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How to cite this paper
APA: Myrko, I., Chaban, T., Demchuk, Y., Drapak, Y., Chaban, I., Drapak, I., Pankiv, M & Matiychuk, V. (2024). Current trends of chemoinformatics and computer chemistry in drug design: A review. Current Chemistry Letters, 13(1), 151-162.
Chicago/Turabian: Myrko, I., Chaban, T., Demchuk, Y., Drapak, Y., Chaban, I., Drapak, I., Pankiv, M & Matiychuk, V. 2024. "Current trends of chemoinformatics and computer chemistry in drug design: A review." Current Chemistry Letters 13, no. 1 (2024): 151-162.
AMA: Myrko, I., Chaban, T., Demchuk, Y., Drapak, Y., Chaban, I., Drapak, I., Pankiv, M & Matiychuk, V. Current trends of chemoinformatics and computer chemistry in drug design: A review. Current Chemistry Letters. 2024;13(1):151-162.
References
1. Kiriiri G. K., Njogu P. M., Mwangi A. N. (2020) Exploring different approaches to improve the success of drug discovery and development projects: a review. Future J. Pharm. Sci. 6 (1) 1-12.
2. Patil P. S. (2016) Drug discovery and ADMET process: a review. Int. J. Adv. Res. Biol. Sci. 3 (7) 181-192.
3. Deore A. B., Dhumane J. R., Wagh, R., Sonawane R. (2019) The stages of drug discovery and development process. Asian j. pharm. res. dev. 7 (6) 62-67.
4. Hoffer L., Muller C., Roche P., Morelli X. (2018) Chemistry driven hit to lead optimization guided by structure based approaches. Mol. Inform. 37 (9-10) 1800059.
5. Rognan D. (2017) The impact of in silico screening in the discovery of novel and safer drug candidates. Pharmacol. Ther. 175 47-66.
6. da Silva Rocha S. F., Olanda C. G., Fokoue H. H., Sant'Anna C. M. (2019) Virtual screening techniques in drug discovery: review and recent applications. Curr. Top. Med. Chem. 19 (19) 1751-1767.
7. Shaker B., Ahmad S., Lee J., Jung C., Na D. (2021). In silico methods and tools for drug discovery. Computers in biology and medicine, 137, 104851.
8. Lin X., Li X., Lin X. (2020) A review on applications of computational methods in drug screening and design. Molecules, 25 (6) 1375.
9. Yusof I., Segall M. D. (2013) Considering the impact drug-like properties have on the chance of success. Drug Discov. Today. 18 (13-14) 659-666.
10. Chaban T., Matiychuk V., Mahlovanyy A., Chaban I., Ogurtsov V. and Leluykh M. (2020) Synthesis and biological evaluation of 1-(3-chloro-2-oxo-4-phenylazetidin-1-yl)-3-(2-oxo-2-(10H-phenothiazin-10-yl)ethyl)urea derivatives. Biointerface Res. Appl. Chem. 10 (4) 5944–5950.
11. Сhulovska Z., Drapak I., Chaban T., Ogurtsov V., Matiychuk V. (2021) Synthesis, anticancer and antioxidant properties of some 4-thioxo-thiazolidin-2-ones. Eur. Chem. Bull. 10 (4) 147-154.
12. Chaban T., Matiychuk V., Ogurtsov V., Chaban I. and Nektegayev I. (2020) Development of effective anti-inflammatory drug candidates among novel thiazolopyridines. Ukr. Biochem. J. 92 (2) 132–139.
13. Rydchuk P., Tymoshuk O., Oleksiv L., Chaban T. and Matiychuk V. (2019) Voltammetric Determination of Pt(IV) using 5-Hydroxyimino-4- imino-1,3-thiazolidine-2-one. Methods Objects Chem. Anal. 14 (3) 130–139.
14. Chaban T. Matiichuk Y., Horishny V., Chaban I. and Matiychuk V. (2020) Synthesis and anticancer activity of 2-aryl-3-methylbenzofuro[3,2-b]pyrazolo[4,3-e]azepine-4,11(2H,10H)-dione and 2-aryl-3,7,9-trimethylpyrido[3',2':4,5]thieno[3,2-b]pyrazolo[4,3-e]azepine-4,11(2H,10H)-diones. Russ. J. Org. Chem. 56 (5) 813–818.
15. Drapak I., Foliush V., Chaban T. and Matiychuk V. (2020) Synthesis antimicrobial and antitumor activities of 2-[5-(2-R-benzyl)thiazol-2-ylimino]thiazolidin-4-ones. Biointerface Res. Appl. Chem. 10 (3) 5507–5511.
16. Сhulovska Z., Chaban T., Drapak I., Chaban I. and Nektegaev I. (2021) Synthesis of some С5 substituted 4-phenylimino-thiazolidin-2-ones as possible anti-inflammatory agents. Biointerface Res. Appl. Chem. 11 (1) 8009–8017.
17. Lelyukh M., Adamchuk S., Harkov S., Demchuk I., Chaban I., Shelepeten L., Chaban T. (2018) Synthetic approaches, chemical modification and biological activity of non-condensed 1,3,4-thiadiazole derivatives: A review. Pharmacia 65 (4) 72–88.
18. Cannon J. G. (2011) Book Review of A History of Medicinal Chemistry. ACS Publications. American Chemical Society.
19. Bosch F., Rosich L. (2008) The contributions of Paul Ehrlich to pharmacology: a tribute on the occasion of the centenary of his Nobel Prize. Pharmacology 82 (3) 171-179.
20. Maehle A. H. (2009) A binding question: the evolution of the receptor concept. Endeavour 33 (4) 135-140.
21. Christensen S. B. (2021) Drugs that changed society: History and current status of the early antibiotics: Salvarsan, sulfonamides, and β-lactams.Molecules 26 (19) 6057.
22. Doytchinova I. (2022) Drug design—past, present, future. Molecules 27 (5) 1496.
23. Sriram K., Insel, P. A. (2018) G protein-coupled receptors as targets for approved drugs: how many targets and how many drugs? Mol. Pharmacol. 93 (4) 251-258.
24. Cavasotto C. N., Di Filippo J. I. (2021) Artificial intelligence in the early stages of drug discovery. Arch. Biochem. Biophys. 698 108730.
25. Tang Y., Zhu W., Chen K., Jiang H. (2006) New technologies in computer-aided drug design: Toward target identification and new chemical entity discovery. Drug Discov. Today Technol. 3 (3) 307-313.
26. Garibsingh, R. A. A., & Schlessinger, A. (2019) Advances and challenges in rational drug design for SLCs. Trends Pharmacol. Sci. 40 (10) 790-800.
27. Fogel D. B. (2018) Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: a review. Contemp. Clin. Trials Commun. 11, 156-164.
28. Ekins S., Mestres J., Testa B. (2007) In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling. Br. J. Pharmacol. 152 (1) 9-20.
29. Noori H. R., Spanagel R. (2013) In silico pharmacology: drug design and discovery’s gate to the future. In Silico Pharmacol.1 1-2.
30. Yu W., MacKerell A. D. (2017) Computer-aided drug design methods. Antibiotics: methods and protocols 85-106.
31. Ece A. (2023) Computer-aided drug design. BMC Chem. Biol. 17 (1) 26.
32. Acharya C., Coop A., Polli J., D MacKerell A. (2011) Recent advances in ligand-based drug design: relevance and utility of the conformationally sampled pharmacophore approach. Curr Comput Aided Drug Des. 7 (1) 10-22.
33. Hefti F. F. (2008) Requirements for a lead compound to become a clinical candidate. BMC neuroscience, 9 (3) 1-7.
34. Anderson A. C. (2012) Structure-based functional design of drugs: from target to lead compound. Methods Mol Biol. 359-366.
35. Rienzo M., Jackson S. J., Chao L. K., Leaf T., Schmidt T. J., Navidi A. H., Leavell M. D. (2021) High-throughput screening for high-efficiency small-molecule biosynthesis. Metab. Eng. 63 102-125.
36. Zhu T., Cao S., Su P. C., Patel R., Shah D., Chokshi H. B., Hevener K. E. (2013) Hit identification and optimization in virtual screening: Practical recommendations based on a critical literature analysis: Miniperspective. J. Med. Chem. 56 (17) 6560-6572.
37. Glökler J., Schütze T., Konthur Z. (2010) Automation in the high-throughput selection of random combinatorial libraries—Different approaches for select applications. Molecules, 15 (4) 2478-2490.
38. Forli S. (2015) Charting a path to success in virtual screening. Molecules, 20 (10), 18732-18758.
39. Petit J., Meurice N., Kaiser C., Maggiora G. (2012) Softening the rule of five—where to draw the line? Bioorganic Med. Chem. 20 (18) 5343-5351.
40. van Deursen R., Blum L. C., Reymond J. L. (2011) Visualisation of the chemical space of fragments, lead-like and drug-like molecules in PubChem. J. Comput. Aided Mol. Des. 25 649-662.
41. Ghose A., Viswanadhan V., Wendoloski J. (1999) A Knowledge-Based Approach in Designing Combinatorial or Medicinal Chemistry Libraries for Drug Discovery. 1. A Qualitative and Quantitative Characterization of Known Drug Databases. J. Comb. Chem. 1(1) 55–68.
42. Muegge I., Heald S., Brittelli D. (2001) Simple Selection Criteria for Drug-like Chemical Matter. J. Med. Chem. 44 (12) 1841–1846.
43. Egan W., Merz K., Baldwin J. (2000) Prediction of Drug Absorption Using Multivariate Statistics, J. Med. Chem., 43 (21) 3867–3877.
44. Pinzi L., Rastelli G. (2019) Molecular docking: shifting paradigms in drug discovery. Int. J. Mol. Sci. 20 (18) 4331.
45. Stanzione F., Giangreco I., Cole J. C. (2021) Use of molecular docking computational tools in drug discovery. Prog. Med. Chem. 60 273-343.
46. Seidel T., Schuetz D. A., Garon A., Langer T. (2019) The pharmacophore concept and its applications in computer-aided drug design. Prog. Chem. Org. Nat. Prod. 99-141.
47. Guryanov I., Fiorucci S.,Tennikova T. (2016) .Receptor-ligand interactions: Advanced biomedical applications. Mater. Sci. Eng. 68 (1) 890-903.
48. Muegge I., Mukherjee P. (2016) An overview of molecular fingerprint similarity search in virtual screening. Expert Opin Drug Discov.11 (2) 137-148.
49. Halder A. K., Moura A. S., Cordeiro M. N. D. (2018) QSAR modelling: A therapeutic patent review 2010-present. Expert Opin Ther Pat .28 (6) 467-476.
50. De P., Kar S., Ambure P., Roy K. (2022) Prediction reliability of QSAR models: an overview of various validation tools. Arch. Toxicol. 96 (5) 1279-1295.
51. Lambrinidis G., Tsantili-Kakoulidou A. (2018) Challenges with multi-objective QSAR in drug discovery. Expert Opin Drug Discov. 13 (9) 851-859.
52. Domingo L., Gutiérrez M., Pérez P. (2016) Applications of the Conceptual Density Functional Theory Indices to Organic Chemistry Reactivity. Molecules. 21 (6) 748.
53. Sadowski M., Utnicka J., Wójtowicz A., Kula K. (2023) The global and local Reactivity of C,N-diarylnitryle imines in [3+2]cycloaddition processes with trans-β-nitrostyrene according to Molecular Electron Density Theory: A computational study. Curr. Chem. Lett. 12 (2) 421-430.
54. Kula K., Zawadzińska K. (2021) Local nucleophile-electrophile interactions in [3+2]cycloaddition reactions between benzonitrile N-oxide and selected conjugated nitroalkenes in the light of MEDT computational study. Curr. Chem. Lett. 10 (1) 9-16.
55. Vijayaraj R., Subramanian V., Chattaraj P. (2009) Comparison of Global Reactivity Descriptors Calculated Using Various Density Functionals: A QSAR Perspective. J. Chem. Theory. Comput. 5 (10) 2744–2753.
56. Kaya S., Putz M. (2022) Atoms-In-Molecules' Faces of Chemical Hardness by Conceptual Density Functional Theory. Molecules. 27 (24) 8825.
57. Rademacher C., Seeberger P. H. (2016) High-throughput synthesis of diverse compound collections for lead discovery and optimization. New Approaches to Drug Discovery 73-89.
58. Meanwell N. A. (2023) Applications of Bioisosteres in the Design of Biologically Active Compounds J Agric Food Chem. in press.
59. Perez J. J. (2018) Designing peptidomimetics. Curr. Top. Med. Chem. 18 (7), 566-590.
1. Kiriiri G. K., Njogu P. M., Mwangi A. N. (2020) Exploring different approaches to improve the success of drug discovery and development projects: a review. Future J. Pharm. Sci. 6 (1) 1-12.
2. Patil P. S. (2016) Drug discovery and ADMET process: a review. Int. J. Adv. Res. Biol. Sci. 3 (7) 181-192.
3. Deore A. B., Dhumane J. R., Wagh, R., Sonawane R. (2019) The stages of drug discovery and development process. Asian j. pharm. res. dev. 7 (6) 62-67.
4. Hoffer L., Muller C., Roche P., Morelli X. (2018) Chemistry driven hit to lead optimization guided by structure based approaches. Mol. Inform. 37 (9-10) 1800059.
5. Rognan D. (2017) The impact of in silico screening in the discovery of novel and safer drug candidates. Pharmacol. Ther. 175 47-66.
6. da Silva Rocha S. F., Olanda C. G., Fokoue H. H., Sant'Anna C. M. (2019) Virtual screening techniques in drug discovery: review and recent applications. Curr. Top. Med. Chem. 19 (19) 1751-1767.
7. Shaker B., Ahmad S., Lee J., Jung C., Na D. (2021). In silico methods and tools for drug discovery. Computers in biology and medicine, 137, 104851.
8. Lin X., Li X., Lin X. (2020) A review on applications of computational methods in drug screening and design. Molecules, 25 (6) 1375.
9. Yusof I., Segall M. D. (2013) Considering the impact drug-like properties have on the chance of success. Drug Discov. Today. 18 (13-14) 659-666.
10. Chaban T., Matiychuk V., Mahlovanyy A., Chaban I., Ogurtsov V. and Leluykh M. (2020) Synthesis and biological evaluation of 1-(3-chloro-2-oxo-4-phenylazetidin-1-yl)-3-(2-oxo-2-(10H-phenothiazin-10-yl)ethyl)urea derivatives. Biointerface Res. Appl. Chem. 10 (4) 5944–5950.
11. Сhulovska Z., Drapak I., Chaban T., Ogurtsov V., Matiychuk V. (2021) Synthesis, anticancer and antioxidant properties of some 4-thioxo-thiazolidin-2-ones. Eur. Chem. Bull. 10 (4) 147-154.
12. Chaban T., Matiychuk V., Ogurtsov V., Chaban I. and Nektegayev I. (2020) Development of effective anti-inflammatory drug candidates among novel thiazolopyridines. Ukr. Biochem. J. 92 (2) 132–139.
13. Rydchuk P., Tymoshuk O., Oleksiv L., Chaban T. and Matiychuk V. (2019) Voltammetric Determination of Pt(IV) using 5-Hydroxyimino-4- imino-1,3-thiazolidine-2-one. Methods Objects Chem. Anal. 14 (3) 130–139.
14. Chaban T. Matiichuk Y., Horishny V., Chaban I. and Matiychuk V. (2020) Synthesis and anticancer activity of 2-aryl-3-methylbenzofuro[3,2-b]pyrazolo[4,3-e]azepine-4,11(2H,10H)-dione and 2-aryl-3,7,9-trimethylpyrido[3',2':4,5]thieno[3,2-b]pyrazolo[4,3-e]azepine-4,11(2H,10H)-diones. Russ. J. Org. Chem. 56 (5) 813–818.
15. Drapak I., Foliush V., Chaban T. and Matiychuk V. (2020) Synthesis antimicrobial and antitumor activities of 2-[5-(2-R-benzyl)thiazol-2-ylimino]thiazolidin-4-ones. Biointerface Res. Appl. Chem. 10 (3) 5507–5511.
16. Сhulovska Z., Chaban T., Drapak I., Chaban I. and Nektegaev I. (2021) Synthesis of some С5 substituted 4-phenylimino-thiazolidin-2-ones as possible anti-inflammatory agents. Biointerface Res. Appl. Chem. 11 (1) 8009–8017.
17. Lelyukh M., Adamchuk S., Harkov S., Demchuk I., Chaban I., Shelepeten L., Chaban T. (2018) Synthetic approaches, chemical modification and biological activity of non-condensed 1,3,4-thiadiazole derivatives: A review. Pharmacia 65 (4) 72–88.
18. Cannon J. G. (2011) Book Review of A History of Medicinal Chemistry. ACS Publications. American Chemical Society.
19. Bosch F., Rosich L. (2008) The contributions of Paul Ehrlich to pharmacology: a tribute on the occasion of the centenary of his Nobel Prize. Pharmacology 82 (3) 171-179.
20. Maehle A. H. (2009) A binding question: the evolution of the receptor concept. Endeavour 33 (4) 135-140.
21. Christensen S. B. (2021) Drugs that changed society: History and current status of the early antibiotics: Salvarsan, sulfonamides, and β-lactams.Molecules 26 (19) 6057.
22. Doytchinova I. (2022) Drug design—past, present, future. Molecules 27 (5) 1496.
23. Sriram K., Insel, P. A. (2018) G protein-coupled receptors as targets for approved drugs: how many targets and how many drugs? Mol. Pharmacol. 93 (4) 251-258.
24. Cavasotto C. N., Di Filippo J. I. (2021) Artificial intelligence in the early stages of drug discovery. Arch. Biochem. Biophys. 698 108730.
25. Tang Y., Zhu W., Chen K., Jiang H. (2006) New technologies in computer-aided drug design: Toward target identification and new chemical entity discovery. Drug Discov. Today Technol. 3 (3) 307-313.
26. Garibsingh, R. A. A., & Schlessinger, A. (2019) Advances and challenges in rational drug design for SLCs. Trends Pharmacol. Sci. 40 (10) 790-800.
27. Fogel D. B. (2018) Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: a review. Contemp. Clin. Trials Commun. 11, 156-164.
28. Ekins S., Mestres J., Testa B. (2007) In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling. Br. J. Pharmacol. 152 (1) 9-20.
29. Noori H. R., Spanagel R. (2013) In silico pharmacology: drug design and discovery’s gate to the future. In Silico Pharmacol.1 1-2.
30. Yu W., MacKerell A. D. (2017) Computer-aided drug design methods. Antibiotics: methods and protocols 85-106.
31. Ece A. (2023) Computer-aided drug design. BMC Chem. Biol. 17 (1) 26.
32. Acharya C., Coop A., Polli J., D MacKerell A. (2011) Recent advances in ligand-based drug design: relevance and utility of the conformationally sampled pharmacophore approach. Curr Comput Aided Drug Des. 7 (1) 10-22.
33. Hefti F. F. (2008) Requirements for a lead compound to become a clinical candidate. BMC neuroscience, 9 (3) 1-7.
34. Anderson A. C. (2012) Structure-based functional design of drugs: from target to lead compound. Methods Mol Biol. 359-366.
35. Rienzo M., Jackson S. J., Chao L. K., Leaf T., Schmidt T. J., Navidi A. H., Leavell M. D. (2021) High-throughput screening for high-efficiency small-molecule biosynthesis. Metab. Eng. 63 102-125.
36. Zhu T., Cao S., Su P. C., Patel R., Shah D., Chokshi H. B., Hevener K. E. (2013) Hit identification and optimization in virtual screening: Practical recommendations based on a critical literature analysis: Miniperspective. J. Med. Chem. 56 (17) 6560-6572.
37. Glökler J., Schütze T., Konthur Z. (2010) Automation in the high-throughput selection of random combinatorial libraries—Different approaches for select applications. Molecules, 15 (4) 2478-2490.
38. Forli S. (2015) Charting a path to success in virtual screening. Molecules, 20 (10), 18732-18758.
39. Petit J., Meurice N., Kaiser C., Maggiora G. (2012) Softening the rule of five—where to draw the line? Bioorganic Med. Chem. 20 (18) 5343-5351.
40. van Deursen R., Blum L. C., Reymond J. L. (2011) Visualisation of the chemical space of fragments, lead-like and drug-like molecules in PubChem. J. Comput. Aided Mol. Des. 25 649-662.
41. Ghose A., Viswanadhan V., Wendoloski J. (1999) A Knowledge-Based Approach in Designing Combinatorial or Medicinal Chemistry Libraries for Drug Discovery. 1. A Qualitative and Quantitative Characterization of Known Drug Databases. J. Comb. Chem. 1(1) 55–68.
42. Muegge I., Heald S., Brittelli D. (2001) Simple Selection Criteria for Drug-like Chemical Matter. J. Med. Chem. 44 (12) 1841–1846.
43. Egan W., Merz K., Baldwin J. (2000) Prediction of Drug Absorption Using Multivariate Statistics, J. Med. Chem., 43 (21) 3867–3877.
44. Pinzi L., Rastelli G. (2019) Molecular docking: shifting paradigms in drug discovery. Int. J. Mol. Sci. 20 (18) 4331.
45. Stanzione F., Giangreco I., Cole J. C. (2021) Use of molecular docking computational tools in drug discovery. Prog. Med. Chem. 60 273-343.
46. Seidel T., Schuetz D. A., Garon A., Langer T. (2019) The pharmacophore concept and its applications in computer-aided drug design. Prog. Chem. Org. Nat. Prod. 99-141.
47. Guryanov I., Fiorucci S.,Tennikova T. (2016) .Receptor-ligand interactions: Advanced biomedical applications. Mater. Sci. Eng. 68 (1) 890-903.
48. Muegge I., Mukherjee P. (2016) An overview of molecular fingerprint similarity search in virtual screening. Expert Opin Drug Discov.11 (2) 137-148.
49. Halder A. K., Moura A. S., Cordeiro M. N. D. (2018) QSAR modelling: A therapeutic patent review 2010-present. Expert Opin Ther Pat .28 (6) 467-476.
50. De P., Kar S., Ambure P., Roy K. (2022) Prediction reliability of QSAR models: an overview of various validation tools. Arch. Toxicol. 96 (5) 1279-1295.
51. Lambrinidis G., Tsantili-Kakoulidou A. (2018) Challenges with multi-objective QSAR in drug discovery. Expert Opin Drug Discov. 13 (9) 851-859.
52. Domingo L., Gutiérrez M., Pérez P. (2016) Applications of the Conceptual Density Functional Theory Indices to Organic Chemistry Reactivity. Molecules. 21 (6) 748.
53. Sadowski M., Utnicka J., Wójtowicz A., Kula K. (2023) The global and local Reactivity of C,N-diarylnitryle imines in [3+2]cycloaddition processes with trans-β-nitrostyrene according to Molecular Electron Density Theory: A computational study. Curr. Chem. Lett. 12 (2) 421-430.
54. Kula K., Zawadzińska K. (2021) Local nucleophile-electrophile interactions in [3+2]cycloaddition reactions between benzonitrile N-oxide and selected conjugated nitroalkenes in the light of MEDT computational study. Curr. Chem. Lett. 10 (1) 9-16.
55. Vijayaraj R., Subramanian V., Chattaraj P. (2009) Comparison of Global Reactivity Descriptors Calculated Using Various Density Functionals: A QSAR Perspective. J. Chem. Theory. Comput. 5 (10) 2744–2753.
56. Kaya S., Putz M. (2022) Atoms-In-Molecules' Faces of Chemical Hardness by Conceptual Density Functional Theory. Molecules. 27 (24) 8825.
57. Rademacher C., Seeberger P. H. (2016) High-throughput synthesis of diverse compound collections for lead discovery and optimization. New Approaches to Drug Discovery 73-89.
58. Meanwell N. A. (2023) Applications of Bioisosteres in the Design of Biologically Active Compounds J Agric Food Chem. in press.
59. Perez J. J. (2018) Designing peptidomimetics. Curr. Top. Med. Chem. 18 (7), 566-590.