Computer-Aided Drug Discovery

  • Birbal SinghEmail author
  • Gorakh Mal
  • Sanjeev K. Gautam
  • Manishi Mukesh


Computer-aided drug designating or drug discovery is the methodology based on computational and bioinformatics approaches to discover, develop, and analyze the drugs and similar biologically active molecules. The computer-aided drug discovery is benefited from massive genome and proteome data of pathogens and hosts accessible for analysis and interpretation. It is possible to discover potential proteins and metabolic pathways of pathogenic microorganisms and the parasites and develop novel biomolecules as drugs or therapeutics.

  • Computer-aided drug discovery is an in silico method of developing drugs or drug-like molecules

  • The technique has important contribution to develop drugs against pathogens and parasites.


Computational drug designing Proteome data Computer-aided drug discovery Protein structure prediction Molecular dynamics Ligand-based drug discovery 


  1. Abagyan R, Totrov M, Kuznetsov D (1994) ICM—a new method for protein modeling and design: applications to docking and structure prediction from the distorted native conformation. J Comput Chem 15(5):488–506CrossRefGoogle Scholar
  2. Acharya C, Coop A, Polli JE, Mackerell AD Jr (2011) Recent advances in ligand-based drug design: relevance and utility of the conformationally sampled pharmacophore approach. Curr Comput Aided Drug Des. 2011 Mar;7(1):10–22 (Review)PubMedPubMedCentralCrossRefGoogle Scholar
  3. Akamatsu M (2002) Current state and perspectives of 3D-QSAR. Curr Top Med Chem 2(12):1381–1394 (Review)PubMedCrossRefGoogle Scholar
  4. Alberg DG, Schreiber SL (1993) Structure-based design of a cyclophilin-calcineurin bridging ligand. Science 262(5131):248–250PubMedCrossRefGoogle Scholar
  5. Allen WJ, Balius TE, Mukherjee S, Brozell SR, Moustakas DT, Lang PT, Case DA, Kuntz ID, Rizzo RC (2015) DOCK 6: Impact of new features and current docking performance. J Comput Chem 36(15):1132–1156. Scholar
  6. Barcellos GB, Pauli I, Caceres RA, Timmers LF, Dias R, de Azevedo WF Jr (2008) Molecular modeling as a tool for drug discovery. Curr Drug Targets 9(12):1084–1091 (Review)PubMedCrossRefGoogle Scholar
  7. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2006) The protein data bank, 1999. In: International tables for crystallography volume F: crystallography of biological macromolecules. Springer, Netherlands, pp. 675–684CrossRefGoogle Scholar
  8. Biasini M, Bienert S, Waterhouse A, Arnold K, Studer G, Schmidt T, Kiefer F, Gallo Cassarino T, Bertoni M, Bordoli L, Schwede T (2014) SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Res 42(Web Server issue):W252–W258. Epub 2014 Apr 29PubMedPubMedCentralCrossRefGoogle Scholar
  9. Chemical Computing Group, M. O. E. (2008) Molecular operating environmentGoogle Scholar
  10. Cramer RD 3rd, Patterson DE, Bunce JD (1989) Recent advances in comparative molecular field analysis (CoMFA). Prog Clin Biol Res 291:161–165PubMedGoogle Scholar
  11. Dai J, Dan W, Li N, Wang J (2018) Computer-aided drug discovery: novel 3,9-disubstituted eudistomin U derivatives as potent antibacterial agents. Eur J Med Chem 5(157):333–338. (Epub 2018 Aug 4)CrossRefGoogle Scholar
  12. De B, Bhandari K, Mendonça FJB, Scotti MT, Scotti L (2018) Computational studies in drug design against cancer. Anticancer Agents Med Chem. (Epub ahead of print)CrossRefGoogle Scholar
  13. Dias R, de Azevedo Jr WF (2008) Molecular docking algorithms. Curr Drug Targets 9(12):1040–1047 (Review)PubMedCrossRefGoogle Scholar
  14. Du QS, Huang RB, Wang SQ, Chou KC (2010) Designing inhibitors of M2 proton channel against H1N1 swine influenza virus. PLoS ONE 5(2):e9388. Scholar
  15. Ehrlich P (1909) Über den jetzigen Stand der Chemotherapie. Eur J Inorg Chem 42(1):17–47Google Scholar
  16. Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoll EH, Shelley M, Perry JK, Shaw DE, Francis P, Shenkin PS (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47(7):1739–1749PubMedCrossRefGoogle Scholar
  17. Gaudreault F, Najmanovich RJ (2015) FlexAID: revisiting docking on non-native-complex structures. J Chem Inf Model 55(7):1323–1336. (Epub 2015 Jun 24)PubMedCrossRefGoogle Scholar
  18. Glicksberg BS, Li L, Chen R, Dudley J, Chen B (2019) Leveraging big data to transform drug discovery. Methods Mol Biol 1939:91–118. Scholar
  19. Goodsell DS, Olson AJ (1990) Automated docking of substrates to proteins by simulated annealing. Proteins 8(3):195–202PubMedCrossRefGoogle Scholar
  20. Grosdidier A, Zoete V, Michielin O (2007) EADock: docking of small molecules into protein active sites with a multiobjective evolutionary optimization. Proteins. 67(4):1010–1025PubMedCrossRefGoogle Scholar
  21. Gund P (1977) Three-dimensional pharmacophoric pattern searching. In: Progress in molecular and subcellular biology. Springer, Berlin, pp 117–143CrossRefGoogle Scholar
  22. Hammes GG (2002) Multiple conformational changes in enzyme catalysis. Biochemistry. 2002 Jul 2;41(26):8221–8 (Review)PubMedCrossRefGoogle Scholar
  23. Jain AN (2003) Surflex: fully automatic flexible molecular docking using a molecular similarity-based search engine. J Med Chem 46(4):499–511PubMedCrossRefGoogle Scholar
  24. Joo K, Lee J, Lee J (2012) Methods for accurate homology modeling by global optimization. Methods Mol Biol 857:175–188. Scholar
  25. Källberg M, Margaryan G, Wang S, Ma J, Xu J (2014) RaptorX server: a resource for template-based protein structure modeling. Methods Mol Biol 1137:17–27. Scholar
  26. Kapetanovic IM (2008) Computer-aided drug discovery and development (CADDD): in silico-chemico-biological approach. Chem Biol Interact 171(2):165–176 (Epub 2006 Dec 16. Review)PubMedCrossRefGoogle Scholar
  27. Kelley LA, Mezulis S, Yates CM, Wass MN, Sternberg MJ (2015) The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc 10(6):845–858. (Epub 2015 May 7)PubMedPubMedCentralCrossRefGoogle Scholar
  28. Kim DE, Chivian D, Baker D (2004) Protein structure prediction and analysis using the Robetta server. Nucleic Acids Res 32(Web Server issue):W526–W531PubMedPubMedCentralCrossRefGoogle Scholar
  29. Klebe G, Abraham U, Mietzner T (1994) Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. J Med Chem 37(24):4130–4146PubMedCrossRefGoogle Scholar
  30. Kubinyi H (2006a) Success stories of computer-aided design. Comput Appl Pharm Res Develop 2:377CrossRefGoogle Scholar
  31. Kubinyi H (2006b) Chemogenomics in drug discovery. Ernst Schering Res Found Workshop. 58:1–19CrossRefGoogle Scholar
  32. Lambert C, Léonard N, De Bolle X, Depiereux E (2002) ESyPred3D: prediction of proteins 3D structures. Bioinformatics 18(9):1250–1256PubMedCrossRefGoogle Scholar
  33. Liu M, Wang S (1999) MCDOCK: a Monte Carlo simulation approach to the molecular docking problem. J Comput Aided Mol Des 13(5):435–451PubMedCrossRefGoogle Scholar
  34. Martí-Renom MA, Stuart AC, Fiser A, Sánchez R, Melo F, Sali A (2000) Comparative protein structure modeling of genes and genomes. Annu Rev Biophys Biomol Struct 29:291–325 (Review)PubMedCrossRefGoogle Scholar
  35. McGuffin LJ, Atkins JD, Salehe BR, Shuid AN, Roche DB (2015) IntFOLD: an integrated server for modelling protein structures and functions from amino acid sequences. Nucleic Acids Res 43(W1):W169–W173. (Epub 2015 Mar 27)PubMedPubMedCentralCrossRefGoogle Scholar
  36. Mezei M (2003) A novel fingerprint for the characterization of protein folds. Protein Eng 16(10):713–715PubMedCrossRefGoogle Scholar
  37. Paul DS, Gautham N (2016) MOLS 2.0: software package for peptide modeling and protein-ligand docking. J Mol Model 22(10):239. (Epub 2016 Sep 16)
  38. Rarey M, Kramer B, Lengauer T, Klebe G (1996) A fast flexible docking method using an incremental construction algorithm. J Mol Biol 261(3):470–489PubMedCrossRefGoogle Scholar
  39. Ricci-López J, Vidal-Limon A, Zunñiga M, Jimènez VA, Alderete JB, Brizuela CA, Aguila S (2019) Molecular modeling simulation studies reveal new potential inhibitors against HPV E6 protein. PLoS ONE 14(3):e0213028. (eCollection 2019)PubMedPubMedCentralCrossRefGoogle Scholar
  40. Ruiz-Carmona S, Alvarez-Garcia D, Foloppe N, Garmendia-Doval AB, Juhos S, Schmidtke P, Barril X, Hubbard RE, Morley SD (2014) rDock: a fast, versatile and open source program for docking ligands to proteins and nucleic acids. PLoS Comput Biol 10(4):e1003571. (eCollection 2014 Apr)PubMedPubMedCentralCrossRefGoogle Scholar
  41. Shaikh SA, Jain T, Sandhu G, Latha N, Jayaram B (2007) From drug target to leads–sketching a physicochemical pathway for lead molecule design in silico. Curr Pharm Des 13(34):3454–3470 (Review)PubMedCrossRefGoogle Scholar
  42. Sharma D, Sharma A, Verma SK, Singh B (2019) Targeting metabolic pathways proteins of Orientia tsutsugamushi using combined hierarchical approach to combat scrub typhus. J Mol Recognit 32(4):e2766. (Epub 2018 Oct 21)CrossRefGoogle Scholar
  43. Shoichet BK (2004) Virtual screening of chemical libraries. Nature 432(7019):862–865 (Review)PubMedPubMedCentralCrossRefGoogle Scholar
  44. Silva LC, Neves BJ, Gomes MN, Melo-Filho CC, Soares CM, Andrade CH, Pereira M (2018) Computer-aided identification of novel anti-paracoccidioidomycosis compounds. Future Microbiol 13:1523–1535. (Epub 2018 Oct 12)PubMedCrossRefGoogle Scholar
  45. Sindhikara D, Spronk SA, Day T, Borrelli K, Cheney DL, Posy SL (2017) Improving accuracy, diversity, and speed with prime macrocycle conformational sampling. J Chem Inf Model 57(8):1881–1894. (Epub 2017 Aug 8)PubMedCrossRefGoogle Scholar
  46. Singh G, Sharma D, Singh V, Rani J, Marotta F, Kumar M, Mal G, Singh B (2017) In silico functional elucidation of uncharacterized proteins of Chlamydia abortus strain LLG. Future Sci OA 3(1):FSO169. (eCollection 2017 Mar. Erratum in: Future Sci OA. 2017 Oct 05;3(4):FSO66C1)PubMedPubMedCentralCrossRefGoogle Scholar
  47. Sliwoski G, Kothiwale S, Meiler J, Lowe EW Jr (2013) Computational methods in drug discovery. Pharmacol Rev 66(1):334–395. (Print 2014. Review)PubMedCrossRefGoogle Scholar
  48. Sujatha K, Mahalakshmi A, Solaiman DK, Shenbagarathai R (2009) Sequence analysis, structure prediction, and functional validation of phaC1/phaC2 genes of Pseudomonas sp. LDC-25 and its importance in polyhydroxyalkanoate accumulation. J Biomol Struct Dyn 26(6):771–779PubMedCrossRefGoogle Scholar
  49. Taylor JS, Burnett RM (2000) DARWIN: a program for docking flexible molecules. Proteins 41(2):173–191PubMedCrossRefGoogle Scholar
  50. Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31(2):455–461. Scholar
  51. van Drie JH (2003) Pharmacophore discovery–lessons learned. Curr Pharm Des9(20):1649–1664 (Review)Google Scholar
  52. Verdonk ML, Cole JC, Hartshorn MJ, Murray CW, Taylor RD (2003) Improved protein-ligand docking using GOLD. Proteins 52(4):609–623PubMedCrossRefGoogle Scholar
  53. Verlinde CL, Hol WG (1994) Structure-based drug design: progress, results and challenges. Structure 2(7):577–587 (Review)PubMedCrossRefGoogle Scholar
  54. Wang R, Lu Y, Wang S (2003) Comparative evaluation of 11 scoring functions for molecular docking. J Med Chem 46(12):2287–2303PubMedCrossRefGoogle Scholar
  55. Webb B, Sali A (2014) Protein structure modeling with MODELLER. Methods Mol Biol 1137:1–15. Scholar
  56. Zhong F, Xing J, Li X, Liu X, Fu Z, Xiong Z, Lu D, Wu X, Zhao J, Tan X, Li F, Luo X, Li Z, Chen K, Zheng M, Jiang H (2018) Artificial intelligence in drug design. Sci China Life Sci. Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Birbal Singh
    • 1
    Email author
  • Gorakh Mal
    • 1
  • Sanjeev K. Gautam
    • 2
  • Manishi Mukesh
    • 3
  1. 1.ICAR-Indian Veterinary Research Institute, Regional StationPalampurIndia
  2. 2.Department of BiotechnologyKurukshetra UniversityKurukshetraIndia
  3. 3.Department of Animal BiotechnologyICAR-National Bureau of Animal Genetic ResourcesKarnalIndia

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