Skip to main content

Recent Deep Learning Applications to Structure-Based Drug Design

  • Protocol
  • First Online:
Computational Drug Discovery and Design

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2714))

Abstract

Identification and optimization of small molecules that bind to and modulate protein function is a crucial step in the early stages of drug development. For decades, this process has benefitted greatly from the use of computational models that can provide insights into molecular binding affinity and optimization. Over the past several years, various types of deep learning models have shown great potential in improving and enhancing the performance of traditional computational methods. In this chapter, we provide an overview of recent deep learning–based developments with applications in drug discovery. We classify these methods into four subcategories dependent on the task each method is aiming to solve. For each subcategory, we provide the general framework of the approach and discuss individual methods.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Protocol
USD 49.95
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. Venkatraman V, Chakravarthy PR, Kihara D (2009) Application of 3D Zernike descriptors to shape-based ligand similarity searching. J Cheminform 1:19. https://doi.org/10.1186/1758-2946-1-19

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Sharma V, Wakode S, Kumar H (2021) Chapter 2 – structure- and ligand-based drug design: concepts, approaches, and challenges. In: Sharma N, Ojha H, Raghav PK, Goyal RK (eds) Chemoinformatics and bioinformatics in the pharmaceutical sciences. Academic Press, pp 27–53

    Chapter  Google Scholar 

  3. Tropsha A (2010) Best practices for QSAR model development, validation, and exploitation. Mol Inform 29:476–488. https://doi.org/10.1002/minf.201000061

    Article  CAS  PubMed  Google Scholar 

  4. Wang D, Yu J, Chen L et al (2021) A hybrid framework for improving uncertainty quantification in deep learning-based QSAR regression modeling. J Cheminform 13:69. https://doi.org/10.1186/s13321-021-00551-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Schaller D, Šribar D, Noonan T et al (2020) Next generation 3D pharmacophore modeling. WIREs Comput Mol Sci 10:e1468. https://doi.org/10.1002/wcms.1468

    Article  CAS  Google Scholar 

  6. Shin W-H, Christoffer CW, Wang J, Kihara D (2016) PL-PatchSurfer2: improved local surface matching-based virtual screening method that is tolerant to target and ligand structure variation. J Chem Inf Model 56:1676–1691. https://doi.org/10.1021/acs.jcim.6b00163

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Shin W-H, Kihara D (2019) Predicting binding poses and affinity ranking in D3R Grand Challenge using PL-PatchSurfer2.0. J Comput Aided Mol Des 33:1083–1094. https://doi.org/10.1007/s10822-019-00222-y

    Article  CAS  PubMed  Google Scholar 

  8. Halgren T (2007) New method for fast and accurate binding-site identification and analysis. Chem Biol Drug Des 69:146–148. https://doi.org/10.1111/j.1747-0285.2007.00483.x

    Article  CAS  PubMed  Google Scholar 

  9. Jiménez J, Doerr S, Martínez-Rosell G et al (2017) DeepSite: protein-binding site predictor using 3D-convolutional neural networks. Bioinformatics 33:3036–3042. https://doi.org/10.1093/bioinformatics/btx350

    Article  CAS  PubMed  Google Scholar 

  10. Shin W-H, Kumazawa K, Imai K et al (2020) Current challenges and opportunities in designing protein-protein interaction targeted drugs. Adv Appl Bioinforma Chem 13:11–25. https://doi.org/10.2147/AABC.S235542

    Article  Google Scholar 

  11. Shin W-H, Christoffer CW, Kihara D (2017) In silico structure-based approaches to discover protein-protein interaction-targeting drugs. Methods 131:22–32. https://doi.org/10.1016/j.ymeth.2017.08.006

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Pagadala NS, Syed K, Tuszynski J (2017) Software for molecular docking: a review. Biophys Rev 9:91–102. https://doi.org/10.1007/s12551-016-0247-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Hoffer L, Muller C, Roche P, Morelli X (2018) Chemistry-driven hit-to-lead optimization guided by structure-based approaches. Mol Inform 37:1800059. https://doi.org/10.1002/minf.201800059

    Article  CAS  Google Scholar 

  14. Bian Y, Xie X-Q (Sean) (2018) Computational fragment-based drug design: current trends, strategies, and applications. AAPS J 20:59. https://doi.org/10.1208/s12248-018-0216-7

  15. Jones H, Rowland-Yeo K (2013) Basic concepts in physiologically based pharmacokinetic modeling in drug discovery and development. CPT Pharmacometrics Syst Pharmacol 2:e63. https://doi.org/10.1038/psp.2013.41

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Anup N, Gadeval A, Rajpoot K, Tekade RK (2021) Chapter 24 – software used in ADME computation. In: Tekade RK (ed) Biopharmaceutics and pharmacokinetics considerations. Academic Press, pp 699–708

    Chapter  Google Scholar 

  17. Jarada TN, Rokne JG, Alhajj R (2020) A review of computational drug repositioning: strategies, approaches, opportunities, challenges, and directions. J Cheminform 12:46. https://doi.org/10.1186/s13321-020-00450-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Ertl P, Schuffenhauer A (2009) Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. J Cheminform 1:8. https://doi.org/10.1186/1758-2946-1-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Weininger D (1988) SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inf Comput Sci 28:31–36. https://doi.org/10.1021/ci00057a005

    Article  CAS  Google Scholar 

  20. Morris GM, Huey R, Lindstrom W et al (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30:2785–2791. https://doi.org/10.1002/jcc.21256

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31:455–461. https://doi.org/10.1002/jcc.21334

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Friesner RA, Murphy RB, Repasky MP et al (2006) Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J Med Chem 49:6177–6196. https://doi.org/10.1021/jm051256o

    Article  CAS  PubMed  Google Scholar 

  23. Guedes IA, Pereira FSS, Dardenne LE (2018) Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges. Front Pharmacol 9:1089

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Li H, Leung K-S, Wong M-H, Ballester PJ (2015) Improving AutoDock Vina using random forest: the growing accuracy of binding affinity prediction by the effective exploitation of larger data sets. Mol Inform 34:115–126. https://doi.org/10.1002/minf.201400132

    Article  CAS  PubMed  Google Scholar 

  25. Su M, Yang Q, Du Y et al (2019) Comparative assessment of scoring functions: the CASF-2016 update. J Chem Inf Model 59:895–913. https://doi.org/10.1021/acs.jcim.8b00545

    Article  CAS  PubMed  Google Scholar 

  26. Brown BP, Mendenhall J, Geanes AR, Meiler J (2021) General purpose structure-based drug discovery neural network score functions with human-interpretable pharmacophore maps. J Chem Inf Model 61:603–620. https://doi.org/10.1021/acs.jcim.0c01001

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Ashtawy HM, Mahapatra NR (2015) BgN-Score and BsN-Score: bagging and boosting based ensemble neural networks scoring functions for accurate binding affinity prediction of protein-ligand complexes. BMC Bioinformatics 16:S8. https://doi.org/10.1186/1471-2105-16-S4-S8

    Article  PubMed  PubMed Central  Google Scholar 

  28. Zheng L, Fan J, Mu Y (2019) OnionNet: a multiple-layer intermolecular-contact-based convolutional neural network for protein–ligand binding affinity prediction. ACS Omega 4:15956–15965. https://doi.org/10.1021/acsomega.9b01997

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Stepniewska-Dziubinska MM, Zielenkiewicz P, Siedlecki P (2018) Development and evaluation of a deep learning model for protein–ligand binding affinity prediction. Bioinformatics 34:3666–3674. https://doi.org/10.1093/bioinformatics/bty374

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Moon S, Zhung W, Yang S et al (2022) PIGNet: a physics-informed deep learning model toward generalized drug–target interaction predictions. Chem Sci 13:3661–3673. https://doi.org/10.1039/D1SC06946B

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Méndez-Lucio O, Ahmad M, del Rio-Chanona EA, Wegner JK (2021) A geometric deep learning approach to predict binding conformations of bioactive molecules. Nat Mach Intell 3:1033–1039. https://doi.org/10.1038/s42256-021-00409-9

    Article  Google Scholar 

  32. Liu Z, Li Y, Han L et al (2015) PDB-wide collection of binding data: current status of the PDBbind database. Bioinformatics 31:405–412. https://doi.org/10.1093/bioinformatics/btu626

    Article  CAS  PubMed  Google Scholar 

  33. Jiang H, Wang J, Cong W et al (2022) Predicting protein–ligand docking structure with graph neural network. J Chem Inf Model 62(12):2923–2932

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Wang J, Dokholyan NV (2019) MedusaDock 2.0: efficient and accurate protein–ligand docking with constraints. J Chem Inf Model 59:2509–2515. https://doi.org/10.1021/acs.jcim.8b00905

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Corso G, Stärk H, Jing B, Barzilay R, Jaakkola T (2022) DiffDock: diffusion steps, twists, and turns for molecular docking. arXiv:2210.01776v2 [q-bio.BM]. https://doi.org/10.48550/arXiv.2210.01776

  36. Ho J, Jain A, Abbeel P (2020) Denoising diffusion probabilistic models. arXiv:2006.11239v2 [cs.LG]. https://doi.org/10.48550/arXiv.2006.11239

  37. Radford A, Kim JW, Hallacy C, et al (2021) Learning transferable visual models from natural language supervision. arXiv:2103.00020v1 [cs.CV]. https://doi.org/10.48550/arXiv.2103.00020

  38. Ramesh A, Pavlov M, Goh G, et al (2021) Zero-shot text-to-image generation. In: Proceedings of the 38th international conference on machine learning PMLR, vol 139, pp 8821–8831

    Google Scholar 

  39. Ramesh A, Dhariwal P, Nichol A Chu C, Chen M (2022) Hierarchical text-conditional image generation with CLIP latents. arXiv:2204.06125v1 [cs.CV]. https://doi.org/10.48550/arXiv.2204.06125

  40. Yang L, Zhang Z, Song Y, et al (2022) Diffusion models: a comprehensive survey of methods and applications. arXiv:2209.00796v10 [cs.LG]. https://doi.org/10.48550/arXiv.2209.00796

  41. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 23:3–25. https://doi.org/10.1016/S0169-409X(96)00423-1

    Article  CAS  Google Scholar 

  42. Zhang Z, Li F, Guan J et al (2022) GANs for molecule generation in drug design and discovery. In: Razavi-Far R, Ruiz-Garcia A, Palade V, Schmidhuber J (eds) Generative adversarial learning: architectures and applications. Springer International Publishing, Cham, pp 233–273

    Google Scholar 

  43. Maziarka Ł, Pocha A, Kaczmarczyk J et al (2020) Mol-CycleGAN: a generative model for molecular optimization. J Cheminform 12:2. https://doi.org/10.1186/s13321-019-0404-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Zhou Z, Kearnes S, Li L et al (2019) Optimization of molecules via deep reinforcement learning. Sci Rep 9:10752. https://doi.org/10.1038/s41598-019-47148-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Bickerton GR, Paolini GV, Besnard J et al (2012) Quantifying the chemical beauty of drugs. Nat Chem 4:90–98. https://doi.org/10.1038/nchem.1243

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Green H, Koes DR, Durrant JD (2021) DeepFrag: a deep convolutional neural network for fragment-based lead optimization. Chem Sci 12:8036–8047. https://doi.org/10.1039/D1SC00163A

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Green H, Durrant JD (2021) DeepFrag: an open-source browser app for deep-learning lead optimization. J Chem Inf Model 61:2523–2529. https://doi.org/10.1021/acs.jcim.1c00103

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Hu L, Benson ML, Smith RD et al (2005) Binding MOAD (Mother Of All Databases). Proteins Struct Funct Bioinform 60:333–340. https://doi.org/10.1002/prot.20512

    Article  CAS  Google Scholar 

  49. Imrie F, Bradley AR, van der Schaar M, Deane CM (2020) Deep generative models for 3D linker design. J Chem Inf Model 60:1983–1995. https://doi.org/10.1021/acs.jcim.9b01120

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Yang Y, Zheng S, Su S et al (2020) SyntaLinker: automatic fragment linking with deep conditional transformer neural networks. Chem Sci 11:8312–8322. https://doi.org/10.1039/D0SC03126G

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Shi W, Singha M, Srivastava G et al (2022) Pocket2Drug: an encoder-decoder deep neural network for the target-based drug design. Front Pharmacol 13:837715

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Krenn M, Häse F, Nigam A et al (2020) Self-referencing embedded strings (SELFIES): a 100% robust molecular string representation. Mach Learn Sci Technol 1:045024. https://doi.org/10.1088/2632-2153/aba947

    Article  Google Scholar 

  53. Krishnan SR, Bung N, Vangala SR et al (2022) De novo structure-based drug design using deep learning. J Chem Inf Model 62:5100–5109. https://doi.org/10.1021/acs.jcim.1c01319

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

This work was partly supported by the National Institutes of Health (R01GM133840, 3R01 GM133840-02S1) and the National Science Foundation (DMS2151678, DBI2003635, DBI2146026, CMMI1825941, IIS2211598, and MCB1925643). JV is supported by an NIGMS-funded predoctoral fellowship (T32 GM132024).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Daisuke Kihara .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Verburgt, J., Jain, A., Kihara, D. (2024). Recent Deep Learning Applications to Structure-Based Drug Design. In: Gore, M., Jagtap, U.B. (eds) Computational Drug Discovery and Design. Methods in Molecular Biology, vol 2714. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3441-7_13

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-3441-7_13

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3440-0

  • Online ISBN: 978-1-0716-3441-7

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics