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Ultrahigh Throughput Protein–Ligand Docking with Deep Learning

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Artificial Intelligence in Drug Design

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2390))

Abstract

Ultrahigh-throughput virtual screening (uHTVS) is an emerging field linking together classical docking techniques with high-throughput AI methods. We outline mechanistic docking models’ goals and successes. We present different AI accelerated workflows for uHTVS, mainly through surrogate docking models. We showcase a novel feature representation technique, molecular depictions (images), as a surrogate model for docking. Along with a discussion on analyzing screens using regression enrichment surfaces at the tens of billion scale, we outline a future for uHTVS screening pipelines with deep learning.

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References

  1. Rester U (2008) From virtuality to reality-Virtual screening in lead discovery and lead optimization: a medicinal chemistry perspective. Curr Opin Drug Discov Devel 11:559

    CAS  PubMed  Google Scholar 

  2. Ltd E Enamine REAL Space

    Google Scholar 

  3. Lahue BR, Glick M, Tudor M et al (2020) Diversity & tractability revisited in collaborative small molecule phenotypic screening library design. Bioorg Med Chem 28:115192

    Article  CAS  PubMed  Google Scholar 

  4. Paricharak S, Méndez-Lucio O, Chavan Ravindranath A et al (2018) Data-driven approaches used for compound library design, hit triage and bioactivity modeling in high-throughput screening. Brief Bioinform 19:277–285

    CAS  PubMed  Google Scholar 

  5. Lyu J, Wang S, Balius TE et al (2019) Ultra-large library docking for discovering new chemotypes. Nature 566:224–229

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Jia X, Lynch A, Huang Y et al (2019) Anthropogenic biases in chemical reaction data hinder exploratory inorganic synthesis. Nature 573:251–255

    Article  CAS  PubMed  Google Scholar 

  7. Su AI, Lorber DM, Weston GS et al (2001) Docking molecules by families to increase the diversity of hits in database screens: computational strategy and experimental evaluation. Proteins 42:279–293

    Article  CAS  PubMed  Google Scholar 

  8. Polishchuk PG, Madzhidov TI, Varnek A (2013) Estimation of the size of drug-like chemical space based on GDB-17 data. J Comput Aided Mol Des 27:675–679

    Article  CAS  PubMed  Google Scholar 

  9. Bolte M, Hogan CJ (1995) Conflict over the age of the Universe. Nature 376:399–402

    Article  CAS  Google Scholar 

  10. Schneider G (2010) Virtual screening: an endless staircase? Nat Rev Drug Discov 9:273–276

    Article  CAS  PubMed  Google Scholar 

  11. McInnes C (2007) Virtual screening strategies in drug discovery. Curr Opin Chem Biol 11:494–502

    Article  CAS  PubMed  Google Scholar 

  12. Sliwoski G, Kothiwale S, Meiler J, Lowe EW Jr (2014) Computational methods in drug discovery. Pharmacol Rev 66:334–395. https://doi.org/10.1124/pr.112.007336

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Sakkiah S, Thangapandian S, John S et al (2010) 3D QSAR pharmacophore based virtual screening and molecular docking for identification of potential HSP90 inhibitors. Eur J Med Chem 45:2132–2140

    Article  CAS  PubMed  Google Scholar 

  14. Sun H (2008) Pharmacophore-based virtual screening. Curr Med Chem 15:1018–1024

    Article  CAS  PubMed  Google Scholar 

  15. Willett P, Barnard JM, Downs GM (1998) Chemical similarity searching. J Chem Inf Comput Sci 38:983–996

    Article  CAS  Google Scholar 

  16. Kumar A, Zhang KY (2018) Advances in the development of shape similarity methods and their application in drug discovery. Front Chem 6:315

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Coley CW, Barzilay R, Green WH et al (2017) Convolutional embedding of attributed molecular graphs for physical property prediction. J Chem Inf Model 57:1757–1772

    Article  CAS  PubMed  Google Scholar 

  18. Liu Z, Du J, Fang J, et al (2019) DeepScreening: a deep learning-based screening web server for accelerating drug discovery Database 2019

    Google Scholar 

  19. Zhou H, Skolnick J (2013) FINDSITEcomb: a threading/structure-based, proteomic-scale virtual ligand screening approach. J Chem Inf Model 53:230–240

    Article  CAS  PubMed  Google Scholar 

  20. Oprea TI (2000) Current trends in lead discovery: are we looking for the appropriate properties? Mol Divers 5:199–208

    Article  CAS  Google Scholar 

  21. Verdonk ML, Berdini V, Hartshorn MJ et al (2004) Virtual screening using protein- ligand docking: avoiding artificial enrichment. J Chem Inf Comput Sci 44:793–806

    Article  CAS  PubMed  Google Scholar 

  22. Klebe G (2006) Virtual ligand screening: strategies, perspectives and limitations. Drug Discov Today 11:580–594. https://doi.org/10.1016/j.drudis.2006.05.012

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Sterling T, Irwin JJ (2015) ZINC 15-ligand discovery for everyone. J Chem Inf Model 55:2324–2337

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Shivanyuk A, Ryabukhin S, Tolmachev A et al (2007) Enamine real database: making chemical diversity real. Chem Today 25:58–59

    CAS  Google Scholar 

  25. O’Boyle NM, Banck M, James CA et al (2011) Open Babel: An open chemical toolbox. J Cheminform 3:33. https://doi.org/10.1186/1758-2946-3-33

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Le Guilloux V, Schmidtke P, Tuffery P (2009) Fpocket: an open source platform for ligand pocket detection. BMC Bioinformatics 10:1–11

    Article  Google Scholar 

  27. Bernstein FC, Koetzle TF, Williams GJ et al (1977) The Protein Data Bank: a computer-based archival file for macromolecular structures. Eur J Biochem 80:319–324

    Article  CAS  PubMed  Google Scholar 

  28. Warren GL, Andrews CW, Capelli A-M et al (2006) A critical assessment of docking programs and scoring functions. J Med Chem 49:5912–5931

    Article  CAS  PubMed  Google Scholar 

  29. Cole JC, Murray CW, Nissink JWM et al (2005) Comparing protein–ligand docking programs is difficult. Proteins 60:325–332

    Article  CAS  PubMed  Google Scholar 

  30. Kitchen D, Decornez H, Furr J, Bajorath J (2004) Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov 3:935–949. https://doi.org/10.1038/nrd1549

    Article  CAS  PubMed  Google Scholar 

  31. Ballester PJ, Mitchell JB (2010) A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking. Bioinformatics 26:1169–1175

    Article  CAS  PubMed  Google Scholar 

  32. Mcgann MR, Almond HR, Nicholls A et al (2003) Gaussian docking functions. Biopolymers 68:76–90

    Article  CAS  PubMed  Google Scholar 

  33. Guedes IA, Pereira FS, 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 

  34. Clark RD, Strizhev A, Leonard JM et al (2002) Consensus scoring for ligand/protein interactions. J Mol Graph Model 20:281–295

    Article  CAS  PubMed  Google Scholar 

  35. Meiler J, Baker D (2006) ROSETTALIGAND: Protein–small molecule docking with full side-chain flexibility. Proteins 65:538–548

    Article  CAS  PubMed  Google Scholar 

  36. Razzaghi-Asl N, Sepehri S, Ebadi A et al (2015) Effect of biomolecular conformation on docking simulation: a case study on a potent HIV-1 protease inhibitor. Iran J Pharm Res 14:785

    CAS  PubMed  PubMed Central  Google Scholar 

  37. McGaughey GB, Sheridan RP, Bayly CI et al (2007) Comparison of topological, shape, and docking methods in virtual screening. J Chem Inf Model 47:1504–1519

    Article  CAS  PubMed  Google Scholar 

  38. Francoeur PG, Masuda T, Sunseri J et al (2020) Three-dimensional convolutional neural networks and a cross-docked data set for structure-based drug design. J Chem Inf Model 60:4200–4215. https://doi.org/10.1021/acs.jcim.0c00411

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Sunseri J, King JE, Francoeur PG, Koes DR (2019) Convolutional neural network scoring and minimization in the D3R 2017 community challenge. J Comput Aided Mol Des 33:19–34. https://doi.org/10.1007/s10822-018-0133-y

    Article  CAS  PubMed  Google Scholar 

  40. Xu Z, Wauchope OR, Frank AT (2020) Navigating chemical space by interfacing generative artificial intelligence and molecular docking. bioRxiv

    Google Scholar 

  41. Li X, Xu Y, Yao H, Lin K (2020) Chemical space exploration based on recurrent neural networks: applications in discovering kinase inhibitors. J Chem 12:1–13

    Google Scholar 

  42. Landrum G et al (2006) RDKit: open-source cheminformatics

    Google Scholar 

  43. Pechan I, Feher B (2011) Molecular docking on FPGA and GPU platforms. In: 2011 21st international conference on field programmable logic and applications. IEEE, pp 474–477

    Google Scholar 

  44. LeGrand S, Scheinberg A, Tillack AF, et al (2020) GPU-accelerated drug discovery with docking on the summit supercomputer: porting, optimization, and application to COVID-19 research. In: Proceedings of the 11th ACM international conference on bioinformatics, computational biology and health informatics, pp 1–10

    Google Scholar 

  45. Zlateski A, Lee K, Seung HS (2016) ZNNi: maximizing the inference throughput of 3D convolutional networks on CPUs and GPUs. In: SC’16: Proceedings of the international conference for high performance computing, networking, storage and analysis. IEEE, pp 854–865

    Google Scholar 

  46. Lee H, Merzky A, Tan L, et al (2020) Scalable HPC and AI infrastructure for COVID-19 therapeutics. arXiv preprint arXiv:201010517

    Google Scholar 

  47. Wright D, Devitt-Lee A, Clyde A, et al (2019) Combining molecular simulation and machine learning to INSPIRE improved cancer therapy. In: CompBioMed conference 2019

    Google Scholar 

  48. Lu S-Y, Jiang Y-J, Lv J et al (2010) Molecular docking and molecular dynamics simulation studies of GPR40 receptor–agonist interactions. J Mol Graph Model 28:766–774

    Article  CAS  PubMed  Google Scholar 

  49. Schütt KT, Sauceda HE, Kindermans P-J et al (2018) SchNet—a deep learning architecture for molecules and materials. J Chem Phys 148:241722

    Article  PubMed  CAS  Google Scholar 

  50. Bartók AP, De S, Poelking C et al (2017) Machine learning unifies the modeling of materials and molecules. Sci Adv 3:e1701816

    Article  PubMed  PubMed Central  Google Scholar 

  51. Pastor M, Cruciani G, McLay I et al (2000) GRid-INdependent descriptors (GRIND): a novel class of alignment-independent three-dimensional molecular descriptors. J Med Chem 43:3233–3243

    Article  CAS  PubMed  Google Scholar 

  52. Yap CW (2011) PaDEL-descriptor: An open source software to calculate molecular descriptors and fingerprints. J Comput Chem 32:1466–1474

    Article  CAS  PubMed  Google Scholar 

  53. Todeschini R, Consonni V (2008) Handbook of molecular descriptors. John Wiley & Sons, Hoboken, NJ

    Google Scholar 

  54. Moriwaki H, Tian Y-S, Kawashita N, Takagi T (2018) Mordred: a molecular descriptor calculator. J Chem 10:4

    Article  CAS  Google Scholar 

  55. Clark AM, Labute P, Santavy M (2006) 2D structure depiction. J Chem Inf Model 46:1107–1123

    Article  CAS  PubMed  Google Scholar 

  56. Ebalunode JO, Zheng W (2009) Unconventional 2D shape similarity method affords comparable enrichment as a 3D shape method in virtual screening experiments. J Chem Inf Model 49:1313–1320

    Article  CAS  PubMed  Google Scholar 

  57. Babel O (2010) The open source chemistry toolbox

    Google Scholar 

  58. OEChem T (2012) OpenEye Scientific Software. Inc, Santa Fe, NM, USA

    Google Scholar 

  59. Duvenaud DK, Maclaurin D, Iparraguirre J, et al (2015) Convolutional networks on graphs for learning molecular fingerprints. In: Advances in neural information processing systems. The Neural Information Processing Systems Foundation. pp. 2224–2232

    Google Scholar 

  60. Zhou J, Cui G, Zhang Z, et al (2018) Graph neural networks: a review of methods and applications. arXiv preprint arXiv:181208434

    Google Scholar 

  61. Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. preprint arXiv:1609.02907

    Google Scholar 

  62. Elton DC, Boukouvalas Z, Fuge MD, Chung PW (2019) Deep learning for molecular design—a review of the state of the art. Mol Syst Des Eng 4:828–849

    Article  CAS  Google Scholar 

  63. Tripathi A, Bankaitis VA (2017) Molecular docking: From lock and key to combination lock. J Mol Med Clin Appl 2

    Google Scholar 

  64. Deng J, Dong W, Socher R, et al (2009) Imagenet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. IEEE, pp 248–255

    Google Scholar 

  65. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

    Google Scholar 

  66. Nam H, Ha J-W, Kim J (2017) Dual attention networks for multimodal reasoning and matching. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 299–307

    Google Scholar 

  67. Sundararajan M, Taly A, Yan Q (2017) Axiomatic attribution for deep networks. arXiv preprint arXiv:170301365

    Google Scholar 

  68. Raghuraman A, Mosier PD, Desai UR (2006) Finding a needle in a haystack: development of a combinatorial virtual screening approach for identifying high specificity heparin/heparan sulfate sequence (s). J Med Chem 49:3553–3562

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Da C, Stashko M, Jayakody C et al (2015) Discovery of Mer kinase inhibitors by virtual screening using structural protein–ligand interaction fingerprints. Bioorg Med Chem 23:1096–1101

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Cheong R, Wang CJ, Levchenko A (2009) High content cell screening in a microfluidic device. Mol Cell Proteomics 8:433–442

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Feinberg EN, Sur D, Wu Z et al (2018) PotentialNet for molecular property prediction. ACS Centr Sci 4:1520–1530

    Article  CAS  Google Scholar 

  72. Irwin JJ, Shoichet BK, Mysinger MM et al (2009) Automated docking screens: a feasibility study. J Med Chem 52:5712–5720

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Malo N, Hanley JA, Cerquozzi S et al (2006) Statistical practice in high-throughput screening data analysis. Nat Biotechnol 24:167–175

    Article  CAS  PubMed  Google Scholar 

  74. Clyde A, Duan X, Stevens R (2020) Regression enrichment surfaces: a simple analysis technique for virtual drug screening models. arXiv preprint arXiv:200601171

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, University of Chicago, Chicago, IL, USA

    Austin Clyde

  2. Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA

    Austin Clyde

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  1. Austin Clyde
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Correspondence to Austin Clyde .

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Editors and Affiliations

  1. Computational Drug Discovery, Evotec (UK) Ltd., Abingdon, Oxfordshire, UK

    Alexander Heifetz

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Clyde, A. (2022). Ultrahigh Throughput Protein–Ligand Docking with Deep Learning. In: Heifetz, A. (eds) Artificial Intelligence in Drug Design. Methods in Molecular Biology, vol 2390. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1787-8_13

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  • DOI: https://doi.org/10.1007/978-1-0716-1787-8_13

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1786-1

  • Online ISBN: 978-1-0716-1787-8

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