Skip to main content
SpringerLink
Log in
Book cover

Artificial Intelligence in Drug Design pp 349–382Cite as

Artificial Intelligence in Compound Design

  • Protocol
  • First Online:

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

Abstract

Artificial intelligence has seen an incredibly fast development in recent years. Many novel technologies for property prediction of drug molecules as well as for the design of novel molecules were introduced by different research groups. These artificial intelligence-based design methods can be applied for suggesting novel chemical motifs in lead generation or scaffold hopping as well as for optimization of desired property profiles during lead optimization. In lead generation, broad sampling of the chemical space for identification of novel motifs is required, while in the lead optimization phase, a detailed exploration of the chemical neighborhood of a current lead series is advantageous. These different requirements for successful design outcomes render different combinations of artificial intelligence technologies useful. Overall, we observe that a combination of different approaches with tailored scoring and evaluation schemes appears beneficial for efficient artificial intelligence-based compound design.

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

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
Softcover Book
USD   179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   249.99
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

Learn about institutional subscriptions

Springer Nature is developing a new tool to find and evaluate Protocols. Learn more

References

  1. Paul SM, Mytelka DS, Dunwiddie CT, Persinger CC, Munos BH, Lindborg SR, Schacht AL (2010) How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nat Rev Drug Discov 9(3):203–214. https://doi.org/10.1038/nrd3078

    Article  CAS  PubMed  Google Scholar 

  2. Green CP, Engkvist O, Pairaudeau G (2018) The convergence of artificial intelligence and chemistry for improved drug discovery. Future Med Chem 10(22):2573–2576. https://doi.org/10.4155/fmc-2018-0161

    Article  CAS  PubMed  Google Scholar 

  3. Hoffmann T, Gastreich M (2019) The next level in chemical space navigation: going far beyond enumerable compound libraries. Drug Discov Today 24(5):1148–1156. https://doi.org/10.1016/j.drudis.2019.02.013

    Article  CAS  PubMed  Google Scholar 

  4. Walters WP (2019) Virtual chemical libraries. J Med Chem 62(3):1116–1124. https://doi.org/10.1021/acs.jmedchem.8b01048

    Article  CAS  PubMed  Google Scholar 

  5. van Hilten N, Chevillard F, Kolb P (2019) Virtual compound libraries in computer-assisted drug discovery. J Chem Inf Model 59(2):644–651. https://doi.org/10.1021/acs.jcim.8b00737

    Article  CAS  PubMed  Google Scholar 

  6. Böhm H-J (1992) LUDI: rule-based automatic design of new substituents for enzyme inhibitor leads. J Comput Aided Mol Des 6(6):593–606. https://doi.org/10.1007/bf00126217

    Article  PubMed  Google Scholar 

  7. Gillet V, Johnson AP, Mata P, Sike S, Williams P (1993) SPROUT: a program for structure generation. J Comput Aided Mol Des 7(2):127–153. https://doi.org/10.1007/bf00126441

    Article  CAS  PubMed  Google Scholar 

  8. Stahl M, Todorov NP, James T, Mauser H, Boehm H-J, Dean PM (2002) A validation study on the practical use of automated de novo design. J Comput Aided Mol Des 16(7):459–478. https://doi.org/10.1023/a:1021242018286

    Article  CAS  PubMed  Google Scholar 

  9. Dean PM, Firth-Clark S, Harris W, Kirton SB, Todorov NP (2006) SkelGen: a general tool for structure-based de novo ligand design. Expert Opin Drug Discov 1(2):179–189. https://doi.org/10.1517/17460441.1.2.179

    Article  CAS  PubMed  Google Scholar 

  10. Schneider G, Fechner U (2005) Computer-based de novo design of drug-like molecules. Nat Rev Drug Discov 4(8):649–663. https://doi.org/10.1038/nrd1799

    Article  CAS  PubMed  Google Scholar 

  11. Hartenfeller M, Schneider G (2011) De novo drug design. In: Bajorath J (ed) Chemoinformatics and computational chemical biology. Humana Press, Totowa, NJ, pp 299–323. https://doi.org/10.1007/978-1-60761-839-3_12

    Chapter  Google Scholar 

  12. Mauser H, Guba W (2008) Recent developments in de novo design and scaffold hopping. Curr Opin Drug Discovery Dev 11:365–374

    CAS  Google Scholar 

  13. Todorov NP, Alberts I, Dean PM (2006) De novo design. In: Taylor JB, Triggle DJ (eds) Comprehensive medicinal chemistry II, vol 4. Elsevier, pp 283–305. https://doi.org/10.1016/B0-08-045044-X/00255-8

    Chapter  Google Scholar 

  14. Schneider G, Clark DE (2019) Automated de novo drug design: are we nearly there yet? Angew Chem Int Ed 58(32):10792–10803. https://doi.org/10.1002/anie.201814681

    Article  CAS  Google Scholar 

  15. Schneider P, Schneider G (2016) De novo design at the edge of chaos. J Med Chem 59(9):4077–4086. https://doi.org/10.1021/acs.jmedchem.5b01849

    Article  CAS  PubMed  Google Scholar 

  16. Hartenfeller M, Zettl H, Walter M, Rupp M, Reisen F, Proschak E, Weggen S, Stark H, Schneider G (2012) DOGS: reaction-driven de novo design of bioactive compounds. PLoS Comput Biol 8(2):e1002380. https://doi.org/10.1371/journal.pcbi.1002380

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Zhavoronkov A, Ivanenkov YA, Aliper A, Veselov MS, Aladinskiy VA, Aladinskaya AV, Terentiev VA, Polykovskiy DA, Kuznetsov MD, Asadulaev A, Volkov Y, Zholus A, Shayakhmetov RR, Zhebrak A, Minaeva LI, Zagribelnyy BA, Lee LH, Soll R, Madge D, Xing L, Guo T, Aspuru-Guzik A (2019) Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat Biotechnol 37(9):1038–1040. https://doi.org/10.1038/s41587-019-0224-x

    Article  CAS  PubMed  Google Scholar 

  18. Grebner C, Matter H, Plowright AT, Hessler G (2020) Automated de novo design in medicinal chemistry: which types of chemistry does a generative neural network learn? J Med Chem 63(16):8809–8823. https://doi.org/10.1021/acs.jmedchem.9b02044

    Article  CAS  PubMed  Google Scholar 

  19. Segler MHS, Kogej T, Tyrchan C, Waller MP (2018) Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS Cent Sci 4(1):120–131. https://doi.org/10.1021/acscentsci.7b00512

    Article  CAS  PubMed  Google Scholar 

  20. Chen H, Engkvist O, Wang Y, Olivecrona M, Blaschke T (2018) The rise of deep learning in drug discovery. Drug Discov Today 23(6):1241–1250. https://doi.org/10.1016/j.drudis.2018.01.039

    Article  PubMed  Google Scholar 

  21. Olivecrona M, Blaschke T, Engkvist O, Chen H (2017) Molecular de-novo design through deep reinforcement learning. J Cheminform 9(1):48. https://doi.org/10.1186/s13321-017-0235-x

    Article  PubMed  PubMed Central  Google Scholar 

  22. Skalic M, Jiménez J, Sabbadin D, De Fabritiis G (2019) Shape-based generative modeling for de novo drug design. J Chem Inf Model 59(3):1205–1214. https://doi.org/10.1021/acs.jcim.8b00706

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  Google Scholar 

  24. Arús-Pous J, Blaschke T, Ulander S, Reymond J-L, Chen H, Engkvist O (2019) Exploring the GDB-13 chemical space using deep generative models. J Cheminf 11(1):20. https://doi.org/10.1186/s13321-019-0341-z

    Article  Google Scholar 

  25. Baell JB, Holloway GA (2010) New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J Med Chem 53:2719–2740

    Article  CAS  PubMed  Google Scholar 

  26. Rishton GM (1997) Reactive compounds and in vitro false positives in HTS. Drug Discov Today 2:382–384

    Article  CAS  Google Scholar 

  27. Hann M, Hudson B, Lewell X, Lifely R, Miller L, Ramsden N (1999) Strategic pooling of compounds for high-throughput screening. J Chem Inf Comput Sci 39(5):897–902. https://doi.org/10.1021/ci990423o

    Article  CAS  PubMed  Google Scholar 

  28. Mignani S, Rodrigues J, Tomas H, Jalal R, Singh PP, Majoral J-P, Vishwakarma RA (2018) Present drug-likeness filters in medicinal chemistry during the hit and lead optimization process: how far can they be simplified? Drug Discov Today 23(3):605–615. https://doi.org/10.1016/j.drudis.2018.01.010

    Article  PubMed  Google Scholar 

  29. Walters WP, Murcko MA (2002) Prediction of ‘drug-likeness’. Adv Drug Deliv Rev 54(3):255–271. https://doi.org/10.1016/S0169-409X(02)00003-0

    Article  CAS  PubMed  Google Scholar 

  30. Kalliokoski T, Salo HS, Lahtela-Kakkonen M, Poso A (2009) The effect of ligand-based tautomer and protomer prediction on structure-based virtual screening. J Chem Inf Model 49(12):2742–2748. https://doi.org/10.1021/ci900364w

    Article  CAS  PubMed  Google Scholar 

  31. Knox AJS, Meegan MJ, Carta G, Lloyd DG (2005) Considerations in compound database preparation“hidden” impact on virtual screening results. J Chem Inf Model 45(6):1908–1919. https://doi.org/10.1021/ci050185z

    Article  CAS  PubMed  Google Scholar 

  32. Scior T, Bender A, Tresadern G, Medina-Franco JL, Martínez-Mayorga K, Langer T, Cuanalo-Contreras K, Agrafiotis DK (2012) Recognizing pitfalls in virtual screening: a critical review. J Chem Inf Model 52(4):867–881. https://doi.org/10.1021/ci200528d

    Article  CAS  PubMed  Google Scholar 

  33. Sastry GM, Adzhigirey M, Day T, Annabhimoju R, Sherman W (2013) Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. J Comput Aided Mol Des 27:221–234

    Article  PubMed  Google Scholar 

  34. Grisoni F, Moret M, Lingwood R, Schneider G (2020) Bidirectional molecule generation with recurrent neural networks. J Chem Inf Model 60(3):1175–1183. https://doi.org/10.1021/acs.jcim.9b00943

    Article  CAS  PubMed  Google Scholar 

  35. Johansson S, Ptykhodko O, Arús-Pous J, Engkvist O, Chen H (2019) Comparison between SMILES-based differential neural computer and recurrent neural network architectures for de novo molecule design. ChemRxiv. https://doi.org/10.26434/chemrxiv.9758600

  36. Arús-Pous J, Patronov A, Bjerrum EJ, Tyrchan C, Reymond J-L, Chen H, Engkvist O (2020) SMILES-based deep generative scaffold decorator for de-novo drug design. J Cheminf 12(1):38. https://doi.org/10.1186/s13321-020-00441-8

    Article  CAS  Google Scholar 

  37. Langevin M, Minoux H, Levesque M, Bianciotto M (2021) Scaffold-constrained molecular generation. J Chem Inf Model. https://doi.org/10.1021/acs.jcim.0c01015

  38. Gómez-Bombarelli R, Wei JN, Duvenaud D, Hernández-Lobato JM, Sánchez-Lengeling B, Sheberla D, Aguilera-Iparraguirre J, Hirzel TD, Adams RP, Aspuru-Guzik A (2018) Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent Sci 4(2):268–276. https://doi.org/10.1021/acscentsci.7b00572

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Bjerrum EJ, Sattarov B (2018) Improving chemical autoencoder latent space and molecular de novo generation diversity with heteroencoders. Biomolecules 8(4):131. https://doi.org/10.3390/biom8040131

    Article  CAS  PubMed Central  Google Scholar 

  40. Lim J, Ryu S, Kim JW, Kim WY (2018) Molecular generative model based on conditional variational autoencoder for de novo molecular design. J Cheminf 10(1):31. https://doi.org/10.1186/s13321-018-0286-7

    Article  CAS  Google Scholar 

  41. Blaschke T, Olivecrona M, Engkvist O, Bajorath J, Chen H (2018) Application of generative autoencoder in de novo molecular design. Mol Inf 37(1–2):1700123. https://doi.org/10.1002/minf.201700123

    Article  CAS  Google Scholar 

  42. Jin W, Barzilay R, Jaakkola T (2019) Junction tree variational autoencoder for molecular graph generation. arXiv:180204364

    Google Scholar 

  43. ChEMBL 24. https://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/releases/chembl_24/. Accessed 15 Jul 2021

  44. Enamine REAL drug like subspace. https://enamine.net/compound-collections/real-compounds/real-compound-libraries. Accessed 15 Jul 2021

  45. Grebner C, Malmerberg E, Shewmaker A, Batista J, Nicholls A, Sadowski J (2020) Virtual screening in the cloud: how big is big enough? J Chem Inf Model 60(9):4274–4282. https://doi.org/10.1021/acs.jcim.9b00779

    Article  CAS  PubMed  Google Scholar 

  46. Ståhl N, Falkman G, Karlsson A, Mathiason G, Boström J (2019) Deep reinforcement learning for multiparameter optimization in de novo drug design. J Chem Inf Model 59(7):3166–3176. https://doi.org/10.1021/acs.jcim.9b00325

    Article  CAS  PubMed  Google Scholar 

  47. Duvenaud D, Maclaurin D, Aguilera-Iparraguirre J, Gómez-Bombarelli R, Hirzel T, Aspuru-Guzik A, Adams RP (2015) Convolutional networks on graphs for learning molecular fingerprints. In: Proceedings of the 28th international conference on neural information processing systems (NIPS’15), vol 2. MIT Press, Cambridge, pp 2224–2232

    Google Scholar 

  48. Kearnes S, McCloskey K, Pande V, Berndl M, Riley P (2018) Molecular graph convolutions: moving beyond fingerprints. ArXiv. https://arxiv.org/abs/1603.00856

  49. Li Y, Vinyals O, Dyer C, Pascanu R, Battaglia P (2018) Learning deep generative models of graphs. ArXiv. https://arxiv.org/abs/1803.03324

  50. Li Y, Zhang L, Liu Z (2018) Multi-objective de novo drug design with conditional graph generative model. J Cheminf 10(1):33. https://doi.org/10.1186/s13321-018-0287-6

    Article  CAS  Google Scholar 

  51. Jensen JH (2019) A graph-based genetic algorithm and generative model/Monte Carlo tree search for the exploration of chemical space. Chem Sci 10(12):3567–3572. https://doi.org/10.1039/C8SC05372C

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Mercado R, Rastemo T, Lindelöf E, Klambauer G, Engkvist O, Chen H, Bjerrum E (2020) Graph networks for molecular design. ChemRxiv. https://doi.org/10.26434/chemrxiv.12843137

  53. Grant JA, Gallardo MA, Pickup BT (1996) A fast method of molecular shape comparison: a simple application of a Gaussian description of molecular shape. J Comput Chem 17(14):1653–1666. https://doi.org/10.1002/(SICI)1096-987X(19961115)17:14<1653::AID-JCC7>3.0.CO;2-K

    Article  CAS  Google Scholar 

  54. Grant JA, Pickup BT (1995) A Gaussian description of molecular shape. J Phys Chem 99(11):3503–3510. https://doi.org/10.1021/j100011a016

    Article  CAS  Google Scholar 

  55. Jiménez, J., Skalic, M., Martinez-Rosell, G., & De Fabritiis, G. (2018). K DEEP: Protein–Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks. Journal of chemical information and modeling, 58(2):287–296

    Google Scholar 

  56. Skalic M, Varela-Rial A, Jiménez J, Martínez-Rosell G, De Fabritiis G (2018) LigVoxel: inpainting binding pockets using 3D-convolutional neural networks. Bioinformatics 35(2):243–250. https://doi.org/10.1093/bioinformatics/bty583

    Article  CAS  Google Scholar 

  57. RDKit: open-source cheminformatics. http://www.rdkit.org. Accessed 15 Jul 2021

  58. Deng J, Dong W, Socher R, Li L, Kai L, Li F-F ImageNet: a large-scale hierarchical image database. In: IEEE conference on computer vision and pattern recognition, 20–25 June 2009, pp 248–255. https://doi.org/10.1109/CVPR.2009.5206848

  59. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv: 14091556

    Google Scholar 

  60. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 770–778. https://doi.org/10.1109/CVPR.2016.90

  61. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2015) Rethinking the inception architecture for computer vision. ArXiv. https://arxiv.org/abs/1512.00567

  62. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2014) Going deeper with convolutions. ArXiv. https://arxiv.org/abs/1409.4842

  63. Amabilino S, Pogány P, Pickett SD, Green DVS (2020) Guidelines for recurrent neural network transfer learning-based molecular generation of focused libraries. J Chem Inf Model 60(12):5699–5713. https://doi.org/10.1021/acs.jcim.0c00343

    Article  CAS  PubMed  Google Scholar 

  64. Merk D, Friedrich L, Grisoni F, Schneider G (2018) De novo design of bioactive small molecules by artificial intelligence. Mol Inf 37:1700153

    Article  Google Scholar 

  65. Li X, Fourches D (2020) Inductive transfer learning for molecular activity prediction: Next-Gen QSAR models with MolPMoFiT. J Cheminf 12(1):27. https://doi.org/10.1186/s13321-020-00430-x

    Article  CAS  Google Scholar 

  66. Pesciullesi G, Schwaller P, Laino T, Reymond J-L (2020) Transfer learning enables the molecular transformer to predict regio- and stereoselective reactions on carbohydrates. Nat Commun 11(1):4874. https://doi.org/10.1038/s41467-020-18671-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Neil D, Segler MH, Guasch L, Ahmed M, Plumbley D, Sellwood M, Brown N (2018) Exploring deep recurrent models with reinforcement learning for molecule design. Openreview. https://openreview.net/forum?id=HkcTe-bR-

  68. Popova M, Isayev O, Tropsha A (2018) Deep reinforcement learning for de novo drug design. Sci Adv 4(7):eaap7885. https://doi.org/10.1126/sciadv.aap7885

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Liu X, Ye K, van Vlijmen HWT, IJzerman AP, van Westen GJP (2019) An exploration strategy improves the diversity of de novo 2 ligands using deep reinforcement learning—a case for the 3 adenosine A2A receptor. J Cheminf 11(1):35. https://doi.org/10.1186/s13321-019-0355-6

    Article  CAS  Google Scholar 

  70. Blaschke T, Arús-Pous J, Chen H, Margreitter C, Tyrchan C, Engkvist O, Papadopoulos K, Patronov A (2020) REINVENT 2.0: an AI tool for de novo drug design. J Chem Inf Model 60(12):5918–5922. https://doi.org/10.1021/acs.jcim.0c00915

    Article  CAS  PubMed  Google Scholar 

  71. Segler MHS, Preuss M, Waller MP (2018) Planning chemical syntheses with deep neural networks and symbolic AI. Nature 555(7698):604–610. https://doi.org/10.1038/nature25978

    Article  CAS  PubMed  Google Scholar 

  72. Brown N, Fiscato M, Segler MH, Vaucher AC (2019) GuacaMol: benchmarking models for de novo molecular design. J Chem Inf Model 59(3):1096–1108

    Article  CAS  PubMed  Google Scholar 

  73. Petrov I, Gao D, Chervoniy N, Liu K, Marangonda S, Umé C, Jiang J, Rp L, Zhang S, Wu P, Zhang W (2020) DeepFaceLab: a simple, flexible and extensible face swapping framework. ArXiv. https://arxiv.org/abs/2005.05535

  74. Guimaraes GL, Sanchez-Lengeling B, Outeiral C, Farias PLC, Aspuru-Guzik A (2018) Objective-reinforced generative adversarial networks (ORGAN) for sequence generation models. arXiv:170510843

    Google Scholar 

  75. Sanchez-Lengeling B, Outeiral C, Guimaraes GL, Aspuru-Guzik A (2017) Optimizing distributions over molecular space. An objective-reinforced generative adversarial network for inverse-design chemistry (ORGANIC) ChemRxiv. https://doi.org/10.26434/chemrxiv.5309668.v3

  76. Maziarka Ł, Pocha A, Kaczmarczyk J, Rataj K, Danel T, Warchoł M (2020) Mol-CycleGAN: a generative model for molecular optimization. J Cheminf 12:2. https://doi.org/10.1186/s13321-019-0404-1

    Article  CAS  Google Scholar 

  77. Zhu J, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: 2017 IEEE international conference on computer vision (ICCV), 22–29 Oct. 2017, pp 2242–2251. https://doi.org/10.1109/ICCV.2017.244

  78. Winter R, Montanari F, Steffen A, Briem H, Noé F, Clevert D-A (2019) Efficient multi-objective molecular optimization in a continuous latent space. Chem Sci 10(34):8016–8024. https://doi.org/10.1039/C9SC01928F

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Besnard J, Ruda GF, Setola V, Abecassis K, Rodriguiz RM, Huang X-P, Norval S, Sassano MF, Shin AI, Webster LA, Simeons FRC, Stojanovski L, Prat A, Seidah NG, Constam DB, Bickerton GR, Read KD, Wetsel WC, Gilbert IH, Roth BL, Hopkins AL (2012) Automated design of ligands to polypharmacological profiles. Nature 492(7428):215–220. https://doi.org/10.1038/nature11691

    Article  CAS  PubMed  Google Scholar 

  80. 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(1):3–25. https://doi.org/10.1016/S0169-409X(96)00423-1

    Article  CAS  Google Scholar 

  81. Maggiora GM, Johnson MA (1990) Concepts and applications of molecular similarity. Wiley, New York, pp 99–117

    Google Scholar 

  82. UNITY Chemical Information Software (2018) Certara, St. Louis, MO

    Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  84. Matter H (1997) Selecting optimally diverse compounds from structure databases: a validation study of two-dimensional and three-dimensional molecular descriptors. J Med Chem 40(8):1219–1229. https://doi.org/10.1021/jm960352+

    Article  CAS  PubMed  Google Scholar 

  85. Willett P, Winterman V (1986) A comparison of some measures for the determination of inter-molecular structural similarity measures of inter-molecular structural similarity. Quant Struct Act Relat 5(1):18–25. https://doi.org/10.1002/qsar.19860050105

    Article  CAS  Google Scholar 

  86. Schneider G, Neidhart W, Giller T, Schmid G (1999) “Scaffold-Hopping” by topological pharmacophore search: a contribution to virtual screening. Angew Chem, Int Ed 38:2894–2896

    Article  CAS  Google Scholar 

  87. Nettles JH, Jenkins JL, Bender A, Deng Z, Davies JW, Glick M (2006) Bridging chemical and biological space: “target fishing” using 2D and 3D molecular descriptors. J Med Chem 49(23):6802–6810. https://doi.org/10.1021/jm060902w

    Article  CAS  PubMed  Google Scholar 

  88. Kubinyi H (1998) Similarity and dissimilarity—a medicinal chemists view. Perspect Drug Discovery Des 11:225–252

    Article  Google Scholar 

  89. Bajorath J, Peltason L, Wawer M, Guha R, Lajiness MS, Van Drie JH (2009) Navigating structure–activity landscapes. Drug Discov Today 14(13):698–705. https://doi.org/10.1016/j.drudis.2009.04.003

    Article  CAS  PubMed  Google Scholar 

  90. Maggiora GM (2006) On outliers and activity cliffs: why QSAR often disappoints. J Chem Inf Model 46(4):1535–1535. https://doi.org/10.1021/ci060117s

    Article  CAS  PubMed  Google Scholar 

  91. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444. https://doi.org/10.1038/nature14539

    Article  CAS  PubMed  Google Scholar 

  92. Baringhaus K-H, Hessler G, Matter H, Schmidt F (2014) Development and applications of global ADMET models: in silico prediction of human microsomal lability. In: Bajorath J (ed) Chemoinformatics for drug discovery. Wiley, New York, pp 245–265

    Google Scholar 

  93. Wenzel J, Matter H, Schmidt F (2019) Predictive multitask deep neural network models for ADME-Tox properties: learning from large data sets. J Chem Inf Model 59(3):1253–1268. https://doi.org/10.1021/acs.jcim.8b00785

    Article  CAS  PubMed  Google Scholar 

  94. Ma J, Sheridan RP, Liaw A, Dahl GE, Svetnik V (2015) Deep neural nets as a method for quantitative structure-activity relationships. J Chem Inf Model 55:263–274

    Article  CAS  PubMed  Google Scholar 

  95. Ramsundar B, Liu B, Wu Z, Verras A, Tudor M, Sheridan RP, Pande V (2017) Is multitask deep learning practical for pharma? J Chem Inf Model 57:2068–2076

    Article  CAS  PubMed  Google Scholar 

  96. Xu Y, Ma J, Liaw A, Sheridan RP, Svetnik V (2017) Demystifying multitask deep neural networks for quantitative structure–activity relationships. J Chem Inf Model 57:2490–2504

    Article  CAS  PubMed  Google Scholar 

  97. Korotcov A, Tkachenko V, Russo DP, Ekins S (2017) Comparison of deep learning with multiple machine learning methods and metrics using diverse drug discovery data sets. Mol Pharm 14:4462–4475

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Li X, Xu Y, Lai L, Pei J (2018) Prediction of human cytochrome P450 inhibition using a multitask deep autoencoder neural network. Mol Pharm 15:4336–4345

    Article  CAS  PubMed  Google Scholar 

  99. Mayr A, Klambauer G, Unterthiner T, Hochreiter S (2016) DeepTox: toxicity prediction using deep learning. Front Environ Sci 3(80):81–15

    Google Scholar 

  100. Xu Y, Dai Z, Chen F, Gao S, Pei J, Lai L (2015) Deep learning for drug-induced liver injury. J Chem Inf Model 55:2085–2093

    Article  CAS  PubMed  Google Scholar 

  101. Schmidt F, Wenzel J, Halland N, Güssregen S, Delafoy L, Czich A (2019) Computational investigation of drug phototoxicity: photosafety assessment, photo-toxophore identification, and machine learning. Chem Res Toxicol 32(11):2338–2352. https://doi.org/10.1021/acs.chemrestox.9b00338

    Article  CAS  PubMed  Google Scholar 

  102. Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M, Dearden J, Gramatica P, Martin YC, Todeschini R, Consonni V, Kuz’min VE, Cramer R, Benigni R, Yang C, Rathman J, Terfloth L, Gasteiger J, Richard A, Tropsha A (2014) QSAR modeling: where have you been? where are you going to? J Med Chem 57(12):4977–5010. https://doi.org/10.1021/jm4004285

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Alexander DLJ, Tropsha A, Winkler DA (2015) Beware of R2: simple, unambiguous assessment of the prediction accuracy of QSAR and QSPR models. J Chem Inf Model 55(7):1316–1322. https://doi.org/10.1021/acs.jcim.5b00206

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Weaver S, Gleeson MP (2008) The importance of the domain of applicability in QSAR modeling. J Mol Graph Model 26(8):1315–1326. https://doi.org/10.1016/j.jmgm.2008.01.002

    Article  CAS  PubMed  Google Scholar 

  105. Dragos H, Gilles M, Alexandre V (2009) Predicting the predictability: a unified approach to the applicability domain problem of QSAR models. J Chem Inf Model 49(7):1762–1776. https://doi.org/10.1021/ci9000579

    Article  CAS  PubMed  Google Scholar 

  106. Obrezanova O, Csányi G, Gola JMR, Segall MD (2007) Gaussian processes: a method for automatic QSAR modeling of ADME properties. J Chem Inf Model 47(5):1847–1857. https://doi.org/10.1021/ci7000633

    Article  CAS  PubMed  Google Scholar 

  107. Schwaighofer A, Schroeter T, Mika S, Laub J, ter Laak A, Sülzle D, Ganzer U, Heinrich N, Müller K-R (2007) Accurate solubility prediction with error bars for electrolytes: a machine learning approach. J Chem Inf Model 47(2):407–424. https://doi.org/10.1021/ci600205g

    Article  CAS  PubMed  Google Scholar 

  108. Schroeter TS, Schwaighofer A, Mika S, Ter Laak A, Suelzle D, Ganzer U, Heinrich N, Müller K-R (2007) Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules. J Comput Aided Mol Des 21(9):485–498. https://doi.org/10.1007/s10822-007-9125-z

    Article  CAS  PubMed  Google Scholar 

  109. Hirschfeld L, Swanson K, Yang K, Barzilay R, Coley CW (2020) Uncertainty quantification using neural networks for molecular property prediction. J Chem Inf Model 60(8):3770–3780. https://doi.org/10.1021/acs.jcim.0c00502

    Article  CAS  PubMed  Google Scholar 

  110. Cortés-Ciriano I, Bender A (2019) Deep confidence: a computationally efficient framework for calculating reliable prediction errors for deep neural networks. J Chem Inf Model 59(3):1269–1281. https://doi.org/10.1021/acs.jcim.8b00542

    Article  CAS  PubMed  Google Scholar 

  111. Cortés-Ciriano I, Bender A (2019) Reliable prediction errors for deep neural networks using test-time dropout. J Chem Inf Model 59(7):3330–3339. https://doi.org/10.1021/acs.jcim.9b00297

    Article  CAS  PubMed  Google Scholar 

  112. Gramatica P (2007) Principles of QSAR models validation: internal and external. QSAR Comb Sci 26(5):694–701. https://doi.org/10.1002/qsar.200610151

    Article  CAS  Google Scholar 

  113. Sheridan RP, Feuston BP, Maiorov VN, Kearsley SK (2004) Similarity to molecules in the training set is a good discriminator for prediction accuracy in QSAR. J Chem Inf Comput Sci 44(6):1912–1928. https://doi.org/10.1021/ci049782w

    Article  CAS  PubMed  Google Scholar 

  114. Segall MD (2012) Multi-parameter optimization: identifying high quality compounds with a balance of properties. Curr Pharm Des 18(9):1292–1310. https://doi.org/10.2174/138161212799436430

    Article  CAS  PubMed  Google Scholar 

  115. Segall MD, Beresford AP, Gola JMR, Hawksley D, Tarbit MH (2006) Focus on success: using a probabilistic approach to achieve an optimal balance of compound properties in drug discovery. Expert Opin Drug Metab 2(2):325–337. https://doi.org/10.1517/17425255.2.2.325

    Article  CAS  Google Scholar 

  116. Schneider G (2018) Generative models for artificially-intelligent molecular design. Mol Inf 37:1880131

    Article  Google Scholar 

  117. Segall M, Champness E, Leeding C, Lilien R, Mettu R, Stevens B (2011) Applying medicinal chemistry transformations and multiparameter optimization to guide the search for high-quality leads and candidates. J Chem Inf Model 51:2967–2976

    Article  CAS  PubMed  Google Scholar 

  118. Kingma DP, Ba J (2017) Adam: a method for stochastic optimization. arXiv:14126980v9

    Google Scholar 

  119. Gentile F, Agrawal V, Hsing M, Ton A-T, Ban F, Norinder U, Gleave ME, Cherkasov A (2020) Deep docking: a deep learning platform for augmentation of structure based drug discovery. ACS Cent Sci 6(6):939–949. https://doi.org/10.1021/acscentsci.0c00229

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Sheridan RP, Zorn N, Sherer EC, Campeau L-C, Chang C, Cumming J, Maddess ML, Nantermet PG, Sinz CJ, O’Shea PD (2014) Modeling a crowdsourced definition of molecular complexity. J Chem Inf Model 54(6):1604–1616. https://doi.org/10.1021/ci5001778

    Article  CAS  PubMed  Google Scholar 

  121. Méndez-Lucio O, Medina-Franco JL (2017) The many roles of molecular complexity in drug discovery. Drug Discov Today 22(1):120–126. https://doi.org/10.1016/j.drudis.2016.08.009

    Article  CAS  PubMed  Google Scholar 

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

    Article  Google Scholar 

  123. PubChem: open chemistry database at the National Institutes of Health (NIH). https://pubchem.ncbi.nlm.nih.gov/

  124. Voršilák M, Kolář M, Čmelo I, Svozil D (2020) SYBA: Bayesian estimation of synthetic accessibility of organic compounds. J Cheminf 12(1):35. https://doi.org/10.1186/s13321-020-00439-2

    Article  Google Scholar 

  125. Szymkuć S, Gajewska EP, Klucznik T, Molga K, Dittwald P, Startek M, Bajczyk M, Grzybowski BA (2016) Computer-assisted synthetic planning: the end of the beginning. Angew Chem Int Ed 55(20):5904

    Article  Google Scholar 

  126. Engkvist O, Norrby P-O, Selmi N, Lam Y-H, Peng Z, Sherer EC, Amberg W, Erhard T, Smyth LA (2018) Computational prediction of chemical reactions: current status and outlook. Drug Discov Today 23(6):1203–1218. https://doi.org/10.1016/j.drudis.2018.02.014

    Article  CAS  PubMed  Google Scholar 

  127. Coley CW, Green WH, Jensen KF (2018) Machine learning in computer-aided synthesis planning. Acc Chem Res 51:1281–1289

    Article  CAS  PubMed  Google Scholar 

  128. Coley CW, Barzilay R, Jaakkola TS, Green WH, Jensen KF (2017) Prediction of organic reaction outcomes using machine learning. ACS Cent Sci 3(5):434–443

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. Strieth-Kalthoff F, Sandfort F, Segler MHS, GloriusF (2020) Machine learning the ropes: principles, applications and directions in synthetic chemistry. Chem Soc Rev 49(17):6154–6168.

    Google Scholar 

  130. Coley CW, Rogers L, Green WH, Jensen KF (2018) SCScore: synthetic complexity learned from a reaction corpus. J Chem Inf Model 58:252–261

    Article  CAS  PubMed  Google Scholar 

  131. Thakkar A, Chadimova V, Bjerrum EJ, Engkvist O, Reymond J-L (2020) Retrosynthetic accessibility score (RAscore)—rapid machine learned synthesizability classification from AI driven retrosynthetic planning. ChemRxiv:1–21. https://doi.org/10.26434/chemrxiv.13019993.v1

  132. Genheden S, Thakkar A, Chadimová V, Reymond J-L, Engkvist O, Bjerrum E (2020) AiZynthFinder: a fast, robust and flexible open-source software for retrosynthetic planning. J Cheminf 12(1):70. https://doi.org/10.1186/s13321-020-00472-1

    Article  Google Scholar 

  133. Quinlan JR (1992) Learning with continuous classes. In: Adams A, Sterling L (eds) Proc. AI’92, 5th Australian joint conference on artificial intelligence. World Scientific, Singapore, pp 343–348

    Google Scholar 

  134. Quinlan JR (1991) Improved estimates for the accuracy of small disjuncts. Mach Learn 6(1):93–98. https://doi.org/10.1007/BF00153762

    Article  Google Scholar 

  135. Wu Z, Ramsundar B, Feinberg EN, Gomes J, Geniesse C, Pappu AS, Leswing K, Pande V (2018) MoleculeNet: a benchmark for molecular machine learning. Chem Sci 9:513–530

    Article  CAS  PubMed  Google Scholar 

  136. Ashton M, Barnard J, Casset F, Charlton M, Downs G, Gorse D, Holliday J, Lahana R, Willett P (2002) Identification of diverse database subsets using property-based and fragment-based molecular descriptions. Quant Struct Act Relat 21(6):598–604. https://doi.org/10.1002/qsar.200290002

    Article  CAS  Google Scholar 

  137. Sheridan RP (2013) Time-split cross-validation as a method for estimating the goodness of prospective prediction. J Chem Inf Model 53:783–790

    Article  CAS  PubMed  Google Scholar 

  138. Struble TJ, Alvarez JC, Brown SP, Chytil M, Cisar J, DesJarlais RL, Engkvist O, Frank SA, Greve DR, Griffin DJ, Hou X, Johannes JW, Kreatsoulas C, Lahue B, Mathea M, Mogk G, Nicolaou CA, Palmer AD, Price DJ, Robinson RI, Salentin S, Xing L, Jaakkola T, Green WH, Barzilay R, Coley CW, Jensen KF (2020) Current and future roles of artificial intelligence in medicinal chemistry synthesis. J Med Chem 63(16):8667–8682. https://doi.org/10.1021/acs.jmedchem.9b02120

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  139. Chodera JD, Mobley DL, Shirts MR, Dixon RW, Branson K, Pande VS (2011) Alchemical free energy methods for drug discovery: progress and challenges. Curr Opin Struct Biol 21:150–160

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  140. Wang L, Wu Y, Deng Y, Kim B, Pierce L, Krilov G, Lupyan D, Robinson S, Dahlgren MK, Greenwood J, Romero DL, Masse C, Knight JL, Steinbrecher T, Beuming T, Damm W, Harder E, Sherman W, Brewer M, Wester R, Murcko M, Frye L, Farid R, Lin T, Mobley DL, Jorgensen WL, Berne BJ, Friesner RA, Abel R (2015) Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field. J Am Chem Soc 137:2695–2703

    Article  CAS  PubMed  Google Scholar 

  141. Cappel D, Jerome S, Hessler G, Matter H (2020) Impact of different automated binding pose generation approaches on relative binding free energy simulations. J Chem Inf Model 60(3):1432–1444. https://doi.org/10.1021/acs.jcim.9b01118

    Article  CAS  PubMed  Google Scholar 

  142. Schindler CEM, Baumann H, Blum A, Böse D, Buchstaller H-P, Burgdorf L, Cappel D, Chekler E, Czodrowski P, Dorsch D, Eguida MKI, Follows B, Fuchß T, Grädler U, Gunera J, Johnson T, Jorand Lebrun C, Karra S, Klein M, Knehans T, Koetzner L, Krier M, Leiendecker M, Leuthner B, Li L, Mochalkin I, Musil D, Neagu C, Rippmann F, Schiemann K, Schulz R, Steinbrecher T, Tanzer E-M, Unzue Lopez A, Viacava Follis A, Wegener A, Kuhn D (2020) Large-scale assessment of binding free energy calculations in active drug discovery projects. J Chem Inf Model 60(11):5457–5474. https://doi.org/10.1021/acs.jcim.0c00900

    Article  CAS  PubMed  Google Scholar 

  143. Jiménez-Luna J, Grisoni F, Schneider G (2020) Drug discovery with explainable artificial intelligence. Nat Mach Intell 2(10):573–584. https://doi.org/10.1038/s42256-020-00236-4

    Article  Google Scholar 

  144. Walters WP, Murcko M (2020) Assessing the impact of generative AI on medicinal chemistry. Nat Biotechnol 38(2):143–145. https://doi.org/10.1038/s41587-020-0418-2

    Article  CAS  PubMed  Google Scholar 

  145. Zhavoronkov A (2020) Medicinal chemists versus machines challenge: what will it take to adopt and advance artificial intelligence for drug discovery? J Chem Inf Model 60(6):2657–2659. https://doi.org/10.1021/acs.jcim.0c00435

    Article  CAS  PubMed  Google Scholar 

  146. Bush JT, Pogany P, Pickett SD, Barker M, Baxter A, Campos S, Cooper AWJ, Hirst D, Inglis G, Nadin A, Patel VK, Poole D, Pritchard J, Washio Y, White G, Green DVS (2020) A turing test for molecular generators. J Med Chem 63(20):11964–11971. https://doi.org/10.1021/acs.jmedchem.0c01148

    Article  CAS  PubMed  Google Scholar 

  147. Muratov EN, Bajorath J, Sheridan RP, Tetko IV, Filimonov D, Poroikov V, Oprea TI, Baskin II, Varnek A, Roitberg A, Isayev O, Curtalolo S, Fourches D, Cohen Y, Aspuru-Guzik A, Winkler DA, Agrafiotis D, Cherkasov A, Tropsha A (2020) QSAR without borders. Chem Soc Rev 49(11):3525–3564. https://doi.org/10.1039/D0CS00098A

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Sanofi-Aventis Deutschland GmbH, R&D, Integrated Drug Discovery, Frankfurt am Main, Germany

    Christoph Grebner, Hans Matter & Gerhard Hessler

Authors
  1. Christoph Grebner
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hans Matter
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gerhard Hessler
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gerhard Hessler .

Editor information

Editors and Affiliations

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

    Alexander Heifetz

Rights and permissions

Reprints and permissions

Copyright information

© 2022 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

Grebner, C., Matter, H., Hessler, G. (2022). Artificial Intelligence in Compound Design. 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_15

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-1787-8_15

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1786-1

  • Online ISBN: 978-1-0716-1787-8

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation