Skip to main content
Log in

Chemical similarity of molecules with physiological response

  • Original Article
  • Published:
Molecular Diversity Aims and scope Submit manuscript

Abstract

Measuring the similarity among molecules is an important task in various chemically oriented problems. This elusive concept is hard to define and quantify. Moreover, the complexity of the problem is elevated by bifurcating the notion of molecular similarity to structural and chemical similarity. While the structural similarity of molecules is being extensively researched, the so-called chemical similarity is being mentioned scarcely. Here, we propose a way of converting the physico-chemical properties into molecular fingerprints. Then, using the apparatus of measuring the structural similarity, the chemical similarity can be assessed. The proof of a concept is demonstrated on a set of molecules that induce diverse physiological responses.

Graphical abstract

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data availability

The data used in this study are available from the corresponding author upon request.

References

  1. Tversky A (1977) Features of similarity. Psychol Rev 84:327–352. https://doi.org/10.1037/0033-295X.84.4.327

    Article  Google Scholar 

  2. Bender A, Glen RC (2004) Molecular similarity: a key technique in molecular informatics. Org Biomol Chem 2:3204–3218. https://doi.org/10.1039/B409813G

    Article  CAS  PubMed  Google Scholar 

  3. Maldonado AG, Doucet JP, Petitjean M, Fan B-T (2006) Molecular similarity and diversity in chemoinformatics: from theory to applications. Mol Divers 10:39–79. https://doi.org/10.1007/s11030-006-8697-1

    Article  CAS  PubMed  Google Scholar 

  4. Dean PM (1995) Defining molecular similarity and complementarity for drug design. In: Dean PM (ed) Molecular similarity in drug design. Springer, Dordrecht, pp 1–23. https://doi.org/10.1007/978-94-011-1350-2_1

    Chapter  Google Scholar 

  5. Coley CW, Rogers L, Green WH, Jensen KF (2017) Computer-assisted retrosynthesis based on molecular similarity. ACS Cent Sci 3:1237–1245. https://doi.org/10.1021/acscentsci.7b00355

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Liu Y, Cao Y, Lai W, Yu T, Ma Y, Ge Z (2021) A strategy for predicting the crystal structure of energetic N-oxides based on molecular similarity and electrostatic matching. CrystEngComm 23:714–723. https://doi.org/10.1039/D0CE01501F

    Article  CAS  Google Scholar 

  7. Krasowski MD, Pizon AF, Siam MG, Giannoutsos S, Iyer M, Ekins S (2009) Using molecular similarity to highlight the challenges of routine immunoassay-based drug of abuse/toxicology screening in emergency medicine. BMC Emerg Med 9:5. https://doi.org/10.1186/1471-227X-9-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Krasowski MD, Drees D, Morris CS, Maakestad J, Blau JL, Ekins S (2014) Cross-reactivity of steroid hormone immunoassays: clinical significance and two-dimensional molecular similarity prediction. BMC Clin Pathol 14:13. https://doi.org/10.1186/1472-6890-14-33

    Article  CAS  Google Scholar 

  9. Martin RL, Willems TF, Lin L-C, Kim J, Swisher JA, Smit B, Haranczyk M (2012) Similarity-driven discovery of zeolite materials for adsorption-based separations. ChemPhysChem 13:3595–3597. https://doi.org/10.1002/cphc.201200554

    Article  CAS  PubMed  Google Scholar 

  10. Rouvray D (1990) The evolution of the concept of molecular similarity. In: Johnson MA, Maggiora GM (eds) Concepts and applications of molecular similarity. Wile, New York. ISBN: 978-0-471-62175-1

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

    Article  CAS  PubMed  Google Scholar 

  12. Guha R, Van Drie JH (2008) Structure−activity landscape index: identifying and quantifying activity cliffs. J Chem Inf Model 48:646–658. https://doi.org/10.1021/ci7004093

    Article  CAS  PubMed  Google Scholar 

  13. Stumpfe D, Bajorath J (2012) Exploring activity cliffs in medicinal chemistry: miniperspective. J Chem Inf Model 55:2932–2942. https://doi.org/10.1021/jm201706b

    Article  CAS  Google Scholar 

  14. Medina-Franco JL (2013) Activity cliffs: facts or artifacts? Chem Biol Drug Des 81:553–556. https://doi.org/10.1111/cbdd.12115

    Article  CAS  PubMed  Google Scholar 

  15. Xue L, Bajorath J (2000) Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening. Comb Chem High Throughput Screen 3:363–372. https://doi.org/10.2174/1386207003331454

    Article  CAS  PubMed  Google Scholar 

  16. Todeschini R, Consonni V (2000) Handbook of molecular descriptors. Wiley, Weinheim. https://doi.org/10.1002/9783527613106

    Book  Google Scholar 

  17. Engel T, Gasteiger J (2018) Applied chemoinformatics: achievements and future opportunities. Wiley, Weinheim

    Book  Google Scholar 

  18. Karelson M, Lobanov VS, Katritzky AR (1996) Quantum-chemical descriptors in QSAR/QSPR studies. Chem Rev 96:1027–1044. https://doi.org/10.1021/cr950202r

    Article  CAS  PubMed  Google Scholar 

  19. Dearden JC, Cronin MTD, Kaiser KLE (2009) How not to develop a quantitative structure–activity or structure–property relationship (QSAR/QSPR). SAR QSAR Environ Res 20:241–266. https://doi.org/10.1080/10629360902949567

    Article  CAS  PubMed  Google Scholar 

  20. Willett P (2011) Similarity searching using 2D structural fingerprints. In: Bajorath J (ed) Chemoinformatics and computational chemical biology. Humana, Totowa, pp 133–158. https://doi.org/10.1007/978-1-60761-839-3_5

    Chapter  Google Scholar 

  21. O’Boyle NM, Sayle RA (2016) Comparing structural fingerprints using a literature-based similarity benchmark. J Cheminform 8:6. https://doi.org/10.1186/s13321-016-0148-0

    Article  CAS  Google Scholar 

  22. Deng Z, Chuaqui C, Singh J (2004) Structural interaction fingerprint (SIFt): a novel method for analyzing three-dimensional protein−ligand binding interactions. J Med Chem 47:337–344. https://doi.org/10.1021/jm030331x

    Article  CAS  PubMed  Google Scholar 

  23. Rácz A, Bajusz D, Héberger K (2018) Life beyond the Tanimoto coefficient: similarity measures for interaction fingerprints. J Cheminform 10:48. https://doi.org/10.1186/s13321-018-0302-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Rogers D, Hahn M (2010) Extended-connectivity fingerprints. J Chem Inf Model 50:742–754. https://doi.org/10.1021/ci100050t

    Article  CAS  PubMed  Google Scholar 

  25. Kubinyi H (1998) Similarity and dissimilarity: a medicinal chemist’s view. Perspect Drug Discov Des 9:225–252. https://doi.org/10.1023/A:1027221424359

    Article  Google Scholar 

  26. Martin YC, Kofron JL, Traphagen LM (2002) Do structurally similar molecules have similar biological activity? J Med Chem 45:4350–4358. https://doi.org/10.1021/jm020155c

    Article  CAS  PubMed  Google Scholar 

  27. Boström J, Hogner A, Schmitt S (2006) Do structurally similar ligands bind in a similar fashion? J Med Chem 49:6716–6725. https://doi.org/10.1021/jm060167o

    Article  CAS  PubMed  Google Scholar 

  28. Xenides D, Fostiropoulou D, Vlachos DS (2020) A metric space approach on the molecular vs. chemical similarity of some analgesic and euphoric compounds. MATCH Commun Math Comput Chem 83:261–284

    Google Scholar 

  29. Kaiko RF, Kanner R, Foley KM, Wallenstein SL, Canel AM, Rogers AG, Houde RW (1987) Cocaine and morphine interaction in acute and chronic cancer pain. Pain 31:35–45. https://doi.org/10.1016/0304-3959(87)90004-2

    Article  PubMed  Google Scholar 

  30. Van Soeren M, Mohr T, Kjaer M, Graham TE (1996) Acute effects of caffeine ingestion at rest in humans with impaired epinephrine responses. J Appl Physiol 80:999–1005. https://doi.org/10.1152/jappl.1996.80.3.999

    Article  PubMed  Google Scholar 

  31. Parrott AC (2015) Why all stimulant drugs are damaging to recreational users: an empirical overview and psychobiological explanation. Hum Psychopharmacol 30:213–224. https://doi.org/10.1002/hup.2468

    Article  PubMed  Google Scholar 

  32. Graziane NM, Sun S, Wright WJ, Jang D, Liu Z, Huang YH, Nestler EJ, Wang YT, Schlüter OM, Dong Y (2016) Opposing mechanisms mediate morphine-and cocaine-induced generation of silent synapses. Nat Neurosci 19:915–925. https://doi.org/10.1038/nn.4313

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. PubChem, https://pubchem.ncbi.nlm.nih.gov/, Accessed 10 Dec 2021

  34. DrugBank, https://go.drugbank.com/, Accessed 10 Dec 2021

  35. 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 52:2884–2901. https://doi.org/10.1021/ci300261r

    Article  CAS  PubMed  Google Scholar 

  36. Jaccard P (1912) The distribution of the flora in the alpine zone. New Phytol 11:37–50. https://doi.org/10.1111/j.1469-8137.1912.tb05611.x

    Article  Google Scholar 

  37. Rogers DJ, Tanimoto TT (1960) A computer program for classifying plants. Science 132:1115–1118. https://doi.org/10.1126/science.132.3434.1115

    Article  CAS  PubMed  Google Scholar 

  38. Gleason HA (1920) Some applications of the quadrat method. Bull Torrey Bot Club 47:21–33. https://doi.org/10.2307/2480223

    Article  Google Scholar 

  39. Sokal RR, Sneath PHA (1963) Principles of numerical taxonomy. W. H. Freeman and Co., London

    Google Scholar 

  40. Consonni V, Todeschini R (2012) New similarity coefficients for binary data. MATCH Commun Math Comput Chem 68:581–592

    CAS  Google Scholar 

  41. RDKit: Open-source cheminformatics, http://www.rdkit.org.

  42. Miranda-Quintana Alain R, Bajusz D, Rácz A, Héberger K (2021) Extended similarity indices: the benefits of comparing more than two objects simultaneously. Part 1: theory and characteristics. J Cheminform 13:32. https://doi.org/10.1186/s13321-021-00505-3

    Article  Google Scholar 

  43. Miranda-Quintana Alain R, Bajusz D, Rácz A, Héberger K (2021) Extended similarity indices: the benefits of comparing more than two objects simultaneously. Part 2: speed, consistency, diversity selection. J Cheminform 13:33. https://doi.org/10.1186/s13321-021-00504-4

    Article  Google Scholar 

  44. Héberger K (2010) Sum of ranking differences compares methods or models fairly. Trends Anal Chem 29:101–109. https://doi.org/10.1016/j.trac.2009.09.009

    Article  CAS  Google Scholar 

  45. Rácz A, Bajusz D, Héberger K (2015) Consistency of QSAR models: Correct split of training and test sets, ranking of models and performance parameters. SAR QSAR Environ Res 26:683–700. https://doi.org/10.1080/1062936X.2015.1084647

    Article  CAS  PubMed  Google Scholar 

  46. West C, Khalikova MA, Lesellier E, Héberger K (2015) Sum of ranking differences to rank stationary phases used in packed column supercritical fluid chromatography. J Chromatogr A 1409:241–250. https://doi.org/10.1016/j.chroma.2015.07.071

    Article  CAS  PubMed  Google Scholar 

  47. Vastag G, Apostolov S, Perišić-Janjić N, Matijević B (2013) Multivariate analysis of chromatographic retention data and lipophilicity of phenylacetamide derivatives. Anal Chim Acta 767:44–49. https://doi.org/10.1016/j.aca.2013.01.002

    Article  CAS  PubMed  Google Scholar 

  48. Héberger K, Kollár-Hunek K (2011) Sum of ranking differences for method discrimination and its validation: comparison of ranks with random numbers. J Chemom 25:151–158. https://doi.org/10.1002/cem.1320

    Article  CAS  Google Scholar 

  49. Moreira de Barros GA, Baradelli R, Rodrigues DG, Toffoletto O, Domingues FS, Gayoso MV, Lopes A, Afiune JB, Guimarães GMN (2021) Use of methadone as an alternative to morphine for chronic pain management: a noninferiority retrospective observational study. PAIN Rep 6:e979. https://doi.org/10.1097/PR9.0000000000000979

    Article  PubMed  PubMed Central  Google Scholar 

  50. Goldsack C, Scuplak SM, Smith M (1996) A double-blind comparison of codeine and morphine for postoperative analgesia following intracranial surgery. Anaesthesia 51:1029–1032. https://doi.org/10.1111/j.1365-2044.1996.tb14997.x

    Article  CAS  PubMed  Google Scholar 

  51. Dixon WE, Hoyle JC (1929) Studies in the pulmonary circulation: II. The action of adrenaline and nicotine. J Physiol 67:77–86. https://doi.org/10.1113/jphysiol.1929.sp002554

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was supported by the Serbian Ministry of Education, Science and Technological Development (Grant No. 451-03-68/2022-14/200122).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Izudin Redžepović.

Ethics declarations

Conflict of interest

The authors declare no conflicts of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 343 kb)

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Redžepović, I., Furtula, B. Chemical similarity of molecules with physiological response. Mol Divers 27, 1603–1612 (2023). https://doi.org/10.1007/s11030-022-10514-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11030-022-10514-5

Keywords

Navigation