Accurate and efficient target prediction using a potency-sensitive influence-relevance voter

Journal of Cheminformatics

Volumn 7, Issue 1, 2015, Pages

  Lusci, Alessandro ^a   Browning, Michael ^b   Fooshee, David ^a   Swamidass, Joshua ^b   Baldi, Pierre ^a  

^a Ringgold Standard Institution Informatics   (United States)

^b WASHINGTON UNIVERSITY   (United States)

Author keywords

Fingerprints; Influence relevance voter; Large scale; Molecular potency; Random inactive molecules; Target prediction

Indexed keywords

EID: 84952304768     PISSN: None     EISSN: 17582946     Source Type: Journal    
DOI: 10.1186/s13321-015-0110-6     Document Type: Article

References (57)

1. Lounkine E, Keiser MJ, Whitebread S, Mikhailov D, Hamon J, Jenkins JL, Lavan P, Weber E, Doak AK, Cote S (2012) Large-scale prediction and testing of drug activity on side-effect targets. Nature 486(7403):361-367
2. Keiser MJ, Setola V, Irwin JJ, Laggner C, Abbas AI, Hufeisen SJ, Jensen NH, Kuijer MB, Matos RC, Tran TB (2009) Predicting new molecular targets for known drugs. Nature 462(7270):175-181
3. Schwab CH (2011) Conformations and 3d pharmacophore searching. Drug Discov Today Technol 7(4):245-253
4. Ripphausen P, Nisius B, Bajorath J (2011) State-of-the-art in ligand-based virtual screening. Drug Discov Today 16(9):372-376
5. Swamidass SJ, Azencott C-A, Lin T-W, Gramajo H, Tsai S-C, Baldi P (2009) Influence relevance voting: an accurate and interpretable virtual high throughput screening method. J Chem Inf Model 49(4):756-766
6. Simon Z, Peragovics Á, Vigh-Smeller M, Csukly G, Tombor L, Yang Z, Zahoránszky-Kohalmi G, Végner L, Jelinek B, Hári P (2011) Drug effect prediction by polypharmacology-based interaction profiling. J Chem Inf Model 52(1):134-145
7. Meslamani J, Bhajun R, Martz F, Rognan D (2013) Computational profiling of bioactive compounds using a target-dependent composite workflow. J Chem Inf Model 53(9):2322-2333
8. Heikamp K, Bajorath J (2011) Large-scale similarity search profiling of chembl compound data sets. J Chem Inf Model 51(8):1831-1839
9. Vidal D, Mestres J (2010) In silico receptorome screening of antipsychotic drugs. Mol Inf 29(6-7):543-551
10. Sugaya N (2013) Training based on ligand efficiency improves prediction of bioactivities of ligands and drug target proteins in a machine learning approach. J Chem Inf Model 53(10):2525-2537
11. Sugaya N (2014) Ligand efficiency-based support vector regression models for predicting bioactivities of ligands to drug target proteins. J Chem Inf Model 54(10):2751-2763
12. Alvarsson J, Eklund M, Engkvist O, Spjuth O, Carlsson L, Wikberg JE, Noeske T (2014) Ligand-based target prediction with signature fingerprints. J Chem Inf Model 54(10):2647-2653
13. Simmons KJ, Chopra I, Fishwick CW (2010) Structure-based discovery of antibacterial drugs. Nat Rev Microbiol 8(7):501-510
14. Lill M (2013) Virtual screening in drug design. In: In Silico Models for Drug Discovery. Springer, New york, pp. 1-12
15. Breault GA, Comita-Prevoir J, Eyermann CJ, Geng B, Petrichko R, Doig P, Gorseth E, Noonan B (2008) Exploring 8-benzyl pteridine-6, 7-diones as inhibitors of glutamate racemase (muri) in gram-positive bacteria. Bioorg Med Chem Lett 18(23):6100-6103
16. Baldi P, Nasr R (2010) When is chemical similarity significant? the statistical distribution of chemical similarity scores and its extreme values. J Chem Inf Model:1205-1222 (in press)
17. Nasr R, Vernica R, Li C, Baldi P (2012) Speeding up chemical searches using the inverted index: the convergence of chemoinformatics and text search methods. J Chem Inf Model 52(4):891-900
18. Chen J, Swamidass SJ, Dou Y, Bruand J, Baldi P (2005) ChemDB: a public database of small molecules and related chemoinformatics resources. Bioinformatics 21:4133-4139
19. Chen JH, Linstead E, Swamidass SJ, Wang D, Baldi P (2007) ChemDB update-full-text search and virtual chemical space. Bioinformatics 23(17):2348-2351
20. Hert J, Keiser MJ, Irwin JJ, Oprea TI, Shoichet BK (2008) Quantifying the relationships among drug classes. J Chem Inf Model 48(4):755-765
21. Olah M, Mracec M, Ostopovici L, Rad R, Bora A, Hadaruga N, Olah I, Banda M, Simon Z (2004) Wombat: world of molecular bioactivity. Chemoinformatics Drug Discov 1
22. Gregori-Puigjané E, Mestres J (2008) A ligand-based approach to mining the chemogenomic space of drugs. Comb Chem High Throughput Screen 11(8):669-676
23. Mestres J, Gregori-Puigjané E, Valverde S, Solé RV (2009) The topology of drug-target interaction networks: implicit dependence on drug properties and target families. Mol Biosyst 5(9):1051-1057
24. Nidhi GM, Davies JW, Jenkins JL (2006) Prediction of biological targets for compounds using multiple-category bayesian models trained on chemogenomics databases. J Chem Inf Model 46(3):1124-1133
25. Heikamp K, Bajorath J (2013) The future of virtual compound screening. Chem Biol Drug Des 81(1):33-40
26. ChEMBL (2014)
27. Hausmann H, Richters A, Kreienkamp HJ, Meyerhof W, Mattes H, Lederis K, Zwiers H, Richter D (1996) Mutational analysis and molecular modeling of the nonapeptide hormone binding domains of the [arg8]vasotocin receptor. Proc Natl Acad Sci USA 93(14):6907-6912
28. Koutsoukas A, Lowe R, KalantarMotamedi Y, Mussa HY, Klaffke W, Mitchell JB, Glen RC, Bender A (2013) In silico target predictions: defining a benchmarking data set and comparison of performance of the multiclass naive bayes and parzen-rosenblatt window. J Chem Inf Model 53(8):1957-1966
29. Johnson MA, Maggiora GM (1990) Concepts and applications of molecular similarity. Wiley, New York
30. Rogers D, Hahn M (2010) Extended-connectivity fingerprints. J Chem Inf Model 50(5):742-754
31. Brown MHRD, Varma-O'Brien S, Rogers D (2006) Cheminformatics analysis and learning in a data pipelining environment. Mol Drivers 10:283-299
32. Baldi P, Benz RW, Hirschberg D, Swamidass SJ (2007) Lossless compression of chemical fingerprints using integer entropy codes improves storage and retrieval. J Chem Inf Model 47(6):2098-2109
33. Tanimoto TT. IBM Internal Report 17th (November 1957)
34. Hert J, Willett P, Wilton DJ, Acklin P, Azzaoui K, Jacoby E, Schuffenhauer A (2004) A. comparison of fingerprint-based methods for virtual screening using multiple bioactive reference structures. J Chem Inf Model 44:1177-1185
35. Hert J, Willett P, Wilton DJ, Acklin P, Azzaoui K, Jacoby E, Schuffenhauer A (2005) Enhancing the effectiveness of similarity-based virtual screening using nearest-neighbor information. J Med Chem 48:7049-7054
36. Nasr RJ, Swamidass SJ, Baldi PF (2009) Large scale study of multiple-molecule queries. J Cheminf 1:7
37. Geppert H, Horváth T, Gärtner T, Wrobel S, Bajorath J (2008) Support-vector-machine-based ranking significantly improves the effectiveness of similarity searching using 2d fingerprints and multiple reference compounds. J Chem Inf Model 48(4):742-746
38. Mahé P, Ralaivola L, Stoven V, Vert J-P (2006) The pharmacophore kernel for virtual screening with support vector machines. J Chem Inf Model 46(5):2003-2014
39. Swamidass SJ, Chen J, Bruand J, Phung P, Ralaivola L, Baldi P (2005) Kernels for small molecules and the prediction of mutagenicity, toxicity, and anti-cancer activity. Bioinformatics 21(Supplement 1):359-368 (Proceedings of the 2005 ISMB Conference)
40. Collobert R, Bengio S (2001) Svmtorch: support vector machines for large-scale regression problems. J Mach Learn Res 1:143-160
41. Breiman L (2001) Random forests. Mach Learn 45(1):5-32
42. Palmer DS, O'Boyle NM, Glen RC, Mitchell JB (2007) Random forest models to predict aqueous solubility. J Chem Inf Model 47(1):150-158
43. Zhang Q-Y, Aires-de-Sousa J (2007) Random forest prediction of mutagenicity from empirical physicochemical descriptors. J Chem Inf Model 47(1):1-8
44. Harvey AL (2008) Natural products in drug discovery. Drug Discov Today 13(19):894-901
45. Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP (2003) Random forest: a classification and regression tool for compound classification and qsar modeling. J Chem Inf Model 43(6):1947-1958
46. Scikit-Learn (2013)
47. Baldi P, Brunak S (2001) Bioinformatics: the machine learning approach, 2nd edn. MIT Press, Cambridge
48. Dybowski R, Roberts SJ (2001) Confidence intervals and prediction intervals for feed-forward neural networks. Clin Appl Artif Neural Netw:298-326
49. Bradley AP (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recogn 30(7):1145-1159
50. Parker CN (2005) Mcmaster university data-mining and docking competition computational models on the catwalk. J Biomol Screen 10(7):647-648
51. 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
52. Wale N, Karypis G (2009) Target fishing for chemical compounds using target-ligand activity data and ranking based methods. J Chem Inf Model 49(10):2190-2201
53. Zaretzki J, Matlock M, Swamidass SJ (2013) Xenosite: accurately predicting cyp-mediated sites of metabolism with neural networks. J Chem Inf Model 53(12):3373-3383
54. Hinselmann G, Rosenbaum L, Jahn A, Fechner N, Ostermann C, Zell A (2011) Large-scale learning of structure- activity relationships using a linear support vector machine and problem-specific metrics. J Chem Inf Model 51(2):203-213
55. Plewczynski D, von Grotthuss M, Spieser H, Stephane A, Rychewski L, Wyrwicz LS, Ginalski K, Koch U (2007) Target specific compound identification using a support vector machine. Comb Chem High Throughput Screen 10(3):189-196
56. Seifert M, Kraus J, Kramer B (2007) Virtual high-throughput screening of molecular databases. Curr Opin Drug Discov Dev 10(3):298-307
57. Lusci A, Pollastri G, Baldi P (2013) Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules. J Chem Inf Model 53(7):1563-1575