Skip to main content

In Silico Insights Toward the Exploration of Adenosine Receptors Ligand Recognition

  • Chapter
  • First Online:
Purinergic Receptors and their Modulators

Part of the book series: Topics in Medicinal Chemistry ((TMC,volume 41))

  • 105 Accesses

Abstract

Adenosine receptors have been studied for many years now, their structures have been resolved and many of the determinants for their interaction patterns have been highlighted. Nevertheless, they still represent extremely fascinating but challenging biological targets, and much effort has been put by both academic and industrial drug discovery groups into finding novel and proficient molecular candidates for the regulations of these purinergic species. Computational methods have been vastly implemented for the research of adenosine receptors modulators, allowing to speed up and optimize the development of potent and selective compounds. In this paper, we describe the main computer-based methods that have been used and that are still exploited in this field, reporting also many of the cases in which their application brought to light novel and promising molecular species with great therapeutic potential. While the research on adenosine receptors is still very open, the evolution and amelioration of computational approaches is expected to fill the gaps that we still miss about these beautiful targets, in a way that we can better communicate with them, finally providing valid and efficacious therapeutic solutions through their regulation.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 299.99
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. Martin L, Hutchens M, Hawkins C (2017) Clinical trial cycle times continue to increase despite industry efforts. Nat Rev Drug Discov 16(3):157–157. https://doi.org/10.1038/nrd.2017.21

    Article  CAS  Google Scholar 

  2. Simoens S, Huys I (2021) R&D costs of new medicines: a landscape analysis. Front Med 8. https://doi.org/10.3389/fmed.2021.760762

  3. FDA. The drug development process. https://www.fda.gov/patients/learn-about-drug-and-device-approvals/drug-development-process

  4. Hertzberg RP, Pope AJ (2000) High-throughput screening: new technology for the 21st century. Curr Opin Chem Biol 4(4):445–451. https://doi.org/10.1016/S1367-5931(00)00110-1

    Article  CAS  Google Scholar 

  5. Keserű GM, Makara GM (2006) Hit discovery and hit-to-lead approaches. Drug Discov Today 11(15–16):741–748. https://doi.org/10.1016/j.drudis.2006.06.016

    Article  Google Scholar 

  6. Deprez-Poulain R, Deprez B (2004) Facts, figures and trends in lead generation. Curr Top Med Chem 4(6):569–580. https://doi.org/10.2174/1568026043451168

    Article  CAS  Google Scholar 

  7. Hevener KE, Pesavento R, Ren J, Lee H, Ratia K, Johnson ME (2018) Hit-to-lead: hit validation and assessment. Methods Enzymol:265–309. https://doi.org/10.1016/bs.mie.2018.09.022

  8. Bleicher KH, Böhm H-J, Müller K, Alanine AI (2003) Hit and lead generation: beyond high-throughput screening. Nat Rev Drug Discov 2(5):369–378. https://doi.org/10.1038/nrd1086

    Article  CAS  Google Scholar 

  9. Hughes J, Rees S, Kalindjian S, Philpott K (2011) Principles of early drug discovery. Br J Pharmacol 162(6):1239–1249. https://doi.org/10.1111/j.1476-5381.2010.01127.x

    Article  CAS  PubMed Central  Google Scholar 

  10. Hefti FF (2008) Requirements for a lead compound to become a clinical candidate. BMC Neurosci 9(S3):S7. https://doi.org/10.1186/1471-2202-9-S3-S7

    Article  CAS  PubMed Central  Google Scholar 

  11. Keserü GM, Makara GM (2009) The influence of lead discovery strategies on the properties of drug candidates. Nat Rev Drug Discov 8(3):203–212. https://doi.org/10.1038/nrd2796

    Article  CAS  Google Scholar 

  12. (2012) The truly staggering cost of inventing new drugs. [Online]. Available: https://www.forbes.com/sites/matthewherper/2012/02/10/the-truly-staggering-cost-of-inventing-new-drugs/?sh=4f5fd0104a94

  13. Congressional Budget Office (2021) Research and development in the pharmaceutical industry. [Online]. Available: https://www.cbo.gov/publication/57126

  14. FDA. Development & approval process | drugs. [Online]. Available: https://www.fda.gov/drugs/development-approval-process-drugs

  15. EMA. Authorisation of medicines. [Online]. Available: https://www.ema.europa.eu/en/about-us/what-we-do/authorisation-medicines

  16. Assenberg R, Wan PT, Geisse S, Mayr LM (2013) Advances in recombinant protein expression for use in pharmaceutical research. Curr Opin Struct Biol 23(3):393–402. https://doi.org/10.1016/j.sbi.2013.03.008

    Article  CAS  Google Scholar 

  17. D’Atri V, Fekete S, Clarke A, Veuthey J-L, Guillarme D (2019) Recent advances in chromatography for pharmaceutical analysis. Anal Chem 91(1):210–239. https://doi.org/10.1021/acs.analchem.8b05026

    Article  CAS  Google Scholar 

  18. Denora N, Trapani A, Laquintana V, Lopedota A, Trapani G (2009) Recent advances in medicinal chemistry and pharmaceutical technology-strategies for drug delivery to the brain. Curr Top Med Chem 9(2):182–196. https://doi.org/10.2174/156802609787521571

    Article  CAS  Google Scholar 

  19. Porta R, Benaglia M, Puglisi A (2016) Flow chemistry: recent developments in the synthesis of pharmaceutical products. Org Process Res Dev 20(1):2–25. https://doi.org/10.1021/acs.oprd.5b00325

    Article  CAS  Google Scholar 

  20. Hann MM, Oprea TI (2004) Pursuing the leadlikeness concept in pharmaceutical research. Curr Opin Chem Biol 8(3):255–263. https://doi.org/10.1016/j.cbpa.2004.04.003

    Article  CAS  Google Scholar 

  21. Bohacek RS, McMartin C, Guida WC (1996) The art and practice of structure-based drug design: a molecular modeling perspective. Med Res Rev 16(1):3–50. https://doi.org/10.1002/(SICI)1098-1128(199601)16:1<3::AID-MED1>3.0.CO;2-6

    Article  CAS  Google Scholar 

  22. Tripathi NM, Bandyopadhyay A (2022) High throughput virtual screening (HTVS) of peptide library: technological advancement in ligand discovery. Eur J Med Chem 243:114766. https://doi.org/10.1016/j.ejmech.2022.114766

    Article  CAS  Google Scholar 

  23. Pirhadi S, Sunseri J, Koes DR (2016) Open source molecular modeling. J Mol Graph Model 69:127–143. https://doi.org/10.1016/j.jmgm.2016.07.008

    Article  CAS  PubMed Central  Google Scholar 

  24. Leelananda SP, Lindert S (2016) Computational methods in drug discovery. Beilstein J Org Chem 12:2694–2718. https://doi.org/10.3762/bjoc.12.267

    Article  CAS  PubMed Central  Google Scholar 

  25. Pavan M, Bassani D, Sturlese M, Moro S (2022) From the Wuhan-Hu-1 strain to the XD and XE variants: is targeting the SARS-CoV-2 spike protein still a pharmaceutically relevant option against COVID-19? J Enzyme Inhib Med Chem 37(1):1704–1714. https://doi.org/10.1080/14756366.2022.2081847

    Article  CAS  PubMed Central  Google Scholar 

  26. Vyas V, Ukawala R, Chintha C, Ghate M (2012) Homology modeling a fast tool for drug discovery: current perspectives. Indian J Pharm Sci 74(1):1. https://doi.org/10.4103/0250-474X.102537

    Article  CAS  PubMed Central  Google Scholar 

  27. Cavasotto CN, Phatak SS (2009) Homology modeling in drug discovery: current trends and applications. Drug Discov Today 14(13–14). https://doi.org/10.1016/j.drudis.2009.04.006

  28. Fourches D, Muratov E, Tropsha A (2010) Trust, but verify: on the importance of chemical structure curation in cheminformatics and QSAR modeling research. J Chem Inf Model 50(7):1189–1204. https://doi.org/10.1021/ci100176x

    Article  CAS  PubMed Central  Google Scholar 

  29. Berman HM (2000) The protein data bank. Nucleic Acids Res 28(1):235–242. https://doi.org/10.1093/nar/28.1.235

    Article  CAS  PubMed Central  Google Scholar 

  30. PDB statistics: overall growth of released structures per year. [Online]. Available: https://www.rcsb.org/stats/growth/growth-released-structures

  31. Sarker IH (2021) Machine learning: algorithms, real-world applications and research directions. SN Comput Sci 2(3):160. https://doi.org/10.1007/s42979-021-00592-x

    Article  PubMed Central  Google Scholar 

  32. Mouchlis VD et al (2021) Advances in de novo drug design: from conventional to machine learning methods. Int J Mol Sci 22(4):1676. https://doi.org/10.3390/ijms22041676

    Article  PubMed Central  Google Scholar 

  33. Sabe VT et al (2021) Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: a review. Eur J Med Chem 224:113705. https://doi.org/10.1016/j.ejmech.2021.113705

    Article  CAS  Google Scholar 

  34. Pecoraro C et al (2023) 1,2,4-Amino-triazine derivatives as pyruvate dehydrogenase kinase inhibitors: synthesis and pharmacological evaluation. Eur J Med Chem 249:115134. https://doi.org/10.1016/j.ejmech.2023.115134

    Article  CAS  Google Scholar 

  35. Pavan M, Bassani D, Sturlese M, Moro S (2022) Bat coronaviruses related to SARS-CoV-2: what about their 3CL proteases (MPro)? J Enzyme Inhib Med Chem 37(1):1077–1082. https://doi.org/10.1080/14756366.2022.2062336

    Article  CAS  PubMed Central  Google Scholar 

  36. Anderson AC (2003) The process of structure-based drug design. Chem Biol 10(9):787–797. https://doi.org/10.1016/j.chembiol.2003.09.002

    Article  CAS  Google Scholar 

  37. Baker D, Sali A (2001) Protein structure prediction and structural genomics. Science 294(5540):93–96. https://doi.org/10.1126/science.1065659

    Article  CAS  Google Scholar 

  38. Webb B, Sali A (2016) Comparative protein structure modeling using modeller. Curr Protoc Bioinformatics 54(1). https://doi.org/10.1002/cpbi.3

  39. Waterhouse A et al (2018) SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res 46(W1):W296–W303. https://doi.org/10.1093/nar/gky427

    Article  CAS  PubMed Central  Google Scholar 

  40. Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJE (2015) The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc 10(6):845–858. https://doi.org/10.1038/nprot.2015.053

    Article  CAS  PubMed Central  Google Scholar 

  41. Song Y et al (2013) High-resolution comparative modeling with RosettaCM. Structure 21(10):1735–1742. https://doi.org/10.1016/j.str.2013.08.005

    Article  CAS  Google Scholar 

  42. Chemical Computing Group ULC (2023) Molecular operating environment (MOE) 2022.02

    Google Scholar 

  43. Jumper J et al (2021) Highly accurate protein structure prediction with AlphaFold. Nature 596(7873):583–589. https://doi.org/10.1038/s41586-021-03819-2

    Article  CAS  PubMed Central  Google Scholar 

  44. Protein Structure Prediction Center. https://predictioncenter.org/

  45. David A, Islam S, Tankhilevich E, Sternberg MJE (2022) The AlphaFold database of protein structures: a biologist’s guide. J Mol Biol 434(2):167336. https://doi.org/10.1016/j.jmb.2021.167336

    Article  CAS  PubMed Central  Google Scholar 

  46. Margiotta E, Moro S (2019) A comparison in the use of the crystallographic structure of the human A1 or the A2A adenosine receptors as a template for the construction of a homology model of the A3 subtype. Appl Sci 9(5):821. https://doi.org/10.3390/app9050821

    Article  CAS  Google Scholar 

  47. Shim J, MacKerell Jr AD (2011) Computational ligand-based rational design: role of conformational sampling and force fields in model development. MedChemComm 2(5):356. https://doi.org/10.1039/c1md00044f

    Article  CAS  Google Scholar 

  48. Silakari O, Singh PK (2021) QSAR: descriptor calculations, model generation, validation and their application. In: Concepts and experimental protocols of modelling and informatics in drug design. Elsevier, pp 29–63. https://doi.org/10.1016/B978-0-12-820546-4.00002-7

    Chapter  Google Scholar 

  49. Kubinyi H (1988) Free Wilson analysis. Theory, applications and its relationship to Hansch analysis. Quant Struct Relationships 7(3):121–133. https://doi.org/10.1002/qsar.19880070303

    Article  CAS  Google Scholar 

  50. Yang S-Y (2010) Pharmacophore modeling and applications in drug discovery: challenges and recent advances. Drug Discov Today 15(11–12):444–450. https://doi.org/10.1016/j.drudis.2010.03.013

    Article  CAS  Google Scholar 

  51. Ebejer J-P, Morris GM, Deane CM (2012) Freely available conformer generation methods: how good are they? J Chem Inf Model 52(5):1146–1158. https://doi.org/10.1021/ci2004658

    Article  CAS  Google Scholar 

  52. Bhisetti G, Fang C (2022) Artificial intelligence–enabled de novo design of novel compounds that are synthesizable. Methods Mol Biol 2390:409–419. https://doi.org/10.1007/978-1-0716-1787-8_17

    Article  CAS  Google Scholar 

  53. PDB-101. Methods for determining atomic structures. https://pdb101.rcsb.org/learn/guide-to-understanding-pdb-data/methods-for-determining-structure#:~:text=Several methods are currently used,create the final atomic model.

  54. Thompson MC, Yeates TO, Rodriguez JA (2020) Advances in methods for atomic resolution macromolecular structure determination. F1000Research 9:667. https://doi.org/10.12688/f1000research.25097.1

    Article  CAS  Google Scholar 

  55. Benjin X, Ling L (2020) Developments, applications, and prospects of cryo-electron microscopy. Protein Sci 29(4):872–882. https://doi.org/10.1002/pro.3805

    Article  CAS  Google Scholar 

  56. Carroni M, Saibil HR (2016) Cryo electron microscopy to determine the structure of macromolecular complexes. Methods 95:78–85. https://doi.org/10.1016/j.ymeth.2015.11.023

    Article  CAS  PubMed Central  Google Scholar 

  57. Wang H-W, Wang J-W (2017) How cryo-electron microscopy and X-ray crystallography complement each other. Protein Sci 26(1):32–39. https://doi.org/10.1002/pro.3022

    Article  CAS  Google Scholar 

  58. Callaway E (2015) The revolution will not be crystallized: a new method sweeps through structural biology. Nature 525(7568):172–174. https://doi.org/10.1038/525172a

    Article  CAS  Google Scholar 

  59. Renaud J-P et al (2018) Cryo-EM in drug discovery: achievements, limitations and prospects. Nat Rev Drug Discov 17(7):471–492. https://doi.org/10.1038/nrd.2018.77

    Article  CAS  Google Scholar 

  60. Batool M, Ahmad B, Choi S (2019) A structure-based drug discovery paradigm. Int J Mol Sci 20(11):2783. https://doi.org/10.3390/ijms20112783

    Article  CAS  PubMed Central  Google Scholar 

  61. Bassani D, Ragazzi E, Lapolla A, Sartore G, Moro S (2022) Omicron variant of SARS-CoV-2 virus: in silico evaluation of the possible impact on people affected by diabetes mellitus. Front Endocrinol (Lausanne) 13. https://doi.org/10.3389/fendo.2022.847993

  62. Sartore G, Bassani D, Ragazzi E, Traldi P, Lapolla A, Moro S (2021) In silico evaluation of the interaction between ACE2 and SARS-CoV-2 spike protein in a hyperglycemic environment. Sci Rep 11(1):22860. https://doi.org/10.1038/s41598-021-02297-w

    Article  CAS  PubMed Central  Google Scholar 

  63. Terayama K, Iwata H, Araki M, Okuno Y, Tsuda K (2018) Machine learning accelerates MD-based binding pose prediction between ligands and proteins. Bioinformatics 34(5):770–778. https://doi.org/10.1093/bioinformatics/btx638

    Article  CAS  Google Scholar 

  64. Doerr S et al (2021) TorchMD: a deep learning framework for molecular simulations. J Chem Theory Comput 17(4):2355–2363. https://doi.org/10.1021/acs.jctc.0c01343

    Article  CAS  PubMed Central  Google Scholar 

  65. Shi W, Singha M, Srivastava G, Pu L, Ramanujam J, Brylinski M (2022) Pocket2Drug: an encoder-decoder deep neural network for the target-based drug design. Front Pharmacol 13. https://doi.org/10.3389/fphar.2022.837715

  66. Dong L, Qu X, Zhao Y, Wang B (2021) Prediction of binding free energy of protein–ligand complexes with a hybrid molecular mechanics/generalized born surface area and machine learning method. ACS Omega 6(48):32938–32947. https://doi.org/10.1021/acsomega.1c04996

    Article  CAS  PubMed Central  Google Scholar 

  67. Cournia Z, Allen B, Sherman W (2017) Relative binding free energy calculations in drug discovery: recent advances and practical considerations. J Chem Inf Model 57(12):2911–2937. https://doi.org/10.1021/acs.jcim.7b00564

    Article  CAS  Google Scholar 

  68. Kuntz ID, Blaney JM, Oatley SJ, Langridge R, Ferrin TE (1982) A geometric approach to macromolecule-ligand interactions. J Mol Biol 161(2):269–288. https://doi.org/10.1016/0022-2836(82)90153-X

    Article  CAS  Google Scholar 

  69. Pavan M, Bassani D, Bolcato G, Bissaro M, Sturlese M, Moro S (2022) Computational strategies to identify new drug candidates against neuroinflammation. Curr Med Chem 29(27):4756–4775. https://doi.org/10.2174/0929867329666220208095122

    Article  CAS  Google Scholar 

  70. Li J, Fu A, Zhang L (2019) An overview of scoring functions used for protein–ligand interactions in molecular docking. Interdiscip Sci Comput Life Sci 11(2):320–328. https://doi.org/10.1007/s12539-019-00327-w

    Article  CAS  Google Scholar 

  71. Alogheli H, Olanders G, Schaal W, Brandt P, Karlén A (2017) Docking of macrocycles: comparing rigid and flexible docking in glide. J Chem Inf Model 57(2):190–202. https://doi.org/10.1021/acs.jcim.6b00443

    Article  CAS  Google Scholar 

  72. Landrum G (2010) RDKit: Open-source cheminformatics. [Online]. Available: https://www.rdkit.org/

  73. Hawkins PCD, Skillman AG, Warren GL, Ellingson BA, Stahl MT (2010) Conformer generation with OMEGA: algorithm and validation using high quality structures from the protein databank and Cambridge structural database. J Chem Inf Model 50(4):572–584. https://doi.org/10.1021/ci100031x

    Article  CAS  PubMed Central  Google Scholar 

  74. Huang S-Y (2018) Comprehensive assessment of flexible-ligand docking algorithms: current effectiveness and challenges. Brief Bioinform 19(5):982–994. https://doi.org/10.1093/bib/bbx030

    Article  CAS  Google Scholar 

  75. Sherman W, Day T, Jacobson MP, Friesner RA, Farid R (2006) Novel procedure for modeling ligand/receptor induced fit effects. J Med Chem 49(2):534–553. https://doi.org/10.1021/jm050540c

    Article  CAS  Google Scholar 

  76. Miller EB et al (2021) Reliable and accurate solution to the induced fit docking problem for protein–ligand binding. J Chem Theory Comput 17(4):2630–2639. https://doi.org/10.1021/acs.jctc.1c00136

    Article  CAS  Google Scholar 

  77. Amaro RE et al (2018) Ensemble docking in drug discovery. Biophys J 114(10):2271–2278. https://doi.org/10.1016/j.bpj.2018.02.038

    Article  CAS  PubMed Central  Google Scholar 

  78. Jones G, Willett P, Glen RC, Leach AR, Taylor R (1997) Development and validation of a genetic algorithm for flexible docking. J Mol Biol 267(3). https://doi.org/10.1006/jmbi.1996.0897

  79. Morris GM et al (2009) AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem 30(16):2785–2791. https://doi.org/10.1002/jcc.21256

    Article  CAS  PubMed Central  Google Scholar 

  80. Friesner RA et al (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47(7). https://doi.org/10.1021/jm0306430

  81. Korb O, Stützle T, Exner TE (2006) PLANTS: application of ant colony optimization to structure-based drug design. https://doi.org/10.1007/11839088_22

    Book  Google Scholar 

  82. Bassani D, Pavan M, Bolcato G, Sturlese M, Moro S (2022) Re-exploring the ability of common docking programs to correctly reproduce the binding modes of non-covalent inhibitors of SARS-CoV-2 protease Mpro. Pharmaceuticals 15(2):180. https://doi.org/10.3390/ph15020180

    Article  CAS  PubMed Central  Google Scholar 

  83. Bolcato G, Cuzzolin A, Bissaro M, Moro S, Sturlese M (2019) Can we still trust docking results? An extension of the applicability of DockBench on PDBbind database. Int J Mol Sci 20(14):3558. https://doi.org/10.3390/ijms20143558

    Article  CAS  PubMed Central  Google Scholar 

  84. Ramírez D, Caballero J (2018) Is it reliable to take the molecular docking top scoring position as the best solution without considering available structural data? Molecules 23(5):1038. https://doi.org/10.3390/molecules23051038

    Article  CAS  PubMed Central  Google Scholar 

  85. Gabel J, Desaphy J, Rognan D (2014) Beware of machine learning-based scoring functions – on the danger of developing black boxes. J Chem Inf Model 54(10):2807–2815. https://doi.org/10.1021/ci500406k

    Article  CAS  Google Scholar 

  86. Wang Z et al (2016) Comprehensive evaluation of ten docking programs on a diverse set of protein–ligand complexes: the prediction accuracy of sampling power and scoring power. Phys Chem Chem Phys 18(18):12964–12975. https://doi.org/10.1039/C6CP01555G

    Article  CAS  Google Scholar 

  87. Ha EJ, Lwin CT, Durrant JD (2020) LigGrep: a tool for filtering docked poses to improve virtual-screening hit rates. J Cheminform 12(1):69. https://doi.org/10.1186/s13321-020-00471-2

    Article  CAS  PubMed Central  Google Scholar 

  88. Gushchina IV, Polenova AM, Suplatov DA, Švedas VK, Nilov DK (2020) vsFilt: a tool to improve virtual screening by structural filtration of docking poses. J Chem Inf Model 60(8):3692–3696. https://doi.org/10.1021/acs.jcim.0c00303

    Article  CAS  Google Scholar 

  89. Spinaci A et al (2023) ‘Dual Anta-Inhibitors’ of the A2A adenosine receptor and casein kinase CK1delta: synthesis, biological evaluation, and molecular modeling studies. Pharmaceuticals 16(2):167. https://doi.org/10.3390/ph16020167

    Article  CAS  PubMed Central  Google Scholar 

  90. Carbone D et al (2023) Discovery of the 3-amino-1,2,4-triazine-based library as selective PDK1 inhibitors with therapeutic potential in highly aggressive pancreatic ductal adenocarcinoma. Int J Mol Sci 24(4):3679. https://doi.org/10.3390/ijms24043679

    Article  CAS  PubMed Central  Google Scholar 

  91. Boeyens J (2001) Molecular mechanics: theoretical basis, rules, scope and limits. Coord Chem Rev 212(1):3–10. https://doi.org/10.1016/S0010-8545(00)00353-2

    Article  CAS  Google Scholar 

  92. Wang J, Cieplak P, Li J, Hou T, Luo R, Duan Y (2011) Development of polarizable models for molecular mechanical calculations I: parameterization of atomic polarizability. J Phys Chem B 115(12):3091–3099. https://doi.org/10.1021/jp112133g

    Article  CAS  PubMed Central  Google Scholar 

  93. Jing Z et al (2019) Polarizable force fields for biomolecular simulations: recent advances and applications. Annu Rev Biophys 48(1):371–394. https://doi.org/10.1146/annurev-biophys-070317-033349

    Article  CAS  PubMed Central  Google Scholar 

  94. Sighel D et al (2023) Streptogramin a derivatives as mitochondrial translation inhibitors to suppress glioblastoma stem cell growth. Eur J Med Chem 246:114979. https://doi.org/10.1016/j.ejmech.2022.114979

    Article  CAS  Google Scholar 

  95. Heilmann E et al (2023) SARS-CoV-2 3CL pro mutations selected in a VSV-based system confer resistance to nirmatrelvir, ensitrelvir, and GC376. Sci Transl Med 15(678). https://doi.org/10.1126/scitranslmed.abq7360

  96. de Beer S, Vermeulen N, Oostenbrink C (2010) The role of water molecules in computational drug design. Curr Top Med Chem 10(1):55–66. https://doi.org/10.2174/156802610790232288

    Article  Google Scholar 

  97. Hu J, Sun X, Kang Z, Cheng J (2023) Computational investigation of functional water molecules in GPCRs bound to G protein or arrestin. J Comput Aided Mol Des 37(2):91–105. https://doi.org/10.1007/s10822-022-00492-z

    Article  CAS  Google Scholar 

  98. Rappas M et al (2020) Comparison of orexin 1 and orexin 2 ligand binding modes using X-ray crystallography and computational analysis. J Med Chem 63(4):1528–1543. https://doi.org/10.1021/acs.jmedchem.9b01787

    Article  CAS  Google Scholar 

  99. Breznik M et al (2023) Prioritizing small sets of molecules for synthesis through in-silico tools: a comparison of common ranking methods. ChemMedChem 18(1). https://doi.org/10.1002/cmdc.202200425

  100. Huang N, Shoichet BK (2008) Exploiting ordered waters in molecular docking. J Med Chem 51(16):4862–4865. https://doi.org/10.1021/jm8006239

    Article  CAS  PubMed Central  Google Scholar 

  101. Wahl J, Smieško M (2019) Assessing the predictive power of relative binding free energy calculations for test cases involving displacement of binding site water molecules. J Chem Inf Model 59(2):754–765. https://doi.org/10.1021/acs.jcim.8b00826

    Article  CAS  Google Scholar 

  102. Song Q, Zeng L, Zheng Q, Liu S (2023) SCARdock: a web server and manually curated resource for discovering covalent ligands. ACS Omega 8(11):10397–10402. https://doi.org/10.1021/acsomega.2c08147

    Article  CAS  PubMed Central  Google Scholar 

  103. Toledo Warshaviak D, Golan G, Borrelli KW, Zhu K, Kalid O (2014) Structure-based virtual screening approach for discovery of covalently bound ligands. J Chem Inf Model 54(7):1941–1950. https://doi.org/10.1021/ci500175r

    Article  CAS  Google Scholar 

  104. Kumalo H, Bhakat S, Soliman M (2015) Theory and applications of covalent docking in drug discovery: merits and pitfalls. Molecules 20(2):1984–2000. https://doi.org/10.3390/molecules20021984

    Article  CAS  PubMed Central  Google Scholar 

  105. Groenhof G (2013) Introduction to QM/MM simulations. Methods Mol Biol:43–66. https://doi.org/10.1007/978-1-62703-017-5_3

  106. Chaskar P, Zoete V, Röhrig UF (2014) Toward on-the-fly quantum mechanical/molecular mechanical (QM/MM) docking: development and benchmark of a scoring function. J Chem Inf Model 54(11):3137–3152. https://doi.org/10.1021/ci5004152

    Article  CAS  Google Scholar 

  107. Mihalovits LM, Ferenczy GG, Keserű GM (2022) The role of quantum chemistry in covalent inhibitor design. Int J Quantum Chem 122(8). https://doi.org/10.1002/qua.26768

  108. Lyu J et al (2019) Ultra-large library docking for discovering new chemotypes. Nature 566(7743):224–229. https://doi.org/10.1038/s41586-019-0917-9

    Article  CAS  PubMed Central  Google Scholar 

  109. Gentile F et al (2022) Artificial intelligence–enabled virtual screening of ultra-large chemical libraries with deep docking. Nat Protoc 17(3):672–697. https://doi.org/10.1038/s41596-021-00659-2

    Article  CAS  Google Scholar 

  110. Inamdar GS et al (2013) New insight into adenosine receptors selectivity derived from a novel series of [5-substituted-4-phenyl-1,3-thiazol-2-yl] benzamides and furamides. Eur J Med Chem 63:924–934. https://doi.org/10.1016/j.ejmech.2013.03.020

    Article  CAS  Google Scholar 

  111. Rodríguez A et al (2015) New selective a 2A agonists and a 3 antagonists for human adenosine receptors: synthesis, biological activity and molecular docking studies. MedChemComm 6(6):1178–1185. https://doi.org/10.1039/C5MD00086F

    Article  CAS  Google Scholar 

  112. Federico S et al (2018) [1,2,4]triazolo[1,5-c]pyrimidines as adenosine receptor antagonists: modifications at the 8 position to reach selectivity towards A3 adenosine receptor subtype. Eur J Med Chem 157:837–851. https://doi.org/10.1016/j.ejmech.2018.08.042

    Article  CAS  Google Scholar 

  113. Wang M, Hou S, Wei Y, Li D, Lin J (2021) Discovery of novel dual adenosine A1/A2A receptor antagonists using deep learning, pharmacophore modeling and molecular docking. PLoS Comput Biol 17(3):e1008821. https://doi.org/10.1371/journal.pcbi.1008821

    Article  CAS  PubMed Central  Google Scholar 

  114. Sarkar B, Maiti S, Jadhav GR, Paira P (2018) Discovery of benzothiazolylquinoline conjugates as novel human a 3 receptor antagonists: biological evaluations and molecular docking studies. R Soc Open Sci 5(2):171622. https://doi.org/10.1098/rsos.171622

    Article  CAS  PubMed Central  Google Scholar 

  115. Cuzzolin A, Sturlese M, Malvacio I, Ciancetta A, Moro S (2015) DockBench: an integrated informatic platform bridging the gap between the robust validation of docking protocols and virtual screening simulations. Molecules 20(6):9977–9993. https://doi.org/10.3390/molecules20069977

    Article  CAS  PubMed Central  Google Scholar 

  116. Margiotta E, Deganutti G, Moro S (2018) Could the presence of sodium ion influence the accuracy and precision of the ligand-posing in the human A2A adenosine receptor orthosteric binding site using a molecular docking approach? Insights from Dockbench. J Comput Aided Mol Des 32(12):1337–1346. https://doi.org/10.1007/s10822-018-0174-2

    Article  CAS  Google Scholar 

  117. Bassani D, Pavan M, Sturlese M, Moro S (2022) Sodium or not sodium: should its presence affect the accuracy of pose prediction in docking GPCR antagonists? Pharmaceuticals 15(3):346. https://doi.org/10.3390/ph15030346

    Article  CAS  PubMed Central  Google Scholar 

  118. Hollingsworth SA, Dror RO (2018) Molecular dynamics simulation for all. Neuron 99(6):1129–1143. https://doi.org/10.1016/j.neuron.2018.08.011

    Article  CAS  PubMed Central  Google Scholar 

  119. Pavan M, Moro S (2023) Lessons learnt from COVID-19: computational strategies for facing present and future pandemics. Int J Mol Sci 24(5):4401. https://doi.org/10.3390/ijms24054401

    Article  CAS  PubMed Central  Google Scholar 

  120. Bolcato G et al (2021) A computational workflow for the identification of novel fragments acting as inhibitors of the activity of protein kinase CK1δ. Int J Mol Sci 22(18):9741. https://doi.org/10.3390/ijms22189741

    Article  CAS  PubMed Central  Google Scholar 

  121. Case PAKDA, Aktulga HM, Belfon K, Ben-Shalom IY, Berryman JT, Brozell SR, Cerutti DS, Cheatham III TE, Cisneros GA, Cruzeiro VWD, Darden TA, Duke RE, Giambasu G, Gilson MK, Gohlke H, Goetz AW, Harris R, Izadi S, Izmailov SA (2017) Comparison of implicit and explicit solvent models for the calculation of solvation free energy in organic solvents. J Chem Theory Comput 13(3):1034–1043. https://doi.org/10.1021/acs.jctc.7b00169

    Article  CAS  Google Scholar 

  122. Marenich AV, Cramer CJ, Truhlar DG (2009) Universal solvation model based on solute electron density and on a continuum model of the solvent defined by the bulk dielectric constant and atomic surface tensions. J Phys Chem B 113(18):6378–6396. https://doi.org/10.1021/jp810292n

    Article  CAS  Google Scholar 

  123. Klamt A, Schüürmann G (1993) COSMO: a new approach to dielectric screening in solvents with explicit expressions for the screening energy and its gradient. J Chem Soc Perkin Trans 2(5):799–805. https://doi.org/10.1039/P29930000799

    Article  Google Scholar 

  124. Takano Y, Houk KN (2005) Benchmarking the conductor-like polarizable continuum model (CPCM) for aqueous solvation free energies of neutral and ionic organic molecules. J Chem Theory Comput 1(1):70–77. https://doi.org/10.1021/ct049977a

    Article  CAS  Google Scholar 

  125. Dyer KM, Perkyns JS, Stell G, Montgomery Pettitt B (2009) Site-renormalised molecular fluid theory: on the utility of a two-site model of water. Mol Phys 107(4–6):423–431. https://doi.org/10.1080/00268970902845313

    Article  CAS  PubMed Central  Google Scholar 

  126. Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML (1983) Comparison of simple potential functions for simulating liquid water. J Chem Phys 79(2):926–935. https://doi.org/10.1063/1.445869

    Article  CAS  Google Scholar 

  127. Florová P, Sklenovský P, Banáš P, Otyepka M (2010) Explicit water models affect the specific solvation and dynamics of unfolded peptides while the conformational behavior and flexibility of folded peptides remain intact. J Chem Theory Comput 6(11):3569–3579. https://doi.org/10.1021/ct1003687

    Article  CAS  Google Scholar 

  128. Deganutti G, Moro S, Reynolds CA (2019) Peeking at G-protein-coupled receptors through the molecular dynamics keyhole. Future Med Chem 11(6):599–615. https://doi.org/10.4155/fmc-2018-0393

    Article  CAS  Google Scholar 

  129. Dror RO et al (2011) Pathway and mechanism of drug binding to G-protein-coupled receptors. Proc Natl Acad Sci 108(32):13118–13123. https://doi.org/10.1073/pnas.1104614108

    Article  PubMed Central  Google Scholar 

  130. Schaller D, Pach S, Wolber G (2019) PyRod: tracing water molecules in molecular dynamics simulations. J Chem Inf Model 59(6):2818–2829. https://doi.org/10.1021/acs.jcim.9b00281

    Article  CAS  Google Scholar 

  131. Araya-Secchi R et al (2014) Characterization of a novel water pocket inside the human Cx26 hemichannel structure. Biophys J 107(3):599–612. https://doi.org/10.1016/j.bpj.2014.05.037

    Article  CAS  PubMed Central  Google Scholar 

  132. Bellissent-Funel M-C et al (2016) Water determines the structure and dynamics of proteins. Chem Rev 116(13):7673–7697. https://doi.org/10.1021/acs.chemrev.5b00664

    Article  CAS  PubMed Central  Google Scholar 

  133. Venkatakrishnan AJ et al (2019) Diverse GPCRs exhibit conserved water networks for stabilization and activation. Proc Natl Acad Sci 116(8):3288–3293. https://doi.org/10.1073/pnas.1809251116

    Article  CAS  PubMed Central  Google Scholar 

  134. Ciancetta A, Sabbadin D, Federico S, Spalluto G, Moro S (2015) Advances in computational techniques to study GPCR–ligand recognition. Trends Pharmacol Sci 36(12):878–890. https://doi.org/10.1016/j.tips.2015.08.006

    Article  CAS  Google Scholar 

  135. Gowers R et al (2016) MDAnalysis: a python package for the rapid analysis of molecular dynamics simulations. pp 98–105. https://doi.org/10.25080/Majora-629e541a-00e

  136. Zhang S et al (2021) ProDy 2.0: increased scale and scope after 10 years of protein dynamics modelling with python. Bioinformatics 37(20):3657–3659. https://doi.org/10.1093/bioinformatics/btab187

    Article  CAS  PubMed Central  Google Scholar 

  137. Abel R, Young T, Farid R, Berne BJ, Friesner RA (2008) Role of the active-site solvent in the thermodynamics of factor Xa ligand binding. J Am Chem Soc 130(9):2817–2831. https://doi.org/10.1021/ja0771033

    Article  CAS  PubMed Central  Google Scholar 

  138. Baroni M, Cruciani G, Sciabola S, Perruccio F, Mason JS (2007) A common reference framework for analyzing/comparing proteins and ligands. Fingerprints for ligands and proteins (FLAP): theory and application. J Chem Inf Model 47(2):279–294. https://doi.org/10.1021/ci600253e

    Article  CAS  Google Scholar 

  139. Cuzzolin A, Deganutti G, Salmaso V, Sturlese M, Moro S (2018) AquaMMapS: an alternative tool to monitor the role of water molecules during protein-ligand association. ChemMedChem 13(6):522–531. https://doi.org/10.1002/cmdc.201700564

    Article  CAS  Google Scholar 

  140. Shaw DE et al (2021) Anton 3. In: Proceedings of the international conference for high performance computing, networking, storage and analysis, pp 1–11. https://doi.org/10.1145/3458817.3487397

    Chapter  Google Scholar 

  141. Fornasier E et al (2022) A new inactive conformation of SARS-CoV-2 main protease. Acta Crystallogr Sect D Struct Biol 78(3):363–378. https://doi.org/10.1107/S2059798322000948

    Article  CAS  Google Scholar 

  142. Bolcato G, Bissaro M, Sturlese M, Moro S (2020) Comparing fragment binding poses prediction using HSP90 as a key study: when bound water makes the difference. Molecules 25(20):4651. https://doi.org/10.3390/molecules25204651

    Article  CAS  PubMed Central  Google Scholar 

  143. Case DA (2022) Amber22. University of California, San Francisco

    Google Scholar 

  144. Scott WRP et al (1999) The GROMOS biomolecular simulation program package. J Phys Chem A 103(19):3596–3607. https://doi.org/10.1021/jp984217f

    Article  CAS  Google Scholar 

  145. Jorgensen WL, Tirado-Rives J (1988) The OPLS [optimized potentials for liquid simulations] potential functions for proteins, energy minimizations for crystals of cyclic peptides and crambin. J Am Chem Soc 110(6):1657–1666. https://doi.org/10.1021/ja00214a001

    Article  CAS  Google Scholar 

  146. Brooks BR et al (2009) CHARMM: the biomolecular simulation program. J Comput Chem 30(10):1545–1614. https://doi.org/10.1002/jcc.21287

    Article  CAS  PubMed Central  Google Scholar 

  147. Lodola A, De Vivo M (2012) The increasing role of QM/MM in drug discovery. Adv Protein Chem Struct Biol 87:337–362. https://doi.org/10.1016/B978-0-12-398312-1.00011-1

    Article  CAS  Google Scholar 

  148. Harvey MJ, De Fabritiis G (2012) High-throughput molecular dynamics: the powerful new tool for drug discovery. Drug Discov Today 17(19–20):1059–1062. https://doi.org/10.1016/j.drudis.2012.03.017

    Article  CAS  Google Scholar 

  149. Bissaro M, Bolcato G, Pavan M, Bassani D, Sturlese M, Moro S (2021) Inspecting the mechanism of fragment hits binding on SARS-CoV-2 M pro by using supervised molecular dynamics (SuMD) simulations. ChemMedChem 16(13):2075–2081. https://doi.org/10.1002/cmdc.202100156

    Article  CAS  PubMed Central  Google Scholar 

  150. Durrant JD, McCammon JA (2011) Molecular dynamics simulations and drug discovery. BMC Biol 9(1):71. https://doi.org/10.1186/1741-7007-9-71

    Article  CAS  PubMed Central  Google Scholar 

  151. Knapp B, Ospina L, Deane CM (2018) Avoiding false positive conclusions in molecular simulation: the importance of replicas. J Chem Theory Comput 14(12):6127–6138. https://doi.org/10.1021/acs.jctc.8b00391

    Article  CAS  Google Scholar 

  152. Kubitzki MB, de Groot BL (2007) Molecular dynamics simulations using temperature-enhanced essential dynamics replica exchange. Biophys J 92(12):4262–4270. https://doi.org/10.1529/biophysj.106.103101

    Article  CAS  PubMed Central  Google Scholar 

  153. Aier I, Varadwaj PK, Raj U (2016) Structural insights into conformational stability of both wild-type and mutant EZH2 receptor. Sci Rep 6(1):34984. https://doi.org/10.1038/srep34984

    Article  CAS  PubMed Central  Google Scholar 

  154. Ivanova L, Tammiku-Taul J, García-Sosa AT, Sidorova Y, Saarma M, Karelson M (2018) Molecular dynamics simulations of the interactions between glial cell line-derived neurotrophic factor family receptor GFRα1 and small-molecule ligands. ACS Omega 3(9):11407–11414. https://doi.org/10.1021/acsomega.8b01524

    Article  CAS  PubMed Central  Google Scholar 

  155. Martínez L (2015) Automatic identification of mobile and rigid substructures in molecular dynamics simulations and fractional structural fluctuation analysis. PloS One 10(3):e0119264. https://doi.org/10.1371/journal.pone.0119264

    Article  CAS  PubMed Central  Google Scholar 

  156. Knapp B, Frantal S, Cibena M, Schreiner W, Bauer P (2011) Is an intuitive convergence definition of molecular dynamics simulations solely based on the root mean square deviation possible? J Comput Biol 18(8):997–1005. https://doi.org/10.1089/cmb.2010.0237

    Article  CAS  PubMed Central  Google Scholar 

  157. Roe DR, Cheatham TE (2013) PTRAJ and CPPTRAJ: software for processing and analysis of molecular dynamics trajectory data. J Chem Theory Comput 9(7):3084–3095. https://doi.org/10.1021/ct400341p

    Article  CAS  Google Scholar 

  158. Bowers KJ et al (2006) Molecular dynamics – scalable algorithms for molecular dynamics simulations on commodity clusters. In: Proceedings of the 2006 ACM/IEEE conference on supercomputing – SC ’06, p 84. https://doi.org/10.1145/1188455.1188544

  159. Cescon E et al (2020) Scaffold repurposing of in-house chemical library toward the identification of new casein kinase 1 δ inhibitors. ACS Med Chem Lett 11(6):1168–1174. https://doi.org/10.1021/acsmedchemlett.0c00028

    Article  CAS  PubMed Central  Google Scholar 

  160. Da C, Kireev D (2014) Structural protein–ligand interaction fingerprints (SPLIF) for structure-based virtual screening: method and benchmark study. J Chem Inf Model 54(9):2555–2561. https://doi.org/10.1021/ci500319f

    Article  CAS  PubMed Central  Google Scholar 

  161. Bouysset C, Fiorucci S (2021) ProLIF: a library to encode molecular interactions as fingerprints. J Cheminform 13(1):72. https://doi.org/10.1186/s13321-021-00548-6

    Article  PubMed Central  Google Scholar 

  162. Wójcikowski M, Zielenkiewicz P, Siedlecki P (2015) Open drug discovery toolkit (ODDT): a new open-source player in the drug discovery field. J Cheminform 7(1):26. https://doi.org/10.1186/s13321-015-0078-2

    Article  PubMed Central  Google Scholar 

  163. Pavan M, Menin S, Bassani D, Sturlese M, Moro S (2022) Implementing a scoring function based on interaction fingerprint for Autogrow4: protein kinase CK1δ as a case study. Front Mol Biosci 9. https://doi.org/10.3389/fmolb.2022.909499

  164. Spiegel JO, Durrant JD (2020) AutoGrow4: an open-source genetic algorithm for de novo drug design and lead optimization. J Cheminform 12(1):25. https://doi.org/10.1186/s13321-020-00429-4

    Article  CAS  PubMed Central  Google Scholar 

  165. Pavan M, Menin S, Bassani D, Sturlese M, Moro S (2022) Qualitative estimation of protein–ligand complex stability through thermal titration molecular dynamics simulations. J Chem Inf Model. https://doi.org/10.1021/acs.jcim.2c00995

  166. Menin S, Pavan M, Salmaso V, Sturlese M, Moro S (2023) Thermal titration molecular dynamics (TTMD): not your usual post-docking refinement. Int J Mol Sci 24(4):3596. https://doi.org/10.3390/ijms24043596

    Article  CAS  PubMed Central  Google Scholar 

  167. Long A, Zhao H, Huang X (2012) Structural basis for the interaction between casein kinase 1 delta and a potent and selective inhibitor. J Med Chem 55(2):956–960. https://doi.org/10.1021/jm201387s

    Article  CAS  Google Scholar 

  168. Ursu A et al (2016) Epiblastin a induces reprogramming of epiblast stem cells into embryonic stem cells by inhibition of casein kinase 1. Cell Chem Biol 23(4):494–507. https://doi.org/10.1016/j.chembiol.2016.02.015

    Article  CAS  Google Scholar 

  169. Anighoro A, Rastelli G (2013) Enrichment factor analyses on G-protein coupled receptors with known crystal structure. J Chem Inf Model 53(4):739–743. https://doi.org/10.1021/ci4000745

    Article  CAS  Google Scholar 

  170. Martinelli A, Ortore G (2013) Molecular modeling of adenosine receptors. Methods Enzymol:37–59. https://doi.org/10.1016/B978-0-12-407865-9.00003-0

  171. Catarzi D et al (2013) Pyrazolo[1,5-c]quinazoline derivatives and their simplified analogues as adenosine receptor antagonists: synthesis, structure–affinity relationships and molecular modeling studies. Bioorg Med Chem 21(1):283–294. https://doi.org/10.1016/j.bmc.2012.10.031

    Article  CAS  Google Scholar 

  172. Jespers W et al (2017) Structure-based design of potent and selective ligands at the four adenosine receptors. Molecules 22(11):1945. https://doi.org/10.3390/molecules22111945

    Article  CAS  PubMed Central  Google Scholar 

  173. Sabbadin D, Ciancetta A, Moro S (2014) Bridging molecular docking to membrane molecular dynamics to investigate GPCR–ligand recognition: the human a 2A adenosine receptor as a key study. J Chem Inf Model 54(1):169–183. https://doi.org/10.1021/ci400532b

    Article  CAS  Google Scholar 

  174. Lotz SD, Dickson A (2018) Unbiased molecular dynamics of 11 min timescale drug unbinding reveals transition state stabilizing interactions. J Am Chem Soc 140(2):618–628. https://doi.org/10.1021/jacs.7b08572

    Article  CAS  Google Scholar 

  175. Hartmann C, Banisch R, Sarich M, Badowski T, Schütte C (2013) Characterization of rare events in molecular dynamics. Entropy 16(1):350–376. https://doi.org/10.3390/e16010350

    Article  CAS  Google Scholar 

  176. Bernardi RC, Melo MCR, Schulten K (2015) Enhanced sampling techniques in molecular dynamics simulations of biological systems. Biochim Biophys Acta Gen Subj 1850(5):872–877. https://doi.org/10.1016/j.bbagen.2014.10.019

    Article  CAS  Google Scholar 

  177. Patel JS, Berteotti A, Ronsisvalle S, Rocchia W, Cavalli A (2014) Steered molecular dynamics simulations for studying protein–ligand interaction in cyclin-dependent kinase 5. J Chem Inf Model 54(2):470–480. https://doi.org/10.1021/ci4003574

    Article  CAS  Google Scholar 

  178. Sinko W, Miao Y, de Oliveira CAF, McCammon JA (2013) Population based reweighting of scaled molecular dynamics. J Phys Chem B 117(42):12759–12768. https://doi.org/10.1021/jp401587e

    Article  CAS  PubMed Central  Google Scholar 

  179. Qi R, Wei G, Ma B, Nussinov R (2018) Replica exchange molecular dynamics: a practical application protocol with solutions to common problems and a peptide aggregation and self-assembly example. Methods Mol Biol:101–119. https://doi.org/10.1007/978-1-4939-7811-3_5

  180. Sabbadin D, Moro S (2014) Supervised molecular dynamics (SuMD) as a helpful tool to depict GPCR–ligand recognition pathway in a nanosecond time scale. J Chem Inf Model 54(2):372–376. https://doi.org/10.1021/ci400766b

    Article  CAS  Google Scholar 

  181. Bussi G, Laio A (2020) Using metadynamics to explore complex free-energy landscapes. Nat Rev Phys 2(4):200–212. https://doi.org/10.1038/s42254-020-0153-0

    Article  Google Scholar 

  182. Potterton A et al (2019) Ensemble-based steered molecular dynamics predicts relative residence time of a 2A receptor binders. J Chem Theory Comput 15(5):3316–3330. https://doi.org/10.1021/acs.jctc.8b01270

    Article  CAS  Google Scholar 

  183. Akhunzada MJ, Yoon HJ, Deb I, Braka A, Wu S (2022) Bell-Evans model and steered molecular dynamics in uncovering the dissociation kinetics of ligands targeting G-protein-coupled receptors. Sci Rep 12(1):15972. https://doi.org/10.1038/s41598-022-20065-2

    Article  CAS  PubMed Central  Google Scholar 

  184. Cuzzolin A et al (2016) Deciphering the complexity of ligand-protein recognition pathways using supervised molecular dynamics (SuMD) simulations. J Chem Inf Model 56(4):687–705. https://doi.org/10.1021/acs.jcim.5b00702

    Article  CAS  Google Scholar 

  185. Panday SK, Sturlese M, Salmaso V, Ghosh I, Moro S (2019) Coupling supervised molecular dynamics (SuMD) with entropy estimations to Shine light on the stability of multiple binding sites. ACS Med Chem Lett 10(4):444–449. https://doi.org/10.1021/acsmedchemlett.8b00490

    Article  CAS  PubMed Central  Google Scholar 

  186. Bissaro M, Federico S, Salmaso V, Sturlese M, Spalluto G, Moro S (2018) Targeting protein kinase CK1δ with Riluzole: could it be one of the possible missing bricks to interpret its effect in the treatment of ALS from a molecular point of view? ChemMedChem 13(24):2601–2605. https://doi.org/10.1002/cmdc.201800632

    Article  CAS  Google Scholar 

  187. Pavan M, Bolcato G, Bassani D, Sturlese M, Moro S (2021) Supervised molecular dynamics (SuMD) insights into the mechanism of action of SARS-CoV-2 main protease inhibitor PF-07321332. J Enzyme Inhib Med Chem 36(1):1645–1649. https://doi.org/10.1080/14756366.2021.1954919

    Article  CAS  Google Scholar 

  188. Pavan M, Bassani D, Sturlese M, Moro S (2022) Investigating RNA–protein recognition mechanisms through supervised molecular dynamics (SuMD) simulations. NAR Genomics Bioinforma 4(4). https://doi.org/10.1093/nargab/lqac088

  189. Bolcato G, Pavan M, Bassani D, Sturlese M, Moro S (2022) Ribose and non-ribose A2A adenosine receptor agonists: do they share the same receptor recognition mechanism? Biomedicine 10(2):515. https://doi.org/10.3390/biomedicines10020515

    Article  CAS  Google Scholar 

  190. Sabbadin D, Salmaso V, Sturlese M, Moro S (2018) Supervised molecular dynamics (SuMD) approaches in drug design, pp 287–298. https://doi.org/10.1007/978-1-4939-8630-9_17

  191. Sabbadin D, Ciancetta A, Deganutti G, Cuzzolin A, Moro S (2015) Exploring the recognition pathway at the human a 2A adenosine receptor of the endogenous agonist adenosine using supervised molecular dynamics simulations. MedChemCommun 6(6):1081–1085. https://doi.org/10.1039/C5MD00016E

    Article  CAS  Google Scholar 

  192. Deganutti G, Welihinda A, Moro S (2017) Comparison of the human a 2A adenosine receptor recognition by adenosine and inosine: new insight from supervised molecular dynamics simulations. ChemMedChem 12(16):1319–1326. https://doi.org/10.1002/cmdc.201700200

    Article  CAS  Google Scholar 

  193. De Filippo E et al (2020) A2A and A2B adenosine receptors: the extracellular loop 2 determines high (A2A) or low affinity (A2B) for adenosine. Biochem Pharmacol 172:113718. https://doi.org/10.1016/j.bcp.2019.113718

    Article  CAS  Google Scholar 

  194. Deganutti G, Cuzzolin A, Ciancetta A, Moro S (2015) Understanding allosteric interactions in G protein-coupled receptors using supervised molecular dynamics: a prototype study analysing the human A3 adenosine receptor positive allosteric modulator LUF6000. Bioorg Med Chem 23(14):4065–4071. https://doi.org/10.1016/j.bmc.2015.03.039

    Article  CAS  Google Scholar 

  195. Bolcato G, Bissaro M, Deganutti G, Sturlese M, Moro S (2020) New insights into key determinants for adenosine 1 receptor antagonists selectivity using supervised molecular dynamics simulations. Biomol Ther 10(5):732. https://doi.org/10.3390/biom10050732

    Article  CAS  Google Scholar 

  196. Bissaro M, Bolcato G, Deganutti G, Sturlese M, Moro S (2019) Revisiting the allosteric regulation of sodium cation on the binding of adenosine at the human A2A adenosine receptor: insights from supervised molecular dynamics (SuMD) simulations. Molecules 24(15):2752. https://doi.org/10.3390/molecules24152752

    Article  PubMed Central  Google Scholar 

  197. Cao R, Giorgetti A, Bauer A, Neumaier B, Rossetti G, Carloni P (2018) Role of extracellular loops and membrane lipids for ligand recognition in the neuronal adenosine receptor type 2A: an enhanced sampling simulation study. Molecules 23(10):2616. https://doi.org/10.3390/molecules23102616

    Article  CAS  PubMed Central  Google Scholar 

  198. Li J, Jonsson AL, Beuming T, Shelley JC, Voth GA (2013) Ligand-dependent activation and deactivation of the human adenosine a 2A receptor. J Am Chem Soc 135(23):8749–8759. https://doi.org/10.1021/ja404391q

    Article  CAS  PubMed Central  Google Scholar 

  199. Deganutti G et al (2017) Impact of protein–ligand solvation and desolvation on transition state thermodynamic properties of adenosine A2A ligand binding kinetics. Silico Pharmacol 5(1):16. https://doi.org/10.1007/s40203-017-0037-x

    Article  Google Scholar 

  200. Zwanzig RW (1954) High-temperature equation of state by a perturbation method. I. Nonpolar gases. J Chem Phys 22(8):1420–1426. https://doi.org/10.1063/1.1740409

    Article  CAS  Google Scholar 

  201. Fratev F, Sirimulla S (2019) An improved free energy perturbation FEP+ sampling protocol for flexible ligand-binding domains. Sci Rep 9(1):16829. https://doi.org/10.1038/s41598-019-53133-1

    Article  CAS  PubMed Central  Google Scholar 

  202. Wu D et al (2022) Free energy perturbation (FEP)-guided scaffold hopping. Acta Pharm Sin B 12(3):1351–1362. https://doi.org/10.1016/j.apsb.2021.09.027

    Article  CAS  Google Scholar 

  203. Wang L et al (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(7):2695–2703. https://doi.org/10.1021/ja512751q

    Article  CAS  Google Scholar 

  204. Matricon P et al (2017) Fragment optimization for GPCRs by molecular dynamics free energy calculations: probing druggable subpockets of the a 2A adenosine receptor binding site. Sci Rep 7(1):6398. https://doi.org/10.1038/s41598-017-04905-0

    Article  CAS  PubMed Central  Google Scholar 

  205. Matricon P, Suresh RR, Gao Z-G, Panel N, Jacobson KA, Carlsson J (2021) Ligand design by targeting a binding site water. Chem Sci 12(3):960–968. https://doi.org/10.1039/D0SC04938G

    Article  CAS  Google Scholar 

  206. Jespers W et al (2021) Deciphering conformational selectivity in the A2A adenosine G protein-coupled receptor by free energy simulations. PLoS Comput Biol 17(11):e1009152. https://doi.org/10.1371/journal.pcbi.1009152

    Article  CAS  PubMed Central  Google Scholar 

  207. Jespers W et al (2020) X-ray crystallography and free energy calculations reveal the binding mechanism of a 2A adenosine receptor antagonists. Angew Chem Int Ed 59(38):16536–16543. https://doi.org/10.1002/anie.202003788

    Article  CAS  Google Scholar 

  208. Wang X et al (2021) Identification of V6.51L as a selectivity hotspot in stereoselective A2B adenosine receptor antagonist recognition. Sci Rep 11(1):14171. https://doi.org/10.1038/s41598-021-93419-x

    Article  CAS  PubMed Central  Google Scholar 

  209. Deflorian F et al (2020) Accurate prediction of GPCR ligand binding affinity with free energy perturbation. J Chem Inf Model 60(11):5563–5579. https://doi.org/10.1021/acs.jcim.0c00449

    Article  CAS  Google Scholar 

  210. Cherkasov A et al (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 Central  Google Scholar 

  211. Voet A et al (2014) Pharmacophore modeling: advances, limitations, and current utility in drug discovery. J Receptor Ligand Channel Res:81. https://doi.org/10.2147/JRLCR.S46843

  212. Laurens van der Maaten GH (2008) Visualizing Data using t-SNE. J Mach Learn Res 9:2579–2605

    Google Scholar 

  213. Stojanović L, Popović M, Tijanić N, Rakočević G, Kalinić M (2020) Improved scaffold hopping in ligand-based virtual screening using neural representation learning. J Chem Inf Model 60(10):4629–4639. https://doi.org/10.1021/acs.jcim.0c00622

    Article  CAS  Google Scholar 

  214. Floris M, Sabbadin D, Medda R, Bulfone A, Moro S (2012) Adenosiland: walking through adenosine receptors landscape. Eur J Med Chem 58:248–257. https://doi.org/10.1016/j.ejmech.2012.10.022

    Article  CAS  Google Scholar 

  215. Xu Z, Cheng F, Da C, Liu G, Tang Y (2010) Pharmacophore modeling of human adenosine receptor A2A antagonists. J Mol Model 16(12):1867–1876. https://doi.org/10.1007/s00894-010-0690-z

    Article  CAS  Google Scholar 

  216. Bacilieri M et al (2013) Revisiting a receptor-based pharmacophore hypothesis for human a 2A adenosine receptor antagonists. J Chem Inf Model 53(7):1620–1637. https://doi.org/10.1021/ci300615u

    Article  CAS  Google Scholar 

  217. Tafi A et al (2006) Pharmacophore based receptor modeling: the case of adenosine A3 receptor antagonists. An approach to the optimization of protein models. J Med Chem 49(14):4085–4097. https://doi.org/10.1021/jm051112+

    Article  CAS  Google Scholar 

  218. Samuel AL (1959) Some studies in machine learning using the game of checkers. IBM J Res Dev 3(3):210–229. https://doi.org/10.1147/rd.33.0210

    Article  Google Scholar 

  219. Alzubaidi L et al (2021) Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data 8(1):53. https://doi.org/10.1186/s40537-021-00444-8

    Article  PubMed Central  Google Scholar 

  220. James G, Witten D, Hastie T, Tibshirani R (2013) An introduction to statistical learning, vol 103. Springer, New York. https://doi.org/10.1007/978-1-4614-7138-7

    Book  Google Scholar 

  221. Chicco D, Jurman G (2020) The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics 21(1):6. https://doi.org/10.1186/s12864-019-6413-7

    Article  PubMed Central  Google Scholar 

  222. Hicks SA et al (2022) On evaluation metrics for medical applications of artificial intelligence. Sci Rep 12(1):5979. https://doi.org/10.1038/s41598-022-09954-8

    Article  CAS  PubMed Central  Google Scholar 

  223. Abiodun OI et al (2019) Comprehensive review of artificial neural network applications to pattern recognition. IEEE Access 7:158820–158846. https://doi.org/10.1109/ACCESS.2019.2945545

    Article  Google Scholar 

  224. Qi G-J, Luo J (2022) Small data challenges in big data era: a survey of recent progress on unsupervised and semi-supervised methods. IEEE Trans Pattern Anal Mach Intell 44(4):2168–2187. https://doi.org/10.1109/TPAMI.2020.3031898

    Article  Google Scholar 

  225. Chu X, Ilyas IF, Krishnan S, Wang J (2016) Data cleaning. In: Proceedings of the 2016 international conference on management of data, pp 2201–2206. https://doi.org/10.1145/2882903.2912574

    Chapter  Google Scholar 

  226. Chen J, Si Y-W, Un C-W, Siu SWI (2021) Chemical toxicity prediction based on semi-supervised learning and graph convolutional neural network. J Cheminform 13(1):93. https://doi.org/10.1186/s13321-021-00570-8

    Article  PubMed Central  Google Scholar 

  227. Venkatraman V (2021) FP-ADMET: a compendium of fingerprint-based ADMET prediction models. J Cheminform 13(1):75. https://doi.org/10.1186/s13321-021-00557-5

    Article  PubMed Central  Google Scholar 

  228. Blaschke T et al (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  Google Scholar 

  229. Gupta A, Müller AT, Huisman BJH, Fuchs JA, Schneider P, Schneider G (2018) Generative recurrent networks for de novo drug design. Mol Inform 37(1–2):1700111. https://doi.org/10.1002/minf.201700111

    Article  CAS  Google Scholar 

  230. Schneider G (2018) Automating drug discovery. Nat Rev Drug Discov 17(2):97–113. https://doi.org/10.1038/nrd.2017.232

    Article  CAS  Google Scholar 

  231. Meli R, Morris GM, Biggin PC (2022) Scoring functions for protein-ligand binding affinity prediction using structure-based deep learning: a review. Front Bioinforma 2. https://doi.org/10.3389/fbinf.2022.885983

  232. Noé F, Tkatchenko A, Müller K-R, Clementi C (2020) Machine learning for molecular simulation. Annu Rev Phys Chem 71(1):361–390. https://doi.org/10.1146/annurev-physchem-042018-052331

    Article  CAS  Google Scholar 

  233. Carpenter KA, Huang X (2018) Machine learning-based virtual screening and its applications to Alzheimer’s drug discovery: a review. Curr Pharm Des 24(28):3347–3358. https://doi.org/10.2174/1381612824666180607124038

    Article  CAS  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed Central  Google Scholar 

  235. Goßen J et al (2023) Machine learning-aided discovery of novel chemotype antagonists for G protein-coupled receptors: the case of the adenosine A2A receptor. bioRxiv. https://doi.org/10.1101/2023.03.31.535043

  236. Tang M, Wen C, Lin J, Chen H, Ran T (2023) Discovery of novel A2AR antagonists through deep learning-based virtual screening. Artif Intell Life Sci 3:100058. https://doi.org/10.1016/j.ailsci.2023.100058

    Article  CAS  Google Scholar 

  237. Puhl AC, Gao Z-G, Jacobson KA, Ekins S (2022) Machine learning for discovery of new ADORA modulators. Front Pharmacol 13. https://doi.org/10.3389/fphar.2022.920643

  238. Böselt L, Thürlemann M, Riniker S (2021) Machine learning in QM/MM molecular dynamics simulations of condensed-phase systems. J Chem Theory Comput 17(5):2641–2658. https://doi.org/10.1021/acs.jctc.0c01112

    Article  CAS  Google Scholar 

  239. Pozzan A (2020) QM calculations in ADMET prediction. Quantum Mech Drug Discov:285–305. https://doi.org/10.1007/978-1-0716-0282-9_18

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Stefano Moro .

Editor information

Editors and Affiliations

Ethics declarations

The author declares that they have no conflict of interest.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Ethical Approval

This chapter does not contain any studies with human participants or animals performed by the authors.

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Bassani, D., Moro, S. (2023). In Silico Insights Toward the Exploration of Adenosine Receptors Ligand Recognition. In: Colotta, V., Supuran, C.T. (eds) Purinergic Receptors and their Modulators. Topics in Medicinal Chemistry, vol 41. Springer, Cham. https://doi.org/10.1007/7355_2023_164

Download citation

Publish with us

Policies and ethics