Rare Diseases: Drug Discovery and Informatics Resource

  • Mingzhu Zhao
  • Dong-Qing Wei


A rare disease refers to any disease with very low prevalence individually. Although the impacted population is small for a single disease, more than 6000 rare diseases affect millions of people across the world. Due to the small market size, high cost and possibly low return on investment, only in recent years, the research and development of rare disease drugs have gradually risen globally, in several domains including gene therapy, enzyme replacement therapy, and drug repositioning. Due to the complex etiology and heterogeneous symptoms, there is a large gap between basic research and patient unmet needs for rare disease drug discovery. As computational biology increasingly arises researchers’ awareness, the informatics database on rare disease have grown rapidly in the recent years, including drug targets, genetic variant and mutation, phenotype and ontology and patient registries. Along with the advances of informatics database and networks, new computational models will help accelerate the target identification and lead optimization process for rare disease pre-clinical drug development.


Rare disease Drug discovery Drug repositioning Informatics database Computational model 



Mingzhu Zhao is supported by grants from National Natural Science Foundation of China (Contract no. 61503244), and SMC-Morning Star Young Scholar Award of Shanghai Jiao Tong University. Dong-Qing Wei is supported by grants from the National High-Tech R&D Program (863 Program Contract no. 2012AA020307), the National Basic Research Program of China (973 Program Contract no. 2012CB721000), the Key Research Area Grant 2016YFA0501703 from the Ministry of Science and Technology of China, and Ph.D. Programs Foundation of Ministry of Education of China (Contract no. 20120073110057).


  1. 1.
    Pryde DC, Palmer MJ (2014) Orphan drugs and rare diseases. RSC drug discovery series, vol 38. Royal Society of Chemistry (Great Britain)Google Scholar
  2. 2.
    Cui Y, Han J (2017) Defining rare diseases in China. Intractable Rare Dis Res 6(2):148–149. PubMedPubMedCentralCrossRefGoogle Scholar
  3. 3.
    Cheng A, Xie Z (2017) Challenges in orphan drug development and regulatory policy in China. Orphanet J Rare Dis 12(1):13. PubMedPubMedCentralCrossRefGoogle Scholar
  4. 4.
    Stockklausner C, Lampert A, Hoffmann GF, Ries M (2016) Novel treatments for rare cancers: the U.S. orphan drug act is delivering—a cross-sectional analysis. Oncologist 21(4):487–493. PubMedPubMedCentralCrossRefGoogle Scholar
  5. 5.
    Melnikova I (2012) Rare diseases and orphan drugs. Nat Rev Drug Discov 11(4):267–268. PubMedCrossRefGoogle Scholar
  6. 6.
    Sardana D, Zhu C, Zhang M, Gudivada RC, Yang L, Jegga AG (2011) Drug repositioning for orphan diseases. Brief Bioinform 12(4):346–356. PubMedCrossRefGoogle Scholar
  7. 7.
    Naldini L (2015) Gene therapy returns to centre stage. Nature 526(7573):351–360. PubMedCrossRefGoogle Scholar
  8. 8.
    Perez IST, Lopez SP, Vergara ACZ (2017) Rare diseases: a current view. J Pediatr Care 3:2. Google Scholar
  9. 9.
    Bender E (2016) Gene therapy: industrial strength. Nature 537(7619):S57–S59. PubMedCrossRefGoogle Scholar
  10. 10.
    Karponi G, Psatha N, Lederer CW, Adair JE, Zervou F, Zogas N, Kleanthous M, Tsatalas C, Anagnostopoulos A, Sadelain M (2015) Plerixafor + G-CSF-mobilized CD34+ cells represent an optimal graft source for thalassemia gene therapy. Blood 126(5):616PubMedPubMedCentralCrossRefGoogle Scholar
  11. 11.
    Lucarelli G, Gaziev J, Isgro A, Sodani P, Paciaroni K, Alfieri C, De Angelis G, Marziali M, Simone MD, Gallucci C, Roveda A, Saltarelli F, Torelli F, Andreani M (2012) Allogeneic cellular gene therapy in hemoglobinopathies–evaluation of hematopoietic SCT in sickle cell anemia. Bone Marrow Transplant 47(2):227–230. PubMedCrossRefGoogle Scholar
  12. 12.
    Olowoyeye A, Okwundu CI (2016) Gene therapy for sickle cell disease. Cochrane Database Syst Rev 11:CD007652. PubMedGoogle Scholar
  13. 13.
    Georgiadis A, Duran Y, Ribeiro J, Abelleira-Hervas L, Robbie SJ, Sunkel-Laing B, Fourali S, Gonzalez-Cordero A, Cristante E, Michaelides M, Bainbridge JW, Smith AJ, Ali RR (2016) Development of an optimized AAV2/5 gene therapy vector for Leber congenital amaurosis owing to defects in RPE65. Gene Ther 23(12):857–862. PubMedPubMedCentralCrossRefGoogle Scholar
  14. 14.
    Bennett J, Wellman J, Marshall KA, McCague S, Ashtari M, DiStefano-Pappas J, Elci OU, Chung DC, Sun J, Wright JF, Cross DR, Aravand P, Cyckowski LL, Bennicelli JL, Mingozzi F, Auricchio A, Pierce EA, Ruggiero J, Leroy BP, Simonelli F, High KA, Maguire AM (2016) Safety and durability of effect of contralateral-eye administration of AAV2 gene therapy in patients with childhood-onset blindness caused by RPE65 mutations: a follow-on phase 1 trial. Lancet 388(10045):661–672. PubMedPubMedCentralCrossRefGoogle Scholar
  15. 15.
    Pang J, Boye SE, Lei B, Boye SL, Everhart D, Ryals R, Umino Y, Rohrer B, Alexander J, Li J, Dai X, Li Q, Chang B, Barlow R, Hauswirth WW (2010) Self-complementary AAV-mediated gene therapy restores cone function and prevents cone degeneration in two models of Rpe65 deficiency. Gene Ther 17(7):815–826. PubMedPubMedCentralCrossRefGoogle Scholar
  16. 16.
    Simonelli F, Maguire AM, Testa F, Pierce EA, Mingozzi F, Bennicelli JL, Rossi S, Marshall K, Banfi S, Surace EM, Sun J, Redmond TM, Zhu X, Shindler KS, Ying G-S, Ziviello C, Acerra C, Wright JF, McDonnell JW, High KA, Bennett J, Auricchio A (2010) Gene therapy for Leber’s congenital amaurosis is safe and effective through 1.5 years after vector administration. Mol Ther 18(3):643–650. PubMedCrossRefGoogle Scholar
  17. 17.
    Jacobson SG, Cideciyan AV, Roman AJ, Sumaroka A, Schwartz SB, Heon E, Hauswirth WW (2015) Improvement and decline in vision with gene therapy in childhood blindness. N Engl J Med 372(20):1920–1926. PubMedPubMedCentralCrossRefGoogle Scholar
  18. 18.
    Mullard A (2011) Gene therapies advance towards finish line. Nat Rev Drug Discov 10(10):719–720. PubMedCrossRefGoogle Scholar
  19. 19.
    Mavilio F (2017) Developing gene and cell therapies for rare diseases: an opportunity for synergy between academia and industry. Gene Ther. PubMedGoogle Scholar
  20. 20.
    Yiu WH, Pan CJ, Mead PA, Starost MF, Mansfield BC, Chou JY (2009) Normoglycemia alone is insufficient to prevent long-term complications of hepatocellular adenoma in glycogen storage disease type Ib mice. J Hepatol 51(5):909–917. PubMedPubMedCentralCrossRefGoogle Scholar
  21. 21.
    Rastall DP, Amalfitano A (2015) Recent advances in gene therapy for lysosomal storage disorders. Appl Clin Genet 8:157–169. PubMedPubMedCentralGoogle Scholar
  22. 22.
    Sasano T, Kikuchi K, McDonald AD, Lai S, Donahue JK (2007) Targeted high-efficiency, homogeneous myocardial gene transfer. J Mol Cell Cardiol 42(5):954–961. PubMedPubMedCentralCrossRefGoogle Scholar
  23. 23.
    Hill AB, Chen M, Chen CK, Pfeifer BA, Jones CH (2016) Overcoming gene-delivery hurdles: physiological considerations for nonviral vectors. Trends Biotechnol 34(2):91–105. PubMedCrossRefGoogle Scholar
  24. 24.
    Linhart A, Elliott PM (2007) The heart in Anderson-–Fabry disease and other lysosomal storage disorders. Heart 93(4):528–535. PubMedPubMedCentralCrossRefGoogle Scholar
  25. 25.
    Hopkin RJ, Jefferies JL, Laney DA, Lawson VH, Mauer M, Taylor MR, Wilcox WR, Fabry Pediatric Expert P (2016) The management and treatment of children with Fabry disease: a United States-based perspective. Mol Genet Metab 117(2):104–113. PubMedCrossRefGoogle Scholar
  26. 26.
    Hollak CE, Weinreb NJ (2015) The attenuated/late onset lysosomal storage disorders: therapeutic goals and indications for enzyme replacement treatment in Gaucher and Fabry disease. Best Pract Res Clin Endocrinol Metab 29(2):205–218. PubMedCrossRefGoogle Scholar
  27. 27.
    Smid BE, Ferraz MJ, Verhoek M, Mirzaian M, Wisse P, Overkleeft HS, Hollak CE, Aerts JM (2016) Biochemical response to substrate reduction therapy versus enzyme replacement therapy in Gaucher disease type 1 patients. Orphanet J Rare Dis 11:28. PubMedPubMedCentralCrossRefGoogle Scholar
  28. 28.
    Limgala RP, Ioanou C, Plassmeyer M, Ryherd M, Kozhaya L, Austin L, Abidoglu C, Unutmaz D, Alpan O, Goker-Alpan O (2016) Time of initiating enzyme replacement therapy affects immune abnormalities and disease severity in patients with gaucher disease. PLoS One 11(12):e0168135. PubMedPubMedCentralCrossRefGoogle Scholar
  29. 29.
    Dib RE, Pastores GM (2013) Enzyme replacement therapy for Anderson–Fabry disease. Cochrane Database Syst Rev 2(9282):CD006663Google Scholar
  30. 30.
    Nagueh SF (2014) Anderson–Fabry disease and other lysosomal storage disorders. Circulation 130(13):1081–1090. PubMedCrossRefGoogle Scholar
  31. 31.
    Hoffman EP, Barr ML, Giovanni MA, Murray MF (1993) Lysosomal acid lipase deficiency. In: Pagon RA, Adam MP, Ardinger HH et al (eds) GeneReviews(R). SeattleGoogle Scholar
  32. 32.
    Ortolano S, Vieitez I, Navarro C, Spuch C (2014) Treatment of lysosomal storage diseases: recent patents and future strategies. Recent Pat Endocr Metab Immune Drug Discov 8(1):9–25PubMedCrossRefGoogle Scholar
  33. 33.
    Sanford M, Lo JH (2014) Elosulfase alfa: first global approval. Drugs 74(6):713–718. PubMedCrossRefGoogle Scholar
  34. 34.
    Aiuti A, Cattaneo F, Galimberti S, Benninghoff U, Cassani B, Callegaro L, Scaramuzza S, Andolfi G, Mirolo M, Brigida I, Tabucchi A, Carlucci F, Eibl M, Aker M, Slavin S, Al-Mousa H, Al Ghonaium A, Ferster A, Duppenthaler A, Notarangelo L, Wintergerst U, Buckley RH, Bregni M, Marktel S, Valsecchi MG, Rossi P, Ciceri F, Miniero R, Bordignon C, Roncarolo MG (2009) Gene therapy for immunodeficiency due to adenosine deaminase deficiency. N Engl J Med 360(5):447–458. PubMedCrossRefGoogle Scholar
  35. 35.
    Whyte MP (2017) Hypophosphatasia: enzyme replacement therapy brings new opportunities and new challenges. J Bone Miner Res 32(4):667–675. PubMedCrossRefGoogle Scholar
  36. 36.
    Butters TD, Dwek RA, Platt FM (2005) Imino sugar inhibitors for treating the lysosomal glycosphingolipidoses. Glycobiology 15(10):43R–52R. PubMedCrossRefGoogle Scholar
  37. 37.
    Bruni S, Loschi L, Incerti C, Gabrielli O, Coppa GV (2007) Update on treatment of lysosomal storage diseases. Acta Myol 26(1):87–92PubMedPubMedCentralGoogle Scholar
  38. 38.
    Germain DP, Hughes DA, Nicholls K, Bichet DG, Giugliani R, Wilcox WR, Feliciani C, Shankar SP, Ezgu F, Amartino H, Bratkovic D, Feldt-Rasmussen U, Nedd K, Sharaf El Din U, Lourenco CM, Banikazemi M, Charrow J, Dasouki M, Finegold D, Giraldo P, Goker-Alpan O, Longo N, Scott CR, Torra R, Tuffaha A, Jovanovic A, Waldek S, Packman S, Ludington E, Viereck C, Kirk J, Yu J, Benjamin ER, Johnson F, Lockhart DJ, Skuban N, Castelli J, Barth J, Barlow C, Schiffmann R (2016) Treatment of Fabry’s disease with the pharmacologic chaperone migalastat. N Engl J Med 375(6):545–555. PubMedCrossRefGoogle Scholar
  39. 39.
    Markham A (2016) Migalastat: first global approval. Drugs 76(11):1147–1152. PubMedCrossRefGoogle Scholar
  40. 40.
    Cammisa M, Correra A, Andreotti G, Cubellis MV (2013) Fabry_CEP: a tool to identify Fabry mutations responsive to pharmacological chaperones. Orphanet J Rare Dis 8:111. PubMedPubMedCentralCrossRefGoogle Scholar
  41. 41.
    Boran AD, Iyengar R (2010) Systems approaches to polypharmacology and drug discovery. Curr Opin Drug Discov Dev 13(3):297–309Google Scholar
  42. 42.
    Metz JT, Hajduk PJ (2010) Rational approaches to targeted polypharmacology: creating and navigating protein-ligand interaction networks. Curr Opin Chem Biol 14(4):498–504. PubMedCrossRefGoogle Scholar
  43. 43.
    Hay Mele B, Citro V, Andreotti G, Cubellis MV (2015) Drug repositioning can accelerate discovery of pharmacological chaperones. Orphanet J Rare Dis 10:55. PubMedPubMedCentralCrossRefGoogle Scholar
  44. 44.
    Pujol A, Mosca R, Farres J, Aloy P (2010) Unveiling the role of network and systems biology in drug discovery. Trends Pharmacol Sci 31(3):115–123. PubMedCrossRefGoogle Scholar
  45. 45.
    Warrell RP Jr, Frankel SR, Miller WH Jr, Scheinberg DA, Itri LM, Hittelman WN, Vyas R, Andreeff M, Tafuri A, Jakubowski A et al (1991) Differentiation therapy of acute promyelocytic leukemia with tretinoin (all-trans-retinoic acid). N Engl J Med 324(20):1385–1393. PubMedCrossRefGoogle Scholar
  46. 46.
    Sun W, Zheng W, Simeonov A (2017) Drug discovery and development for rare genetic disorders. Am J Med Genet Part A 173(9):2307–2322. PubMedCrossRefGoogle Scholar
  47. 47.
    Rothstein JD, Patel S, Regan MR, Haenggeli C, Huang YH, Bergles DE, Jin L, Dykes Hoberg M, Vidensky S, Chung DS, Toan SV, Bruijn LI, Su ZZ, Gupta P, Fisher PB (2005) Beta-lactam antibiotics offer neuroprotection by increasing glutamate transporter expression. Nature 433(7021):73–77. PubMedCrossRefGoogle Scholar
  48. 48.
    Rath A, Olry A, Dhombres F, Brandt MM, Urbero B, Ayme S (2012) Representation of rare diseases in health information systems: the Orphanet approach to serve a wide range of end users. Hum Mutat 33(5):803–808. PubMedCrossRefGoogle Scholar
  49. 49.
    Maiella S, Rath A, Angin C, Mousson F, Kremp O (2013) Orphanet and its consortium: where to find expert-validated information on rare diseases. Revue Neurol 169(Suppl 1):S3–S8. CrossRefGoogle Scholar
  50. 50.
    Xu K, Cote TR (2011) Database identifies FDA-approved drugs with potential to be repurposed for treatment of orphan diseases. Brief Bioinform 12(4):341–345. PubMedCrossRefGoogle Scholar
  51. 51.
    Topel T, Scheible D, Trefz F, Hofestadt R (2010) RAMEDIS: a comprehensive information system for variations and corresponding phenotypes of rare metabolic diseases. Hum Mutat 31(1):E1081–E1088. PubMedCrossRefGoogle Scholar
  52. 52.
    Beaulieu CL, Majewski J, Schwartzentruber J, Samuels ME, Fernandez BA, Bernier FP, Brudno M, Knoppers B, Marcadier J, Dyment D, Adam S, Bulman DE, Jones SJ, Avard D, Nguyen MT, Rousseau F, Marshall C, Wintle RF, Shen Y, Scherer SW, Consortium FC, Friedman JM, Michaud JL, Boycott KM (2014) FORGE Canada Consortium: outcomes of a 2-year national rare-disease gene-discovery project. Am J Hum Genet 94(6):809–817. PubMedPubMedCentralCrossRefGoogle Scholar
  53. 53.
    Iorio F, Bosotti R, Scacheri E, Belcastro V, Mithbaokar P, Ferriero R, Murino L, Tagliaferri R, Brunetti-Pierri N, Isacchi A, di Bernardo D (2010) Discovery of drug mode of action and drug repositioning from transcriptional responses. Proc Natl Acad Sci USA 107(33):14621–14626. PubMedPubMedCentralCrossRefGoogle Scholar
  54. 54.
    Iorio F, Isacchi A, di Bernardo D, Brunetti-Pierri N (2010) Identification of small molecules enhancing autophagic function from drug network analysis. Autophagy 6(8):1204–1205. PubMedCrossRefGoogle Scholar
  55. 55.
    Amberger JS, Bocchini CA, Schiettecatte F, Scott AF, Hamosh A (2015) Online Mendelian Inheritance in Man (OMIM(R)), an online catalog of human genes and genetic disorders. Nucleic acids Res 43(Database issue):D789–D798. PubMedCrossRefGoogle Scholar
  56. 56.
    van Driel MA, Bruggeman J, Vriend G, Brunner HG, Leunissen JA (2006) A text-mining analysis of the human phenome. Eur J Hum Genet 14(5):535–542. PubMedCrossRefGoogle Scholar
  57. 57.
    Molineris I, Ala U, Provero P, Di Cunto F (2013) Drug repositioning for orphan genetic diseases through Conserved Anticoexpressed Gene Clusters (CAGCs). BMC Bioinform 14:288. CrossRefGoogle Scholar
  58. 58.
    Robinson PN, Kohler S, Bauer S, Seelow D, Horn D, Mundlos S (2008) The Human Phenotype Ontology: a tool for annotating and analyzing human hereditary disease. Am J Hum Genet 83(5):610–615. PubMedPubMedCentralCrossRefGoogle Scholar
  59. 59.
    The Lancet N (2017) Rare advances for rare diseases. Lancet Neurol 16(1):1. CrossRefGoogle Scholar
  60. 60.
    Buske OJ, Girdea M, Dumitriu S, Gallinger B, Hartley T, Trang H, Misyura A, Friedman T, Beaulieu C, Bone WP, Links AE, Washington NL, Haendel MA, Robinson PN, Boerkoel CF, Adams D, Gahl WA, Boycott KM, Brudno M (2015) PhenomeCentral: a portal for phenotypic and genotypic matchmaking of patients with rare genetic diseases. Hum Mutat 36(10):931–940. PubMedPubMedCentralCrossRefGoogle Scholar
  61. 61.
    Swaminathan GJ, Bragin E, Chatzimichali EA, Corpas M, Bevan AP, Wright CF, Carter NP, Hurles ME, Firth HV (2012) DECIPHER: web-based, community resource for clinical interpretation of rare variants in developmental disorders. Hum Mol Genet 21(R1):R37–R44. PubMedPubMedCentralCrossRefGoogle Scholar
  62. 62.
    Rodger S, Lochmuller H, Tassoni A, Gramsch K, Konig K, Bushby K, Straub V, Korinthenberg R, Kirschner J (2013) The TREAT-NMD care and trial site registry: an online registry to facilitate clinical research for neuromuscular diseases. Orphanet J Rare Dis 8:171. PubMedPubMedCentralCrossRefGoogle Scholar
  63. 63.
    Nagel G, Unal H, Rosenbohm A, Ludolph AC, Rothenbacher D, Group ALSRS (2013) Implementation of a population-based epidemiological rare disease registry: study protocol of the amyotrophic lateral sclerosis (ALS)–registry Swabia. BMC Neurol 13:22. PubMedPubMedCentralCrossRefGoogle Scholar
  64. 64.
    Roy AJ, Van den Bergh P, Van Damme P, Doggen K, Van Casteren V, Committee BS (2015) Early stages of building a rare disease registry, methods and 2010 data from the Belgian Neuromuscular Disease Registry (BNMDR). Acta Neurol Belg 115(2):97–104. PubMedCrossRefGoogle Scholar
  65. 65.
    Hilbert JE, Kissel JT, Luebbe EA, Martens WB, McDermott MP, Sanders DB, Tawil R, Thornton CA, Moxley RT 3rd, Registry Scientific Advisory C (2012) If you build a rare disease registry, will they enroll and will they use it? Methods and data from the National Registry of Myotonic Dystrophy (DM) and Facioscapulohumeral Muscular Dystrophy (FSHD). Contemp Clin Trials 33(2):302–311. PubMedCrossRefGoogle Scholar
  66. 66.
    Rubinstein YR, McInnes P (2015) NIH/NCATS/GRDR(R) common data elements: a leading force for standardized data collection. Contemp Clin Trials 42:78–80. PubMedPubMedCentralCrossRefGoogle Scholar
  67. 67.
    Song P, He J, Li F, Jin C (2017) Innovative measures to combat rare diseases in China: the national rare diseases registry system, larger-scale clinical cohort studies, and studies in combination with precision medicine research. Intractable Rare Dis Res 6(1):1–5. PubMedPubMedCentralCrossRefGoogle Scholar
  68. 68.
    Shameer K, Readhead B, Dudley JT (2015) Computational and experimental advances in drug repositioning for accelerated therapeutic stratification. Curr Top Med Chem 15(1):5–20PubMedCrossRefGoogle Scholar
  69. 69.
    Keiser MJ, Roth BL, Armbruster BN, Ernsberger P, Irwin JJ, Shoichet BK (2007) Relating protein pharmacology by ligand chemistry. Nat Biotechnol 25(2):197–206. PubMedCrossRefGoogle Scholar
  70. 70.
    Keiser MJ, Setola V, Irwin JJ, Laggner C, Abbas AI, Hufeisen SJ, Jensen NH, Kuijer MB, Matos RC, Tran TB, Whaley R, Glennon RA, Hert J, Thomas KL, Edwards DD, Shoichet BK, Roth BL (2009) Predicting new molecular targets for known drugs. Nature 462(7270):175–181. PubMedPubMedCentralCrossRefGoogle Scholar
  71. 71.
    DeGraw AJ, Keiser MJ, Ochocki JD, Shoichet BK, Distefano MD (2010) Prediction and evaluation of protein farnesyltransferase inhibition by commercial drugs. J Med Chem 53(6):2464–2471. PubMedPubMedCentralCrossRefGoogle Scholar
  72. 72.
    Lounkine E, Keiser MJ, Whitebread S, Mikhailov D, Hamon J, Jenkins JL, Lavan P, Weber E, Doak AK, Cote S, Shoichet BK, Urban L (2012) Large-scale prediction and testing of drug activity on side-effect targets. Nature 486(7403):361–367. PubMedPubMedCentralGoogle Scholar
  73. 73.
    Lin H, Sassano MF, Roth BL, Shoichet BK (2013) A pharmacological organization of G protein-coupled receptors. Nat Methods 10(2):140–146. PubMedPubMedCentralCrossRefGoogle Scholar
  74. 74.
    Grzybowski BA, Ishchenko AV, Kim CY, Topalov G, Chapman R, Christianson DW, Whitesides GM, Shakhnovich EI (2002) Combinatorial computational method gives new picomolar ligands for a known enzyme. Proc Natl Acad Sci USA 99(3):1270–1273. PubMedPubMedCentralCrossRefGoogle Scholar
  75. 75.
    Bissantz C (2003) Conformational changes of G protein-coupled receptors during their activation by agonist binding. J Recept Signal Transduct Res 23(2–3):123–153. PubMedCrossRefGoogle Scholar
  76. 76.
    Chen YZ, Zhi DG (2001) Ligand-protein inverse docking and its potential use in the computer search of protein targets of a small molecule. Proteins 43(2):217–226PubMedCrossRefGoogle Scholar
  77. 77.
    Chen YZ, Ung CY (2001) Prediction of potential toxicity and side effect protein targets of a small molecule by a ligand-protein inverse docking approach. J Mol Graph Model 20(3):199–218PubMedCrossRefGoogle Scholar
  78. 78.
    Zahler S, Tietze S, Totzke F, Kubbutat M, Meijer L, Vollmar AM, Apostolakis J (2007) Inverse in silico screening for identification of kinase inhibitor targets. Chem Biol 14(11):1207–1214. PubMedCrossRefGoogle Scholar
  79. 79.
    MacDonald ML, Lamerdin J, Owens S, Keon BH, Bilter GK, Shang Z, Huang Z, Yu H, Dias J, Minami T, Michnick SW, Westwick JK (2006) Identifying off-target effects and hidden phenotypes of drugs in human cells. Nat Chem Biol 2(6):329–337. PubMedCrossRefGoogle Scholar
  80. 80.
    Fischer M, Coleman RG, Fraser JS, Shoichet BK (2014) Incorporation of protein flexibility and conformational energy penalties in docking screens to improve ligand discovery. Nat Chem 6(7):575–583. PubMedPubMedCentralCrossRefGoogle Scholar
  81. 81.
    Takigawa I, Tsuda K, Mamitsuka H (2011) Mining significant substructure pairs for interpreting polypharmacology in drug-target network. PLoS One 6(2):e16999. PubMedPubMedCentralCrossRefGoogle Scholar
  82. 82.
    Fan H, Gu R, Wei D (2015) The α7 nAChR selective agonists as drug candidates for Alzheimer’s disease. In: Wei D, Xu Q, Zhao T, Dai H (eds) Advance in structural bioinformatics. Springer, Dordrecht, pp 353–365. doi:
  83. 83.
    Liu YT, Li Y, Huang ZF, Xu ZJ, Yang Z, Chen ZX, Chen KX, Shi JY, Zhu WL (2014) Multi-algorithm and multi-model based drug target prediction and web server. Acta Pharmacol Sin 35(3):419–431. PubMedPubMedCentralCrossRefGoogle Scholar
  84. 84.
    Yang L, Wang K, Chen J, Jegga AG, Luo H, Shi L, Wan C, Guo X, Qin S, He G, Feng G, He L (2011) Exploring off-targets and off-systems for adverse drug reactions via chemical-protein interactome–clozapine-induced agranulocytosis as a case study. PLoS Comput Biol 7(3):e1002016. PubMedPubMedCentralCrossRefGoogle Scholar
  85. 85.
    Li H, Gao Z, Kang L, Zhang H, Yang K, Yu K, Luo X, Zhu W, Chen K, Shen J, Wang X, Jiang H (2006) TarFisDock: a web server for identifying drug targets with docking approach. Nucleic Acids Res 34(Web Server issue):W219–W224. PubMedPubMedCentralCrossRefGoogle Scholar
  86. 86.
    Liu X, Ouyang S, Yu B, Liu Y, Huang K, Gong J, Zheng S, Li Z, Li H, Jiang H (2010) PharmMapper server: a web server for potential drug target identification using pharmacophore mapping approach. Nucleic Acids Res 38(Web Server issue):W609–W614. PubMedPubMedCentralCrossRefGoogle Scholar
  87. 87.
    Gong J, Cai C, Liu X, Ku X, Jiang H, Gao D, Li H (2013) ChemMapper: a versatile web server for exploring pharmacology and chemical structure association based on molecular 3D similarity method. Bioinformatics 29(14):1827–1829. PubMedCrossRefGoogle Scholar
  88. 88.
    Yildirim MA, Goh KI, Cusick ME, Barabasi AL, Vidal M (2007) Drug-target network. Nat Biotechnol 25(10):1119–1126. PubMedCrossRefGoogle Scholar
  89. 89.
    Mestres J, Gregori-Puigjane E, Valverde S, Sole RV (2009) The topology of drug-target interaction networks: implicit dependence on drug properties and target families. Mol BioSyst 5(9):1051–1057. PubMedCrossRefGoogle Scholar
  90. 90.
    Cheng F, Liu C, Jiang J, Lu W, Li W, Liu G, Zhou W, Huang J, Tang Y (2012) Prediction of drug-target interactions and drug repositioning via network-based inference. PLoS Comput Biol 8(5):e1002503. PubMedPubMedCentralCrossRefGoogle Scholar
  91. 91.
    Wang YY, Nacher JC, Zhao XM (2012) Predicting drug targets based on protein domains. Mol BioSyst 8(5):1528–1534. PubMedCrossRefGoogle Scholar
  92. 92.
    Wang H, Zheng H, Azuaje F, Zhao XM (2013) Drug-domain interaction networks in myocardial infarction. IEEE Trans Nanobiosci 12(3):182–188. CrossRefGoogle Scholar
  93. 93.
    Campillos M, Kuhn M, Gavin AC, Jensen LJ, Bork P (2008) Drug target identification using side-effect similarity. Science 321(5886):263–266. PubMedCrossRefGoogle Scholar
  94. 94.
    Chiang AP, Butte AJ (2009) Systematic evaluation of drug-disease relationships to identify leads for novel drug uses. Clin Pharmacol Ther 86(5):507–510. PubMedPubMedCentralCrossRefGoogle Scholar
  95. 95.
    Luo H, Wang J, Li M, Luo J, Peng X, Wu FX, Pan Y (2016) Drug repositioning based on comprehensive similarity measures and Bi-Random walk algorithm. Bioinformatics 32(17):2664–2671. PubMedCrossRefGoogle Scholar
  96. 96.
    Moghadam H, Rahgozar M, Gharaghani S (2016) Scoring multiple features to predict drug disease associations using information fusion and aggregation. SAR QSAR Environ Res 27(8):609–628. PubMedCrossRefGoogle Scholar
  97. 97.
    Sahin O, Frohlich H, Lobke C, Korf U, Burmester S, Majety M, Mattern J, Schupp I, Chaouiya C, Thieffry D, Poustka A, Wiemann S, Beissbarth T, Arlt D (2009) Modeling ERBB receptor-regulated G1/S transition to find novel targets for de novo trastuzumab resistance. BMC Syst Biol 3:1. PubMedPubMedCentralCrossRefGoogle Scholar
  98. 98.
    Goltsov A, Maryashkin A, Swat M, Kosinsky Y, Humphery-Smith I, Demin O, Goryanin I, Lebedeva G (2009) Kinetic modelling of NSAID action on COX-1: focus on in vitro/in vivo aspects and drug combinations. Eur J Pharm Sci 36(1):122–136. PubMedCrossRefGoogle Scholar
  99. 99.
    Autiero I, Costantini S, Colonna G (2009) Modeling of the bacterial mechanism of methicillin-resistance by a systems biology approach. PLoS One 4(7):e6226. PubMedPubMedCentralCrossRefGoogle Scholar
  100. 100.
    Sanseau P, Agarwal P, Barnes MR, Pastinen T, Richards JB, Cardon LR, Mooser V (2012) Use of genome-wide association studies for drug repositioning. Nat Biotechnol 30(4):317–320. PubMedCrossRefGoogle Scholar
  101. 101.
    Wu Z, Wang Y, Chen L (2013) Drug repositioning framework by incorporating functional information. IET Syst Biol 7(5):188–194. PubMedCrossRefGoogle Scholar
  102. 102.
    Pratanwanich N, Lio P (2014) Pathway-based Bayesian inference of drug-disease interactions. Mol BioSyst 10(6):1538–1548. PubMedCrossRefGoogle Scholar
  103. 103.
    Zhao J, Jiang P, Zhang W (2010) Molecular networks for the study of TCM pharmacology. Brief Bioinform 11(4):417–430. PubMedCrossRefGoogle Scholar
  104. 104.
    Wu X, Jiang R, Zhang MQ, Li S (2008) Network-based global inference of human disease genes. Mol Syst Biol 4:189. PubMedPubMedCentralCrossRefGoogle Scholar
  105. 105.
    Yao X, Hao H, Li Y, Li S (2011) Modularity-based credible prediction of disease genes and detection of disease subtypes on the phenotype-gene heterogeneous network. BMC Syst Biol 5:79. PubMedPubMedCentralCrossRefGoogle Scholar
  106. 106.
    Davis AP, Wiegers TC, Roberts PM, King BL, Lay JM, Lennon-Hopkins K, Sciaky D, Johnson R, Keating H, Greene N, Hernandez R, McConnell KJ, Enayetallah AE, Mattingly CJ (2013) A CTD-Pfizer collaboration: manual curation of 88,000 scientific articles text mined for drug-disease and drug-phenotype interactions. Database 2013:bat080. doi:
  107. 107.
    Yang J, Li Z, Fan X, Cheng Y (2014) Drug-disease association and drug-repositioning predictions in complex diseases using causal inference-probabilistic matrix factorization. J Chem Inf Model 54(9):2562–2569. PubMedCrossRefGoogle Scholar
  108. 108.
    Oh M, Ahn J, Yoon Y (2014) A network-based classification model for deriving novel drug-disease associations and assessing their molecular actions. PLoS One 9(10):e111668. PubMedPubMedCentralCrossRefGoogle Scholar
  109. 109.
    Schriml LM, Arze C, Nadendla S, Chang YW, Mazaitis M, Felix V, Feng G, Kibbe WA (2012) Disease Ontology: a backbone for disease semantic integration. Nucleic Acids Res 40(Database issue):D940–D946. PubMedCrossRefGoogle Scholar
  110. 110.
    Martinez V, Navarro C, Cano C, Fajardo W, Blanco A (2015) DrugNet: network-based drug-disease prioritization by integrating heterogeneous data. Artif Intell Med 63(1):41–49. PubMedCrossRefGoogle Scholar
  111. 111.
    Sun PG (2015) The human drug-disease-gene network. Inf Sci 306(C):70–80CrossRefGoogle Scholar
  112. 112.
    Jin G, Wong ST (2014) Toward better drug repositioning: prioritizing and integrating existing methods into efficient pipelines. Drug Discov Today 19(5):637–644. PubMedCrossRefGoogle Scholar
  113. 113.
    Pei J, Yin N, Ma X, Lai L (2014) Systems biology brings new dimensions for structure-based drug design. J Am Chem Soc 136(33):11556–11565. PubMedCrossRefGoogle Scholar
  114. 114.
    Wu Z, Wang Y, Chen L (2013) Network-based drug repositioning. Mol BioSyst 9(6):1268–1281. PubMedCrossRefGoogle Scholar
  115. 115.
    Zhao M, Zhou Q, Ma W, Wei DQ (2013) Exploring the ligand-protein networks in traditional chinese medicine: current databases, methods, and applications. Evid Based Complement Altern Med 2013:806072. Google Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Instrumental Analysis CenterShanghai Jiao Tong UniversityShanghaiChina
  2. 2.School of Life Science and BiotechnologyShanghai Jiao Tong UniversityShanghaiChina

Personalised recommendations