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Virtual Screening Process: A Guide in Modern Drug Designing

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Computational Drug Discovery and Design

Abstract

Due to its capacity to drastically cut the cost and time necessary for experimental screening of compounds, virtual screening (VS) has grown to be a crucial component of drug discovery and development. VS is a computational method used in drug design to identify potential drugs from enormous libraries of chemicals. This approach makes use of molecular modeling and docking simulations to assess the small molecule’s ability to bind to the desired protein. Virtual screening has a bright future, as high computational power and modern techniques are likely to further enhance the accuracy and speed of the process.

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References

  1. Walters WP, Wang R (2020) New trends in virtual screening. J Chem Inf Model 60:4109–4111

    Article  CAS  PubMed  Google Scholar 

  2. Gorgulla C, Boeszoermenyi A, Wang ZF, Fischer PD, Coote PW, Padmanabha Das KM, Malets YS, Radchenko DS, Moroz YS, Scott DA, Fackeldey K (2020) An open-source drug discovery platform enables ultra-large virtual screens. Nature 580:663–668

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Lavecchia A, Di Giovanni C (2013) Virtual screening strategies in drug discovery: a critical review. Curr Med Chem 20:2839–2860

    Article  CAS  PubMed  Google Scholar 

  4. Grebner C, Malmerberg E, Shewmaker A, Batista J, Nicholls A, Sadowski J (2019) Virtual screening in the cloud: how big is big enough? J Chem Inf Model 60:4274–4282

    Article  PubMed  Google Scholar 

  5. Macarron R, Banks MN, Bojanic D, Burns DJ, Cirovic DA, Garyantes T, Green DV, Hertzberg RP, Janzen WP, Paslay JW, Schopfer U (2011) Impact of high-throughput screening in biomedical research. Nat Rev Drug Discov 10:188–195

    Article  CAS  PubMed  Google Scholar 

  6. Doytchinova I (2022) Drug design – past, present, future. Molecules 27:1496

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Kar S, Roy K (2013) How far can virtual screening take us in drug discovery? Expert Opin Drug Discov 8:245–261

    Article  CAS  PubMed  Google Scholar 

  8. Gimeno A, Ojeda-Montes MJ, Tomás-Hernández S, Cereto-Massagué A, Beltrán-Debón R, Mulero M, Pujadas G, Garcia-Vallvé S (2019) The light and dark sides of virtual screening: what is there to know? Int J Mol Sci 20:1375

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Berry M, Fielding B, Gamieldien J (2015) Practical considerations in virtual screening and molecular docking. In: Emerging trends in computational biology, bioinformatics, and Systems biology. Elsevier, p 487

    Chapter  Google Scholar 

  10. De Vita S, Lauro G, Ruggiero D, Terracciano S, Riccio R, Bifulco G (2019) Protein preparation automatic protocol for high-throughput inverse virtual screening: accelerating the target identification by computational methods. J Chem Inf Model 59:4678–4690

    Article  PubMed  Google Scholar 

  11. Chiba S, Ishida T, Ikeda K, Mochizuki M, Teramoto R, Taguchi YH, Iwadate M, Umeyama H, Ramakrishnan C, Thangakani AM, Velmurugan D (2017) An iterative compound screening contest method for identifying target protein inhibitors using the tyrosine-protein kinase Yes. Sci Rep 7:12038

    Article  PubMed  PubMed Central  Google Scholar 

  12. Panwar U, Chandra I, Selvaraj C, Singh SK (2019) Current computational approaches for the development of anti-HIV inhibitors: an overview. Curr Pharm Des 25:3390–3405

    Article  CAS  PubMed  Google Scholar 

  13. Panwar U, Singh SK (2018) An overview on Zika Virus and the importance of computational drug discovery. J Explor Res Pharmacol 3:43–51

    Article  Google Scholar 

  14. Rampogu S, Lemuel MR, Lee KW (2022) Virtual screening, molecular docking, molecular dynamics simulations and free energy calculations to discover potential DDX3 inhibitors. Adv Cancer Res 4:100022

    Google Scholar 

  15. Kontoyianni M (2017) Docking and virtual screening in drug discovery. In: Proteomics for drug discovery: methods and protocols. Springer, pp 255–266

    Chapter  Google Scholar 

  16. Aarthy M, Panwar U, Singh SK (2021) Magnitude and advancements of CADD in identifying therapeutic intervention against Flaviviruses. In: Innovations and implementations of computer aided drug discovery strategies in rational drug design. Springer, Singapore, pp 179–203

    Chapter  Google Scholar 

  17. Varela-Rial A, Majewski M, De Fabritiis G (2022) Structure based virtual screening: fast and slow. Wiley Interdiscip Rev Comput Mol Sci 12:e1544

    Article  CAS  Google Scholar 

  18. Bhrdwaj A, Abdalla M, Pande A, Madhavi M, Chopra I, Soni L, Vijayakumar N, Panwar U, Khan M, Prajapati L, Gujrati D (2023) Structure-based virtual screening, molecular docking, molecular dynamics simulation of EGFR for the clinical treatment of glioblastoma. Appl Biochem Biotechnol 28:1–26

    Google Scholar 

  19. Chopra I, Panwar U, Bhrdwaj A, Madhavi M, Soni L, Sharma K, Parihar AS, Mohan VP, Prajapati L, Joshi I, Sharma R (2023) Structural insights into conformational stability of ESR1 and structure base screening of new potent inhibitor for the treatment of Breast Cancer. https://doi.org/10.21203/rs.3.rs-1413803/v1

  20. Ferraz WR, Gomes RA, S Novaes AL, Goulart Trossini GH (2020) Ligand and structure-based virtual screening applied to the SARS-CoV-2 main protease: an in silico repurposing study. Future Med Chem 12:1815–1828

    Article  CAS  PubMed  Google Scholar 

  21. Drwal MN, Griffith R (2013) Combination of ligand-and structure-based methods in virtual screening. Drug Discov Today Technol 10:e395–e401

    Article  PubMed  Google Scholar 

  22. Sharda S, Sarmandal P, Cherukommu S, Dindhoria K, Yadav M, Bandaru S, Sharma A, Sakhi A, Vyas T, Hussain T, Nayarisseri A (2017) A virtual screening approach for the identification of high affinity small molecules targeting BCR-ABL1 inhibitors for the treatment of chronic myeloid leukemia. Curr Top Med Chem 17:2989–2996

    Article  CAS  PubMed  Google Scholar 

  23. Reddy KK, Singh SK, Tripathi SK, Selvaraj C, Suryanarayanan V (2013) Shape and pharmacophore-based virtual screening to identify potential cytochrome P450 sterol 14α-demethylase inhibitors. J Recept Signal Transduct Res 33:234–243

    Article  CAS  PubMed  Google Scholar 

  24. Ranganathan S, Ilavarasi AV, Palaka BK, Kuppusamy D, Ampasala DR (2022) Cloning, functional characterization and screening of potential inhibitors for Chilo partellus chitin synthase A using in silico, in vitro and in vivo approaches. J Biomol Struct Dyn 40:1416–1429

    Article  CAS  PubMed  Google Scholar 

  25. Patidar K, Deshmukh A, Bandaru S, Lakkaraju C, Girdhar A, Gutlapalli VR, Banerjee T, Nayarisseri A, Singh SK (2016) Virtual screening approaches in identification of bioactive compounds Akin to delphinidin as potential HER2 inhibitors for the treatment of breast cancer. Asian Pac J Cancer Prev 17:2291–2295

    Article  PubMed  Google Scholar 

  26. Ranganathan S, Ampasala DR, Palaka BK, Ilavarasi AV, Patidar I, Poovadan LP, Sapam TD (2021) In silico binding profile analysis and in vitro investigation on chitin synthase substrate and inhibitors from maize stem borer, Chilo partellus. Curr Comput-Aided Drug Des 17:881–895

    Article  CAS  PubMed  Google Scholar 

  27. Selvaraj C, Singh SK, Tripathi SK, Reddy KK, Rama M (2012) In silico screening of indinavir-based compounds targeting proteolytic activity in HIV PR: binding pocket fit approach. Med Chem Res 21:4060–4068

    Article  CAS  Google Scholar 

  28. Doucet D, Retnakaran A, Krell PJ, Feng Q, Ampasala DR (2016) Molecular cloning and structural characterization of ecdysis triggering hormone from Choristoneura fumiferana. Int J Biol Macromol 88:213–221

    Article  PubMed  Google Scholar 

  29. Selvaraj C, Panwar U, Dinesh DC, Boura E, Singh P, Dubey VK, Singh SK (2021) Microsecond MD simulation and multiple-conformation virtual screening to identify potential anti-COVID-19 inhibitors against SARS-CoV-2 main protease. Front Chem 8:595273

    Article  PubMed  PubMed Central  Google Scholar 

  30. Hamza A, Wei NN, Zhan CG (2012) Ligand-based virtual screening approach using a new scoring function. J Chem Inf Model 52:963–974

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Wu KJ, Lei PM, Liu H, Wu C, Leung CH, Ma DL (2019) Mimicking strategy for protein–protein interaction inhibitor discovery by virtual screening. Molecules 24:4428

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Asadzadeh A, Samad-Soltani T, Rezaei-Hachesu P (2021) Applications of virtual and augmented reality in infectious disease epidemics with a focus on the COVID-19 outbreak. Inform Med Unlocked 24:100579

    Article  PubMed  PubMed Central  Google Scholar 

  33. Selvaraj C, Panwar U, Ramalingam KR, Vijayakumar R, Singh SK (2022) Exploring the macromolecules for secretory pathway in cancer disease. Adv Protein Chem Struct Biol 133:55–83

    Article  PubMed  Google Scholar 

  34. Schottlender G, Prieto JM, Palumbo MC, Castello FA, Serral F, Sosa EJ, Turjanski AG, Martì MA, Fernández Do Porto D (2022) From drugs to targets: reverse engineering the virtual screening process on a proteomic scale. Front Drug Discov 2

    Google Scholar 

  35. Murugan NA, Podobas A, Gadioli D, Vitali E, Palermo G, Markidis S (2022) A review on parallel virtual screening softwares for high-performance computers. Pharmaceuticals 15:63

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. da Silva Rocha SFL, Olanda CG, Fokoue HH, Sant’Anna CMR (2019) Virtual screening techniques in drug discovery: review and recent applications. Curr Top Med Chem 19:1751–1767

    Article  PubMed  Google Scholar 

  37. Suryanarayanan V, Panwar U, Chandra I, Singh SK (2018) De novo design of ligands using computational methods. In: Gore M, Jagtap U (eds) Computational drug discovery and design. Methods in molecular biology, vol 1762. Humana Press, New York

    Google Scholar 

  38. Zhang B, Li H, Yu K, Jin Z (2022) Molecular docking-based computational platform for high-throughput virtual screening. CCF Trans High Perform Comput 13:1–2

    Google Scholar 

  39. Panwar U, Singh SK (2018) Structure-based virtual screening toward the discovery of novel inhibitors for impeding the protein-protein interaction between HIV-1 integrase and human lens epithelium-derived growth factor (LEDGF/p75). J Biomol Struct Dyn 36:3199–3217

    Article  CAS  PubMed  Google Scholar 

  40. Panwar U, Singh SK (2021) In silico virtual screening of potent inhibitor to hamper the interaction between HIV-1 integrase and LEDGF/p75 interaction using E-pharmacophore modeling, molecular docking, and dynamics simulations. Comput Biol Chem 93:107509

    Article  CAS  PubMed  Google Scholar 

  41. Reddy KK, Singh P, Singh SK (2014) Blocking the interaction between HIV-1 integrase and human LEDGF/p75: mutational studies, virtual screening, and molecular dynamics simulations. Mol Biosyst 10:526–536

    Article  CAS  PubMed  Google Scholar 

  42. Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoll EH, Shelley M, Perry JK, Shaw DE (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47:1739–1749

    Article  CAS  PubMed  Google Scholar 

  43. Panwar U, Singh SK (2019) Identification of novel pancreatic lipase inhibitors using in silico studies. Endocr Metab Immune Disord Drug Targets 19:449–457

    Article  CAS  PubMed  Google Scholar 

  44. Clark AJ, Tiwary P, Borrelli K, Feng S, Miller EB, Abel R, Friesner RA, Berne BJ (2016) Prediction of protein–ligand binding poses via a combination of induced fit docking and metadynamics simulations. J Chem Theory Comput 12:2990–2998

    Article  CAS  PubMed  Google Scholar 

  45. Panwar U, Singh SK (2021) Atom-based 3D-QSAR, molecular docking, DFT, and simulation studies of acylhydrazone, hydrazine, and diazene derivatives as IN-LEDGF/p75 inhibitors. Struct Chem 32:337–352

    Article  CAS  Google Scholar 

  46. QikProp, Schrödinger, LLC, New York, NY (2021)

    Google Scholar 

  47. Tripathi SK, Selvaraj C, Singh SK, Reddy KK (2012) Molecular docking, QPLD, and ADME prediction studies on HIV-1 integrase leads. Med Chem Res 21:4239–4251

    Article  CAS  Google Scholar 

  48. Desmond molecular dynamics system, D. E. Shaw Research, New York, NY (2021)

    Google Scholar 

  49. Reddy KK, Singh SK, Dessalew N, Tripathi SK, Selvaraj C (2012) Pharmacophore modelling and atom-based 3D-QSAR studies on N-methyl pyrimidones as HIV-1 integrase inhibitors. J Enzyme Inhib Med Chem 27:339–347

    Article  CAS  PubMed  Google Scholar 

  50. Jones D, Kim H, Zhang X, Zemla A, Stevenson G, Bennett WD, Kirshner D, Wong SE, Lightstone FC, Allen JE (2021) Improved protein–ligand binding affinity prediction with structure-based deep fusion inference. J Chem Inf Model 61:1583–1592

    Article  CAS  PubMed  Google Scholar 

  51. Vázquez J, López M, Gibert E, Herrero E, Luque FJ (2020) Merging ligand-based and structure-based methods in drug discovery: an overview of combined virtual screening approaches. Molecules 25:4723

    Article  PubMed  PubMed Central  Google Scholar 

  52. Luukkonen S, van den Maagdenberg HW, Emmerich MT, van Westen GJ (2023) Artificial intelligence in multi-objective drug design. Curr Opin Struct Biol 79:102537

    Article  CAS  PubMed  Google Scholar 

  53. Murugan NA, Priya GR, Sastry GN, Markidis S (2022) Artificial intelligence in virtual screening: models versus experiments. Drug Discov Today 18:1913

    Article  Google Scholar 

  54. Lyu J, Irwin JJ, Shoichet BK (2023) Modeling the expansion of virtual screening libraries. Nat Chem Biol 16:1–7

    Google Scholar 

  55. Wang E, Sun H, Wang J, Wang Z, Liu H, Zhang JZ, Hou T (2019) End-point binding free energy calculation with MM/PBSA and MM/GBSA: strategies and applications in drug design. Chem Rev 119:9478–9508

    Article  CAS  PubMed  Google Scholar 

  56. Genheden S, Ryde U (2015) The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discov 10:449–461

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Aarthy M, Panwar U, Singh SK (2020) Structural dynamic studies on identification of EGCG analogues for the inhibition of Human Papillomavirus E7. Sci Rep 10(1):8661

    Google Scholar 

  58. Majhi M, Ali MA, Limaye A, Sinha K, Bairagi P, Chouksey M, Shukla R, Kanwar N, Hussain T, Nayarisseri A, Singh SK (2019) An in silico investigation of potential EGFR inhibitors for the clinical treatment of colorectal cancer. Curr Top Med Chem 18(27):2355–2366

    Google Scholar 

  59. Ranganathan S, Ilavarasi AV, Palaka BK, Kuppusamy D, Ampasala DR (2022) Cloning, functional characterization and screening of potential inhibitors for Chilo partellus chitin synthase A using in silico in vitro and in vivo approaches. J Biomol Struct Dyn 40(3):1416–1429

    Google Scholar 

  60. Reddy KK, Singh SK, Tripathi SK, Selvaraj C (2013) Identification of potential HIV-1 integrase strand transfer inhibitors: In silico virtual screening and QM/MM docking studies. SAR QSAR Environ Res 24(7):581–595

    Google Scholar 

  61. Cavasotto CN, Adler NS, Aucar MG (2018) Quantum chemical approaches in structure-based virtual screening and lead optimization. Front Chem 6:188

    Google Scholar 

  62. Vijayalakshmi P, Selvaraj C, Singh SK, Nisha J, Saipriya K, Daisy P (2013) Exploration of the binding of DNA binding ligands to Staphylococcal DNA through QM/MM docking and molecular dynamics simulation. J Biomol Struct Dyn 31(6):561–571

    Google Scholar 

  63. Gleeson MP, Gleeson D (2009) QM/MM calculations in drug discovery: a useful method for studying binding phenomena? J Chem Inf Model 49:670–677

    Google Scholar 

  64. Aarthy M, Panwar U, Selvaraj C, Singh SK (2017) Advantages of structure-based drug design approaches in neurological disorders. Curr Neuropharmacol 15(8):1136–1155

    Google Scholar 

  65. Reddy KK, Singh SK (2014) Combined ligand and structure-based approaches on HIV-1 integrase strand transfer inhibitors. Chem Biol Interact 218:71–81

    Google Scholar 

  66. Ranganathan S, Ampasala DR, Palaka BK, Ilavarasi AV, Patidar I, Poovadan LP, Sapam TD (2021) In silico binding profile analysis and in vitro investigation on chitin synthase substrate and inhibitors from maize stem borer, Chilo partellus. Curr Comput Aided Drug Des 17:881–895

    Google Scholar 

  67. Selvaraj C, Krishnasamy G, Jagtap SS, Patel SK, Dhiman SS, Kim TS, Singh SK, Lee JK (2016) Structural insights into the binding mode of D-sorbitol with sorbitol dehydrogenase using QM-polarized ligand docking and molecular dynamics simulations. Biochem Eng J 114:244–256

    Google Scholar 

  68. Selvaraj C, Omer A, Singh P, Singh SK (2015) Molecular insights of protein contour recognition with ligand pharmacophoric sites through combinatorial library design and MD simulation in validating HTLV-1 PR inhibitors. Mol Biosyst 11(1):178–189

    Google Scholar 

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Acknowledgments

UP, AM, and SKS thankfully acknowledge the DST-PURSE 2nd Phase Programme grant [No. SR/PURSE Phase 2/38 (G); DST-FIST Grant [(SR/FST/LSI—667/2016)]; MHRD RUSA-Phase 2.0 grant sanctioned vide Letter no. [F.24‐51/2014‐U, Policy (TN Multi‐Gen), Department of Education, Govt of India]; Tamil Nadu State Council for Higher Education (TANSCHE) under [No. AU: S.O. (P&D): TANSCHE Projects: 117/ 202, File No. RGP/2019‐20/ALU/ HECP‐0048]; DBT-BIC, New Delhi, under Grant/Award [No. BT/PR40154/BTIS/137/ 34/2021, dated 31.12.2021]; and DBT-NNP Project, New Delhi, under Grant/Award [No. BT/PR40156/BTIS/54/2023 dated 06.02.2023] for providing the research grant and infrastructure facilities in the lab. CS thankfully acknowledge the Saveetha University for providing the infrastructure facilities to perform this work. MAK thankfully acknowledge the Alagappa University for providing the RUSA 2.0 Senior Research Fellowship [Alu/RUSA/SRF-Bioinformatics/4156/2022 dated 30.11.2022].

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Panwar, U., Murali, A., Khan, M.A., Selvaraj, C., Singh, S.K. (2024). Virtual Screening Process: A Guide in Modern Drug Designing. In: Gore, M., Jagtap, U.B. (eds) Computational Drug Discovery and Design. Methods in Molecular Biology, vol 2714. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3441-7_2

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