Skip to main content
SpringerLink
Log in

Artificial Intelligence in Drug Safety and Metabolism

  • Protocol
  • First Online:
Artificial Intelligence in Drug Design

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

Abstract

The use of artificial intelligence methods in drug safety began in the early 2000s with applications such as predicting bacterial mutagenicity and hERG inhibition. The field has been endlessly expanding ever since and the models have become more complex. These approaches are now integrated into molecule risk assessment processes along with in vitro and in vivo methods. Today, artificial intelligence can be used in every phase of drug discovery and development, from profiling chemical libraries in early discovery, to predicting off-target effects in the mid-discovery phase, to assessing potential mutagenic impurities in development and degradants as part of life cycle management. This chapter provides an overview of artificial intelligence in drug safety and describes its application throughout the entire discovery and development process.

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

Access this chapter

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

Institutional subscriptions

References

  1. DiMasi JA, Grabowski HG, Hansen RW (2016) Innovation in the pharmaceutical industry: new estimates of R&D costs. J Health Econ 47:20–33. https://doi.org/10.1016/j.jhealeco.2016.01.012

    Article  PubMed  Google Scholar 

  2. Kola I, Landis J (2004) Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov 3(8):711–715. https://doi.org/10.1038/nrd1470

    Article  CAS  PubMed  Google Scholar 

  3. Hornberg JJ, Mow T (2014) How can we discover safer drugs? Future Med Chem 6(5):481–483. https://doi.org/10.4155/fmc.14.15

    Article  CAS  PubMed  Google Scholar 

  4. Cook D, Brown D, Alexander R, March R, Morgan P, Satterthwaite G, Pangalos MN (2014) Lessons learned from the fate of AstraZeneca's drug pipeline: a five-dimensional framework. Nat Rev Drug Discov 13(6):419–431. https://doi.org/10.1038/nrd4309

    Article  CAS  PubMed  Google Scholar 

  5. Morgan P, Brown DG, Lennard S, Anderton MJ, Barrett JC, Eriksson U, Fidock M, Hamren B, Johnson A, March RE, Matcham J, Mettetal J, Nicholls DJ, Platz S, Rees S, Snowden MA, Pangalos MN (2018) Impact of a five-dimensional framework on R&D productivity at AstraZeneca. Nat Rev Drug Discov 17(3):167–181. https://doi.org/10.1038/nrd.2017.244

    Article  CAS  PubMed  Google Scholar 

  6. Kim H, Kim E, Lee I, Bae B, Park M, Nam H (2020) Artificial intelligence in drug discovery: a comprehensive review of data-driven and machine learning approaches. Biotechnol Bioprocess Eng 25(6):895–930. https://doi.org/10.1007/s12257-020-0049-y

    Article  CAS  PubMed  Google Scholar 

  7. Herrmann K, Jayne K (2019) Animal experimentation: working towards a paradigm change. In: Human-Animal Studies. Leiden; Boston: Brill. https://doi.org/10.1163/9789004391192

  8. Hansch C (2002) Quantitative approach to biochemical structure-activity relationships. Acc Chem Res 2(8):232–239. https://doi.org/10.1021/ar50020a002

    Article  Google Scholar 

  9. Hansch C, Fujita T (1964) P-σ-π analysis. A method for the correlation of biological activity and chemical structure. J Am Chem Soc 86(8):1616–1626. https://doi.org/10.1021/ja01062a035

    Article  CAS  Google Scholar 

  10. Hansch C, Maloney PP, Fujita T, Muir RM (1962) Correlation of biological activity of Phenoxyacetic acids with Hammett substituent constants and partition coefficients. Nature 194(4824):178–180. https://doi.org/10.1038/194178b0

    Article  CAS  Google Scholar 

  11. Free SM Jr, Wilson JW (1964) A mathematical contribution to structure-activity studies. J Med Chem 7(4):395–399. https://doi.org/10.1021/jm00334a001

    Article  CAS  PubMed  Google Scholar 

  12. David L, Thakkar A, Mercado R, Engkvist O (2020) Molecular representations in AI-driven drug discovery: a review and practical guide. J Cheminform 12(1):56. https://doi.org/10.1186/s13321-020-00460-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. OECD. http://www.oecd.org/

  14. ICH. https://www.ich.org/

  15. ICH-M3 (2009) Guidance on non clinical safety studies for the conduct of human clinical trials and marketing authorisation for pharmaceuticals M3(R2). https://database.ich.org/sites/default/files/M3_R2__Guideline.pdf

  16. Nicolotti O (2018) Computational toxicology. In: Methods in Molecular Biology. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7899-1

    Chapter  Google Scholar 

  17. Berro J (2018) "essentially, all models are wrong, but some are useful"-a cross-disciplinary agenda for building useful models in cell biology and biophysics. Biophys Rev 10(6):1637–1647. https://doi.org/10.1007/s12551-018-0478-4

    Article  PubMed  PubMed Central  Google Scholar 

  18. OECD (2004) OECD principles for the validation, for regulatory purposes, of (quantitative) structure activity relationship models. Organisation for economic co-operation and development. https://www.oecd.org/chemicalsafety/risk-assessment/37849783.pdf

  19. 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 

  20. Moksony F (1999) Small is beautiful: the use and interpretation of R2 in social research. Szociologiai Szemle:130–138

    Google Scholar 

  21. Alexander DL, Tropsha A, Winkler DA (2015) Beware of R(2): 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 

  22. Scalia G, Grambow CA, Pernici B, Li YP, Green WH (2020) Evaluating scalable uncertainty estimation methods for deep learning-based molecular property prediction. J Chem Inf Model 60(6):2697–2717. https://doi.org/10.1021/acs.jcim.9b00975

    Article  CAS  PubMed  Google Scholar 

  23. Lazic SE, Williams DP (2020) Improving drug safety predictions by reducing poor analytical practices. Toxicol Res Appl 4:2397847320978633. https://doi.org/10.1177/2397847320978633

    Article  Google Scholar 

  24. Hanser T, Barber C, Marchaland JF, Werner S (2016) Applicability domain: towards a more formal definition. SAR QSAR Environ Res 27(11):893–909. https://doi.org/10.1080/1062936X.2016.1250229

    Article  CAS  PubMed  Google Scholar 

  25. Johnson MAM, G. M. (1990) Concepts and applications of molecular similarity. Wiley, Hoboken, New Jersey

    Google Scholar 

  26. MOE Chemical Computing Group. https://www.chemcomp.com/

  27. Schrodinger Schrodinger Inc. https://www.schrodinger.com/

  28. Studio D Biovia—Dassault Systemes. https://www.3ds.com/products-services/biovia/products/molecular-modeling-simulation/biovia-discovery-studio/

  29. Todeschini R, Consonni V, Xiang H, Holliday J, Buscema M, Willett P (2012) Similarity coefficients for binary chemoinformatics data: overview and extended comparison using simulated and real data sets. J Chem Inf Model 52(11):2884–2901. https://doi.org/10.1021/ci300261r

    Article  CAS  PubMed  Google Scholar 

  30. Hunter P (2008) A toxic brew we cannot live without. Micronutrients give insights into the interplay between geochemistry and evolutionary biology. EMBO Rep 9(1):15–18. https://doi.org/10.1038/sj.embor.7401148

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Pang KS, Han YR, Noh K, Lee PI, Rowland M (2019) Hepatic clearance concepts and misconceptions: why the well-stirred model is still used even though it is not physiologic reality? Biochem Pharmacol 169:113596. https://doi.org/10.1016/j.bcp.2019.07.025

    Article  CAS  PubMed  Google Scholar 

  32. Sager JE, Yu J, Ragueneau-Majlessi I, Isoherranen N (2015) Physiologically based pharmacokinetic (PBPK) modeling and simulation approaches: a systematic review of published models, applications, and model verification. Drug Metab Dispos 43(11):1823–1837. https://doi.org/10.1124/dmd.115.065920

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Jones HM, Dickins M, Youdim K, Gosset JR, Attkins NJ, Hay TL, Gurrell IK, Logan YR, Bungay PJ, Jones BC, Gardner IB (2012) Application of PBPK modelling in drug discovery and development at Pfizer. Xenobiotica 42(1):94–106. https://doi.org/10.3109/00498254.2011.627477

    Article  CAS  PubMed  Google Scholar 

  34. Davies M, RDO J, Grime K, Jansson-Lofmark R, Fretland AJ, Winiwarter S, Morgan P, McGinnity DF (2020) Improving the accuracy of predicted human pharmacokinetics: lessons learned from the AstraZeneca drug pipeline over two decades. Trends Pharmacol Sci 41(6):390–408. https://doi.org/10.1016/j.tips.2020.03.004

    Article  CAS  PubMed  Google Scholar 

  35. Laverty H, Benson C, Cartwright E, Cross M, Garland C, Hammond T, Holloway C, McMahon N, Milligan J, Park B, Pirmohamed M, Pollard C, Radford J, Roome N, Sager P, Singh S, Suter T, Suter W, Trafford A, Volders P, Wallis R, Weaver R, York M, Valentin J (2011) How can we improve our understanding of cardiovascular safety liabilities to develop safer medicines? Br J Pharmacol 163(4):675–693. https://doi.org/10.1111/j.1476-5381.2011.01255.x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Ekins S (2014) Progress in computational toxicology. J Pharmacol Toxicol Methods 69(2):115–140. https://doi.org/10.1016/j.vascn.2013.12.003

    Article  CAS  PubMed  Google Scholar 

  37. Zhou Z, Gong Q, Epstein ML, January CT (1998) HERG channel dysfunction in human long QT syndrome. Intracellular transport and functional defects. J Biol Chem 273(33):21061–21066. https://doi.org/10.1074/jbc.273.33.21061

    Article  CAS  PubMed  Google Scholar 

  38. Recanatini M, Poluzzi E, Masetti M, Cavalli A, De Ponti F (2005) QT prolongation through hERG K(+) channel blockade: current knowledge and strategies for the early prediction during drug development. Med Res Rev 25(2):133–166. https://doi.org/10.1002/med.20019

    Article  CAS  PubMed  Google Scholar 

  39. Viskin S (1999) Long QT syndromes and torsade de pointes. Lancet 354(9190):1625–1633. https://doi.org/10.1016/S0140-6736(99)02107-8

    Article  CAS  PubMed  Google Scholar 

  40. Cai C, Guo P, Zhou Y, Zhou J, Wang Q, Zhang F, Fang J, Cheng F (2019) Deep learning-based prediction of drug-induced cardiotoxicity. J Chem Inf Model 59(3):1073–1084. https://doi.org/10.1021/acs.jcim.8b00769

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Zhang Y, Zhao J, Wang Y, Fan Y, Zhu L, Yang Y, Chen X, Lu T, Chen Y, Liu H (2019) Prediction of hERG K+ channel blockage using deep neural networks. Chem Biol Drug Des 94(5):1973–1985. https://doi.org/10.1111/cbdd.13600

    Article  CAS  PubMed  Google Scholar 

  42. Fermini B, Hancox JC, Abi-Gerges N, Bridgland-Taylor M, Chaudhary KW, Colatsky T, Correll K, Crumb W, Damiano B, Erdemli G, Gintant G, Imredy J, Koerner J, Kramer J, Levesque P, Li Z, Lindqvist A, Obejero-Paz CA, Rampe D, Sawada K, Strauss DG, Vandenberg JI (2016) A new perspective in the field of cardiac safety testing through the comprehensive in vitro Proarrhythmia assay paradigm. J Biomol Screen 21(1):1–11. https://doi.org/10.1177/1087057115594589

    Article  CAS  PubMed  Google Scholar 

  43. Huang H, Pugsley MK, Fermini B, Curtis MJ, Koerner J, Accardi M, Authier S (2017) Cardiac voltage-gated ion channels in safety pharmacology: review of the landscape leading to the CiPA initiative. J Pharmacol Toxicol Methods 87:11–23. https://doi.org/10.1016/j.vascn.2017.04.002

    Article  CAS  PubMed  Google Scholar 

  44. Park J-S, Jeon J-Y, Yang J-H, Kim M-G (2019) Introduction to in silico model for proarrhythmic risk assessment under the CiPA initiative. Transl Clin Pharmacol 27(1):12–18. https://doi.org/10.12793/tcp.2019.27.1.12

    Article  PubMed  PubMed Central  Google Scholar 

  45. Mamoshina P, Bueno-Orovio A, Rodriguez B (2020) Dual transcriptomic and molecular machine learning predicts all major clinical forms of drug cardiotoxicity. Front Pharmacol 11:639. https://doi.org/10.3389/fphar.2020.00639

    Article  PubMed  PubMed Central  Google Scholar 

  46. Haq KT, Howell SJ, Tereshchenko LG (2020) Applying artificial intelligence to ECG analysis: promise of a better future. Circ Arrhythm Electrophysiol 13(8):e009111. https://doi.org/10.1161/CIRCEP.120.009111

    Article  PubMed  Google Scholar 

  47. Adedinsewo D, Carter RE, Attia Z, Johnson P, Kashou AH, Dugan JL, Albus M, Sheele JM, Bellolio F, Friedman PA, Lopez-Jimenez F, Noseworthy PA (2020) Artificial intelligence-enabled ECG algorithm to identify patients with left ventricular systolic dysfunction presenting to the emergency department with dyspnea. Circ Arrhythm Electrophysiol 13(8):e008437. https://doi.org/10.1161/CIRCEP.120.008437

    Article  CAS  PubMed  Google Scholar 

  48. Hannun AY, Rajpurkar P, Haghpanahi M, Tison GH, Bourn C, Turakhia MP, Ng AY (2019) Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat Med 25(1):65–69. https://doi.org/10.1038/s41591-018-0268-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Attia ZI, Kapa S, Lopez-Jimenez F, McKie PM, Ladewig DJ, Satam G, Pellikka PA, Enriquez-Sarano M, Noseworthy PA, Munger TM, Asirvatham SJ, Scott CG, Carter RE, Friedman PA (2019) Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nat Med 25(1):70–74. https://doi.org/10.1038/s41591-018-0240-2

    Article  CAS  PubMed  Google Scholar 

  50. Olson H, Betton G, Robinson D, Thomas K, Monro A, Kolaja G, Lilly P, Sanders J, Sipes G, Bracken W, Dorato M, Van Deun K, Smith P, Berger B, Heller A (2000) Concordance of the toxicity of pharmaceuticals in humans and in animals. Regul Toxicol Pharmacol 32(1):56–67. https://doi.org/10.1006/rtph.2000.1399

    Article  CAS  PubMed  Google Scholar 

  51. Williams DP, Lazic SE, Foster AJ, Semenova E, Morgan P (2020) Predicting drug-induced liver injury with Bayesian machine learning. Chem Res Toxicol 33(1):239–248. https://doi.org/10.1021/acs.chemrestox.9b00264

    Article  CAS  PubMed  Google Scholar 

  52. Semenova E, Williams DP, Afzal AM, Lazic SE (2020) A Bayesian neural network for toxicity prediction. Comput Toxicol 16:100133. https://doi.org/10.1016/j.comtox.2020.100133

    Article  Google Scholar 

  53. Minerali E, Foil DH, Zorn KM, Lane TR, Ekins S (2020) Comparing machine learning algorithms for predicting drug-induced liver injury (DILI). Mol Pharm 17(7):2628–2637. https://doi.org/10.1021/acs.molpharmaceut.0c00326

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Hammann F, Schoning V, Drewe J (2019) Prediction of clinically relevant drug-induced liver injury from structure using machine learning. J Appl Toxicol 39(3):412–419. https://doi.org/10.1002/jat.3741

    Article  CAS  PubMed  Google Scholar 

  55. Ashby J, Tennant RW (1988) Chemical structure, salmonella mutagenicity and extent of carcinogenicity as indicators of genotoxic carcinogenesis among 222 chemicals tested in rodents by the U.S. NCI/NTP. Mutat Res 204(1):17–115. https://doi.org/10.1016/0165-1218(88)90114-0

    Article  CAS  PubMed  Google Scholar 

  56. Stepan AF, Walker DP, Bauman J, Price DA, Baillie TA, Kalgutkar AS, Aleo MD (2011) Structural alert/reactive metabolite concept as applied in medicinal chemistry to mitigate the risk of idiosyncratic drug toxicity: a perspective based on the critical examination of trends in the top 200 drugs marketed in the United States. Chem Res Toxicol 24(9):1345–1410. https://doi.org/10.1021/tx200168d

    Article  CAS  PubMed  Google Scholar 

  57. Smith GF (2011) Designing drugs to avoid toxicity. Prog Med Chem 50:1–47. https://doi.org/10.1016/B978-0-12-381290-2.00001-X

    Article  CAS  PubMed  Google Scholar 

  58. Ahlberg E, Amberg A, Beilke LD, Bower D, Cross KP, Custer L, Ford KA, Van Gompel J, Harvey J, Honma M, Jolly R, Joossens E, Kemper RA, Kenyon M, Kruhlak N, Kuhnke L, Leavitt P, Naven R, Neilan C, Quigley DP, Shuey D, Spirkl HP, Stavitskaya L, Teasdale A, White A, Wichard J, Zwickl C, Myatt GJ (2016) Extending (Q)SARs to incorporate proprietary knowledge for regulatory purposes: a case study using aromatic amine mutagenicity. Regul Toxicol Pharmacol 77:1–12. https://doi.org/10.1016/j.yrtph.2016.02.003

    Article  CAS  PubMed  Google Scholar 

  59. Kalgutkar A, Dalvie D, Obach R, Smith D (2012) Pathways of reactive metabolite formation with Toxicophores/-structural alerts. In: Reactive drug metabolites. Methods and principles in medicinal chemistry. Wiley, Hoboken, New Jersey, pp 93–129. https://doi.org/10.1002/9783527655748.ch5

    Chapter  Google Scholar 

  60. Kalgutkar AS, Dalvie D (2015) Predicting toxicities of reactive metabolite-positive drug candidates. Annu Rev Pharmacol Toxicol 55(1):35–54. https://doi.org/10.1146/annurev-pharmtox-010814-124720

    Article  CAS  PubMed  Google Scholar 

  61. Kazius J, McGuire R, Bursi R (2005) Derivation and validation of toxicophores for mutagenicity prediction. J Med Chem 48(1):312–320. https://doi.org/10.1021/jm040835a

    Article  CAS  PubMed  Google Scholar 

  62. ICH-M7 (2017) Assessment and control of DNA reactive (mutagenic) impurities in pharmaceuticals to limit potential carcinogenic risk. ICH. https://database.ich.org/sites/default/files/M7_R1_Guideline.pdf

  63. Lowe Jr E, Butkiewicz M, White Z,Spellings M, Omlor A, Meiler J (2012) Comparative analysis of machine learning techniques for the prediction of the DMPK parameters intrinsic clearance and plasma protein binding. In: 2011 IEEE symposium on computational intelligence in bioinformatics and computational biology (CIBCB), IEEE, Paris

    Google Scholar 

  64. Wang Y, Liu H, Fan Y, Chen X, Yang Y, Zhu L, Zhao J, Chen Y, Zhang Y (2019) In silico prediction of human intravenous pharmacokinetic parameters with improved accuracy. J Chem Inf Model 59(9):3968–3980. https://doi.org/10.1021/acs.jcim.9b00300

    Article  CAS  PubMed  Google Scholar 

  65. Lombardo F, Berellini G, Obach RS (2018) Trend analysis of a database of intravenous pharmacokinetic parameters in humans for 1352 drug compounds. Drug Metab Dispos 46(11):1466–1477. https://doi.org/10.1124/dmd.118.082966

    Article  CAS  PubMed  Google Scholar 

  66. Schneckener S, Grimbs S, Hey J, Menz S, Osmers M, Schaper S, Hillisch A, Goller AH (2019) Prediction of Oral bioavailability in rats: transferring insights from in vitro correlations to (deep) machine learning models using in silico model outputs and chemical structure parameters. J Chem Inf Model 59(11):4893–4905. https://doi.org/10.1021/acs.jcim.9b00460

    Article  CAS  PubMed  Google Scholar 

  67. Feinberg EN, Joshi E, Pande VS, Cheng AC (2020) Improvement in ADMET prediction with multitask deep Featurization. J Med Chem 63(16):8835–8848. https://doi.org/10.1021/acs.jmedchem.9b02187

    Article  CAS  PubMed  Google Scholar 

  68. Ye Z, Yang Y, Li X, Cao D, Ouyang D (2019) An integrated transfer learning and multitask learning approach for pharmacokinetic parameter prediction. Mol Pharm 16(2):533–541. https://doi.org/10.1021/acs.molpharmaceut.8b00816

    Article  CAS  PubMed  Google Scholar 

  69. Kosugi Y, Hosea N (2020) Direct comparison of Total clearance prediction: computational machine learning model versus bottom-up approach using in vitro assay. Mol Pharm 17(7):2299–2309. https://doi.org/10.1021/acs.molpharmaceut.9b01294

    Article  CAS  PubMed  Google Scholar 

  70. Bera K, Schalper KA, Rimm DL, Velcheti V, Madabhushi A (2019) Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology. Nat Rev Clin Oncol 16(11):703–715. https://doi.org/10.1038/s41571-019-0252-y

    Article  PubMed  PubMed Central  Google Scholar 

  71. Bhargava R, Madabhushi A (2016) Emerging themes in image informatics and molecular analysis for digital pathology. Annu Rev Biomed Eng 18(1):387–412. https://doi.org/10.1146/annurev-bioeng-112415-114722

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Steiner DF, MacDonald R, Liu Y, Truszkowski P, Hipp JD, Gammage C, Thng F, Peng L, Stumpe MC (2018) Impact of deep learning assistance on the histopathologic review of lymph nodes for metastatic breast cancer. Am J Surg Pathol 42(12):1636–1646. https://doi.org/10.1097/PAS.0000000000001151

    Article  PubMed  PubMed Central  Google Scholar 

  73. Barisoni L, Lafata KJ, Hewitt SM, Madabhushi A, Balis UGJ (2020) Digital pathology and computational image analysis in nephropathology. Nat Rev Nephrol 16(11):669–685. https://doi.org/10.1038/s41581-020-0321-6

    Article  PubMed  Google Scholar 

  74. Tokarz DA, Steinbach TJ, Lokhande A, Srivastava G, Ugalmugle R, Co CA, Shockley KR, Singletary E, Cesta MF, Thomas HC, Chen VS, Hobbie K, Crabbs TA (2020) Using artificial intelligence to detect, classify, and objectively score severity of rodent cardiomyopathy. Toxicol Pathol 49(4):888–896. https://doi.org/10.1177/0192623320972614

    Article  PubMed  PubMed Central  Google Scholar 

  75. Race AM, Sutton D, Hamm G, Maglennon G, Morton JP, Strittmatter N, Campbell A, Sansom OJ, Wang Y, Barry ST, Takats Z, Goodwin RJA, Bunch J (2021) Deep learning-based annotation transfer between molecular imaging modalities: an automated workflow for multimodal data integration. Anal Chem 93(6):3061–3071. https://doi.org/10.1021/acs.analchem.0c02726

    Article  CAS  PubMed  Google Scholar 

  76. MELLODDY machine learning ledger orchestration for drug discovery. https://www.melloddy.eu/

  77. Schwartz SM, Wildenhaus K, Bucher A, Byrd B (2020) Digital twins and the emerging science of self: implications for digital health experience design and “small” data. Front Comput Sci 2(31). https://doi.org/10.3389/fcomp.2020.00031

Download references

Acknowledgments

I would like to thank Caroline, Aidan, and James for the time we spent together during the pandemic, for keeping me entertained and for their love. I would like to thank Mr. Anton Martinsson and Dr. Filip Miljković for critically reading the manuscript and for useful discussions on the subject.

Author information

Authors and Affiliations

  1. Imaging & Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Cambridge, UK

    Graham F. Smith

Authors
  1. Graham F. Smith
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Graham F. Smith .

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

Smith, G.F. (2022). Artificial Intelligence in Drug Safety and Metabolism. 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_22

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-1787-8_22

  • 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