaglia, P. Learning deep generative models of graphs. arXiv preprint arXiv:180303324.

    Google Scholar 

  15. Satorras, V. G., Hoogeboom, E., & Welling M. (2021). E(n) equivariant graph neural networks. In International Conference on Machine Learning: PMLR (pp. 9323–9332).

    Google Scholar 

  16. Kingma, D. P., & Welling, M. (2013). Auto-encoding variational bayes. arXiv preprint arXiv:13126114.

    Google Scholar 

  17. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S, Courville, A., & Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems (pp. 2672–2680).

    Google Scholar 

  18. Rezende, D., & Mohamed, S. (2015). Variational inference with normalizing flows. In International Conference on Machine Learning: PMLR (pp. 1530–1538).

    Google Scholar 

  19. Ho, J., Jain, A., & Abbeel, P. (2020). Denoising diffusion probabilistic models. In Advances in neural information processing systems (Vol. 33, pp. 6840–6451).

    Google Scholar 

  20. Segler, M. H., Kogej, T., Tyrchan, C., & Waller, M. P. (2018). Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS Central Science, 4(1), 120–131.

    Article  Google Scholar 

  21. Bagal, V., Aggarwal, R., Vinod, P., & Priyakumar, U. D. (2021). MolGPT: Molecular generation using a transformer-decoder model. Journal of Chemical Information and Modeling, 62(9), 2064–2076.

    Article  Google Scholar 

  22. Jin, W., Barzilay, R., & Jaakkola, T. (2018). Junction tree variational autoencoder for molecular graph generation. In International Conference on Machine Learning: PMLR (pp. 2323–2332).

    Google Scholar 

  23. Jin, W., Barzilay, R., & Jaakkola, T. (2020). Hierarchical generation of molecular graphs using structural motifs. In International Conference on Machine Learning: PMLR (pp. 4839–4848).

    Google Scholar 

  24. Shi, C., Xu, M., Zhu, Z., Zhang, W., Zhang, M., & Tang, J. (2020). Graphaf: A flow-based autoregressive model for molecular graph generation. arXiv preprint arXiv:200109382.

    Google Scholar 

  25. Dinh, L., Sohl-Dickstein, J., & Bengio, S. (2016). Density estimation using real nvp. arXiv preprint arXiv:160508803.

    Google Scholar 

  26. Vignac, C., Krawczuk, I., Siraudin, A., Wang, B., Cevher, V., & Frossard, P. (2022). DiGress: Discrete Denoising diffusion for graph generation. arXiv preprint arXiv:220914734.

    Google Scholar 

  27. Ragoza, M., Masuda, T., & Koes, D. R. (2022). Generating 3D molecules conditional on receptor binding sites with deep generative models. Chemical Science, 13(9), 2701–2713.

    Article  Google Scholar 

  28. Luo, Y., & Ji, S. (2022). An autoregressive flow model for 3D molecular geometry generation from scratch. In International Conference on Learning Representations (ICLR).

    Google Scholar 

  29. Liu, M., Luo, Y., Uchino, K., Maruhashi, K., & Ji. S. (2022). Generating 3D molecules for target protein binding. arXiv preprint arXiv:220409410.

    Google Scholar 

  30. Hoogeboom, E., Satorras, V. G., Vignac, C., & Welling, M. (2022). Equivariant diffusion for molecule generation in 3D. In International Conference on Machine Learning: PMLR (pp. 8867–8887).

    Google Scholar 

  31. Huang, L., Zhang, H., Xu, T., & Wong, K.-C. (2022). MDM: Molecular diffusion model for 3D molecule generation. arXiv preprint arXiv:220905710.

    Google Scholar 

  32. Huang, L. (2023). A dual diffusion model enables 3D binding bioactive molecule generation and lead optimization given target pockets. bioRxiv 2023:2023.01.28.526011.

    Google Scholar 

  33. Xu, M., Powers, A., Dror, R., Ermon, S., & Leskovec, J. (2023). Geometric latent diffusion models for 3D molecule generation. arXiv preprint arXiv:230501140.

    Google Scholar 

  34. Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10684–10695).

    Google Scholar 

  35. Zhang, Z., Min, Y., Zheng, S., & Liu, Q. (2023). Molecule generation for target protein binding with structural motifs. In The Eleventh International Conference on Learning Representations.

    Google Scholar 

  36. Huang, Y., Peng, X., Ma, J., & Zhang, M. (2022). 3Dlinker: An E(3) equivariant variational autoencoder for molecular linker design. arXiv preprint arXiv:220507309.

    Google Scholar 

  37. Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016). Improved techniques for training gans. In Advances in neural information processing systems (Vol. 29).

    Google Scholar 

  38. Arjovsky, M., & Bottou, L. (2017). Towards principled methods for training generative adversarial networks. arXiv preprint arXiv:170104862.

    Google Scholar 

  39. Arjovsky, M., Chintala, S., & Bottou, L. (2017). Wasserstein GAN. arXiv preprint arXiv:170107875.

    Google Scholar 

  40. Levina, E., & Bickel, P. (2001). The earth mover’s distance is the mallows distance: Some insights from statistics. In Proceedings Eighth IEEE International Conference on Computer Vision (ICCV 2001) (pp. 251–256). IEEE.

    Google Scholar 

  41. Bai, Q. (2020). Research and development of MolAICal for drug design via deep learning and classical programming. arXiv preprint arXiv:200609747.

    Google Scholar 

  42. Kusner, M. J., Paige, B., & Hernández-Lobato, J. M. (2017). Grammar variational autoencoder. In International Conference on Machine Learning: PMLR (pp. 1945–1954).

    Google Scholar 

  43. Dinh, L., Sohl-Dickstein, J., & Bengio, S. (2016). Density estimation using real NVP. arXiv:160508803.

    Google Scholar 

  44. Kingma, D. P., & Dhariwal, P. J. (2018). Glow: Generative flow with invertible 1x1 convolutions. Adv Neural Inf Process Syst. 2018;31.

    Google Scholar 

  45. Papamakarios G, Pavlakou T, Murray IJAinips. Masked autoregressive flow for density estimation. In Advances in neural information processing systems (Vol. 30).

    Google Scholar 

  46. Frey, N. C., & Gadepally, V., & Ramsundar, B. (2022). Fastflows: Flow-based models for molecular graph generation. arXiv preprint arXiv:220112419.

    Google Scholar 

  47. Madhawa, K., Ishiguro, K., Nakago, K., & Abe, M. GraphNVP: An invertible flow model for generating molecular graphs. arXiv preprint arXiv:190511600.

    Google Scholar 

  48. Welling, M., & The, Y. W. (2011). Bayesian learning via stochastic gradient Langevin dynamics. In Proceedings of the 28th International conference on Machine Learning (ICML-11) (pp. 681–688).

    Google Scholar 

  49. Ho, J., Jain, A., & Abbeel, P. J. A. (2020). Denoising diffusion probabilistic models. arXiv:200611239.

    Google Scholar 

  50. Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., & Ganguli, S. (2015). Deep unsupervised learning using nonequilibrium thermodynamics. In International Conference on Machine Learning: PMLR (pp. 2256–2565).

    Google Scholar 

  51. Song, Y., & Ermon, S. J. (2019). Generative modeling by estimating gradients of the data distribution. In Advances in neural information processing systems (Vol. 32).

    Google Scholar 

  52. Corso, G., Stärk, H., Jing, B., Barzilay, R., & Jaakkola, T. (2022). Diffdock: Diffusion steps, twists, and turns for molecular docking. arXiv preprint arXiv:221001776.

    Google Scholar 

  53. Chen, X., Mishra, N., Rohaninejad, M., & Abbeel, P. (2018). Pixelsnail: An improved autoregressive generative model. In International Conference on Machine Learning: PMLR (pp. 864–872).

    Google Scholar 

  54. LeCun, Y., Chopra, S., Ranzato, M., & Huang, F.-J. (2007). Energy-based models in document recognition and computer vision. In Ninth International Conference on Document Analysis and Recognition (ICDAR 2007) (pp. 337–341). IEEE.

    Google Scholar 

  55. Xie, J., Zhu, S.-C., & Wu, Y. N. (2019). Learning energy-based spatial-temporal generative convnets for dynamic patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(2), 516–531.

    Article  Google Scholar 

  56. Van Den Oord, A., Kalchbrenner, N., & Kavukcuoglu, K. (2016). Pixel recurrent neural networks. In International Conference on Machine Learning: PMLR (pp. 1747–1756).

    Google Scholar 

  57. Gao, R., Song, Y., Poole, B., & Wu, Y. N., & Kingma, D. P. (2020). Learning energy-based models by diffusion recovery likelihood. arXiv preprint arXiv:201208125.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Basic Medical Sciences, Lanzhou University, Lanzhou, 730000, Gansu, People’s Republic of China

    Qifeng Bai & Jian Ma

  2. Tencent AI Lab, Shenzhen, People’s Republic of China

    Tingyang Xu

Authors
  1. Qifeng Bai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jian Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tingyang Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qifeng Bai .

Editor information

Editors and Affiliations

  1. Department of Game Design, Uppsala Universitet, Visby, Sweden

    Zhihan Lyu

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Bai, Q., Ma, J., Xu, T. (2024). AI Deep Learning Generative Models for Drug Discovery. In: Lyu, Z. (eds) Applications of Generative AI. Springer, Cham. https://doi.org/10.1007/978-3-031-46238-2_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-46238-2_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-46237-5

  • Online ISBN: 978-3-031-46238-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation