메뉴 건너뛰기




Volumn 3, Issue DEC, 2015, Pages

Prediction of compounds activity in nuclear receptor signaling and stress pathway assays using machine learning algorithms and low-dimensional molecular descriptors

Author keywords

Machine learning; Molecular descriptors; Molecular fingerprints; Tox21 Data Challenge 2014; Toxicity prediction

Indexed keywords


EID: 85060344810     PISSN: None     EISSN: 2296665X     Source Type: Journal    
DOI: 10.3389/fenvs.2015.00077     Document Type: Article
Times cited : (32)

References (38)
  • 4
    • 84907834013 scopus 로고    scopus 로고
    • Towards global QSAR model building for acute toxicity: munro database case study
    • Chavan, S., Nicholls, I. A., Karlsson, B. C., Rosengren, A. M., Ballabio, D., Consonni, V., et al. (2014). Towards global QSAR model building for acute toxicity: munro database case study. Int. J. Mol. Sci. 15, 18162-18174. doi: 10.3390/ijms151018162
    • (2014) Int. J. Mol. Sci. , vol.15 , pp. 18162-18174
    • Chavan, S.1    Nicholls, I.A.2    Karlsson, B.C.3    Rosengren, A.M.4    Ballabio, D.5    Consonni, V.6
  • 5
    • 84881360676 scopus 로고    scopus 로고
    • In silico ADMET prediction: recent advances, current challenges and future trends
    • Cheng, F., Li, W., Liu, G., and Tang, Y. (2013). In silico ADMET prediction: recent advances, current challenges and future trends. Curr. Top. Med. Chem. 13, 1273-1289. doi: 10.2174/15680266113139990033
    • (2013) Curr. Top. Med. Chem. , vol.13 , pp. 1273-1289
    • Cheng, F.1    Li, W.2    Liu, G.3    Tang, Y.4
  • 6
    • 84876742787 scopus 로고    scopus 로고
    • In silico quantitative structure toxicity relationship of chemical compounds: some case studies
    • Deeb, O., and Goodarzi, M. (2012). In silico quantitative structure toxicity relationship of chemical compounds: some case studies. Curr. Drug Saf. 7, 289-297. doi: 10.2174/157488612804096533
    • (2012) Curr. Drug Saf. , vol.7 , pp. 289-297
    • Deeb, O.1    Goodarzi, M.2
  • 8
    • 84918779199 scopus 로고    scopus 로고
    • Machine-learning techniques applied to antibacterial drug discovery
    • Durrant, J. D., and Amaro, R. E. (2015). Machine-learning techniques applied to antibacterial drug discovery. Chem. Biol. Drug Des. 85, 14-21. doi: 10.1111/cbdd.12423
    • (2015) Chem. Biol. Drug Des. , vol.85 , pp. 14-21
    • Durrant, J.D.1    Amaro, R.E.2
  • 9
    • 79960608914 scopus 로고    scopus 로고
    • Modernizing toxicity tests
    • Erickson, B. E. (2011). Modernizing toxicity tests. Chem. Eng. News 89, 25-26. doi: 10.1021/cen-v089n029.p025
    • (2011) Chem. Eng. News , vol.89 , pp. 25-26
    • Erickson, B.E.1
  • 10
    • 0035412779 scopus 로고    scopus 로고
    • Can 3D structural parameters be predicted from 2D (topological) molecular descriptors? J
    • Estrada, E., Molina, E., and Perdomo-Lopez, I. (2001). Can 3D structural parameters be predicted from 2D (topological) molecular descriptors? J. Chem. Inf. Comput. Sci. 41, 1015-1021. doi: 10.1021/ci000170v
    • (2001) Chem. Inf. Comput. Sci. , vol.41 , pp. 1015-1021
    • Estrada, E.1    Molina, E.2    Perdomo-Lopez, I.3
  • 11
    • 84924959832 scopus 로고    scopus 로고
    • Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients
    • Freitas, A. A., Limbu, K., and Ghafourian, T. (2015). Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients. J. Cheminform. 7, 6. doi: 10.1186/s13321-015-0054-x
    • (2015) J. Cheminform. , vol.7 , pp. 6
    • Freitas, A.A.1    Limbu, K.2    Ghafourian, T.3
  • 12
    • 84855218909 scopus 로고    scopus 로고
    • Theoretical study of GSK-3 alpha: neural networks QSAR studies for the design of new inhibitors using 2D descriptors
    • Garcia, I., Fall, Y., Garcia-Mera, X., and Prado-Prado, F. (2011). Theoretical study of GSK-3 alpha: neural networks QSAR studies for the design of new inhibitors using 2D descriptors. Mol. Divers. 15, 947-955. doi: 10.1007/s11030-011-9325-2
    • (2011) Mol. Divers. , vol.15 , pp. 947-955
    • Garcia, I.1    Fall, Y.2    Garcia-Mera, X.3    Prado-Prado, F.4
  • 14
    • 84904350064 scopus 로고    scopus 로고
    • Profiling of the Tox21 10K compound library for agonists and antagonists of the estrogen receptor alpha signaling pathway
    • Huang, R. L., Sakamuru, S., Martin, M. T., Reif, D. M., Judson, R. S., Houck, K. A., et al. (2014). Profiling of the Tox21 10K compound library for agonists and antagonists of the estrogen receptor alpha signaling pathway. Sci. Rep. 4, 1664-1673. doi: 10.1038/srep05664
    • (2014) Sci. Rep. , vol.4 , pp. 1664-1673
    • Huang, R.L.1    Sakamuru, S.2    Martin, M.T.3    Reif, D.M.4    Judson, R.S.5    Houck, K.A.6
  • 15
    • 77951675220 scopus 로고    scopus 로고
    • In vitro screening of environmental chemicals for targeted testing prioritization: the toxcast project
    • Judson, R. S., Houck, K. A., Kavlock, R. J., Knudsen, T. B., Martin, M. T., Mortensen, H. M., et al. (2010). In vitro screening of environmental chemicals for targeted testing prioritization: the toxcast project. Environ. Health Perspect. 118, 485-492. doi: 10.1289/ehp.0901392
    • (2010) Environ. Health Perspect. , vol.118 , pp. 485-492
    • Judson, R.S.1    Houck, K.A.2    Kavlock, R.J.3    Knudsen, T.B.4    Martin, M.T.5    Mortensen, H.M.6
  • 16
    • 35349025783 scopus 로고    scopus 로고
    • Systematic reviews of animal experiments demonstrate poor human clinical and toxicological utility
    • Knight, A. (2007). Systematic reviews of animal experiments demonstrate poor human clinical and toxicological utility. Altern. Lab. Anim. 35, 641-659. Available online at: http://www.atla.org.uk/systematic-reviews-of-animal-experiments-demonstrate-poor-human-clinical-and-toxicological-utility/
    • (2007) Altern. Lab. Anim. , vol.35 , pp. 641-659
    • Knight, A.1
  • 17
    • 4344645978 scopus 로고    scopus 로고
    • Can the pharmaceutical industry reduce attrition rates? Nat
    • Kola, I., and Landis, J. (2004). Can the pharmaceutical industry reduce attrition rates? Nat. Rev. Drug Discov. 3, 711-715. doi: 10.1038/nrd1470
    • (2004) Rev. Drug Discov. , vol.3 , pp. 711-715
    • Kola, I.1    Landis, J.2
  • 18
    • 85042116457 scopus 로고    scopus 로고
    • Machine learning for drug design
    • Liu, Y. (2015). Machine learning for drug design. Int. J. Comput. Inf. Technol. 4, 1-7.
    • (2015) Int. J. Comput. Inf. Technol. , vol.4 , pp. 1-7
    • Liu, Y.1
  • 19
    • 84903693496 scopus 로고    scopus 로고
    • Integrative approaches for predicting in vivo effects of chemicals from their structural descriptors and the results of short-term biological assays
    • Low, Y. S., Sedykh, A. Y., Rusyn, I., and Tropsha, A. (2014). Integrative approaches for predicting in vivo effects of chemicals from their structural descriptors and the results of short-term biological assays. Curr. Top. Med. Chem. 14, 1356-1364. doi: 10.2174/1568026614666140506121116
    • (2014) Curr. Top. Med. Chem. , vol.14 , pp. 1356-1364
    • Low, Y.S.1    Sedykh, A.Y.2    Rusyn, I.3    Tropsha, A.4
  • 20
    • 80052549683 scopus 로고    scopus 로고
    • Predictive model of rat reproductive toxicity from toxcast high throughput screening
    • Martin, M. T., Knudsen, T. B., Reif, D. M., Houck, K. A., Judson, R. S., Kavlock, R. J., et al. (2011). Predictive model of rat reproductive toxicity from toxcast high throughput screening. Biol. Reprod. 85, 327-339. doi: 10.1095/biolreprod.111.090977
    • (2011) Biol. Reprod. , vol.85 , pp. 327-339
    • Martin, M.T.1    Knudsen, T.B.2    Reif, D.M.3    Houck, K.A.4    Judson, R.S.5    Kavlock, R.J.6
  • 21
    • 84910109687 scopus 로고    scopus 로고
    • An overview of data mining algorithms in drug induced toxicity prediction
    • Omer, A., Singh, P., Yadav, N. K., and Singh, R. K. (2014). An overview of data mining algorithms in drug induced toxicity prediction. Mini Rev. Med. Chem. 14, 345-354. doi: 10.2174/1389557514666140219110244
    • (2014) Mini Rev. Med. Chem. , vol.14 , pp. 345-354
    • Omer, A.1    Singh, P.2    Yadav, N.K.3    Singh, R.K.4
  • 22
    • 0036985975 scopus 로고    scopus 로고
    • On the information content of 2D and 3D descriptors for QSAR
    • Oprea, T. I. (2002). On the information content of 2D and 3D descriptors for QSAR. J. Braz. Chem. Soc. 13, 811-815. doi: 10.1590/s0103-50532002000600013
    • (2002) J. Braz. Chem. Soc. , vol.13 , pp. 811-815
    • Oprea, T.I.1
  • 23
    • 84927735077 scopus 로고    scopus 로고
    • Massively multitask networks for drug discovery
    • arXiv:1502.02072
    • Ramsundar, B., Kearnes, S., Riley, P., Webster, D., Konerding, D., and Pande, V. (2015). Massively multitask networks for drug discovery. arXiv:1502.02072. Available online at: http://arxiv.org/abs/1502.02072
    • (2015)
    • Ramsundar, B.1    Kearnes, S.2    Riley, P.3    Webster, D.4    Konerding, D.5    Pande, V.6
  • 24
    • 84922918827 scopus 로고    scopus 로고
    • Contribution of new technologies to characterization and prediction of adverse effects
    • Rouquié, D., Heneweer, M., Botham, J., Ketelslegers, H., Markell, L., Pfister, T., et al. (2015). Contribution of new technologies to characterization and prediction of adverse effects. Crit. Rev. Toxicol. 45, 172-183. doi: 10.3109/10408444.2014.986054
    • (2015) Crit. Rev. Toxicol. , vol.45 , pp. 172-183
    • Rouquié, D.1    Heneweer, M.2    Botham, J.3    Ketelslegers, H.4    Markell, L.5    Pfister, T.6
  • 25
    • 84925783987 scopus 로고    scopus 로고
    • A review on principles, theory and practices of 2D-QSAR
    • Roy, K., and Das, R. N. (2014). A review on principles, theory and practices of 2D-QSAR. Curr. Drug Metab. 15, 346-379. doi: 10.2174/1389200215666140908102230
    • (2014) Curr. Drug Metab. , vol.15 , pp. 346-379
    • Roy, K.1    Das, R.N.2
  • 26
    • 61849085398 scopus 로고    scopus 로고
    • QSAR Studies of CYP2D6 inhibitor aryloxypropanolamines using 2D and 3D descriptors
    • Roy, P. P., and Roy, K. (2009). QSAR Studies of CYP2D6 inhibitor aryloxypropanolamines using 2D and 3D descriptors. Chem. Biol. Drug Des. 73, 442-455. doi: 10.1111/j.1747-0285.2009.00791.x
    • (2009) Chem. Biol. Drug Des. , vol.73 , pp. 442-455
    • Roy, P.P.1    Roy, K.2
  • 27
    • 60949086792 scopus 로고    scopus 로고
    • Are animal models predictive for humans?
    • Shanks, N., Greek, R., and Greek, J. (2009). Are animal models predictive for humans? Philos. Ethics Hum. Med. 4:2. doi: 10.1186/1747-5341-4-2
    • (2009) Philos. Ethics Hum. Med. , vol.4
    • Shanks, N.1    Greek, R.2    Greek, J.3
  • 28
    • 84864750654 scopus 로고    scopus 로고
    • A three-stage algorithm to make toxicologically relevant activity calls from quantitative high throughput screening data
    • Shockley, K. R. (2012). A three-stage algorithm to make toxicologically relevant activity calls from quantitative high throughput screening data. Environ. Health Perspect. 120, 1107-1115. doi: 10.1289/ehp.1104688
    • (2012) Environ. Health Perspect. , vol.120 , pp. 1107-1115
    • Shockley, K.R.1
  • 29
    • 80053516123 scopus 로고    scopus 로고
    • Predictive models of prenatal developmental toxicity from toxcast high-throughput screening data
    • Sipes, N. S., Martin, M. T., Reif, D. M., Kleinstreuer, N. C., Judson, R. S., Singh, A. V., et al. (2011). Predictive models of prenatal developmental toxicity from toxcast high-throughput screening data. Toxicol. Sci. 124, 109-127. doi: 10.1093/toxsci/kfr220
    • (2011) Toxicol. Sci. , vol.124 , pp. 109-127
    • Sipes, N.S.1    Martin, M.T.2    Reif, D.M.3    Kleinstreuer, N.C.4    Judson, R.S.5    Singh, A.V.6
  • 30
    • 84883339723 scopus 로고    scopus 로고
    • A multidimensional analysis of machine learning methods performance in the classification of bioactive compounds
    • Smusz, S., Kurczab, R., and Bojarski, A. J. (2013). A multidimensional analysis of machine learning methods performance in the classification of bioactive compounds. Chemometr. Intel. Lab. Syst. 128, 89-100. doi: 10.1016/j.chemolab.2013.08.003
    • (2013) Chemometr. Intel. Lab. Syst. , vol.128 , pp. 89-100
    • Smusz, S.1    Kurczab, R.2    Bojarski, A.J.3
  • 31
    • 84923343419 scopus 로고    scopus 로고
    • Rule-based classification models of molecular autofluorescence
    • Su, B. H., Tu, Y. S., Lin, O. A., Harn, Y. C., Shen, M. Y., and Tseng, Y. J. (2015). Rule-based classification models of molecular autofluorescence. J. Chem. Inf. Model. 55, 434-445. doi: 10.1021/ci5007432
    • (2015) J. Chem. Inf. Model. , vol.55 , pp. 434-445
    • Su, B.H.1    Tu, Y.S.2    Lin, O.A.3    Harn, Y.C.4    Shen, M.Y.5    Tseng, Y.J.6
  • 32
    • 84862775836 scopus 로고    scopus 로고
    • Paradigm shift in toxicity testing and modeling
    • Sun, H. M., Xia, M. H., Austin, C. P., and Huang, R. L. (2012). Paradigm shift in toxicity testing and modeling. Aaps J. 14, 473-480. doi: 10.1208/s12248-012-9358-1
    • (2012) Aaps J. , vol.14 , pp. 473-480
    • Sun, H.M.1    Xia, M.H.2    Austin, C.P.3    Huang, R.L.4
  • 33
    • 84879599189 scopus 로고    scopus 로고
    • Improving the human hazard characterization of chemicals: a Tox21 update
    • Tice, R. R., Austin, C. P., Kavlock, R. J., and Bucher, J. R. (2013). Improving the human hazard characterization of chemicals: a Tox21 update. Environ. Health Perspect. 121, 756-765. doi: 10.1289/ehp.1205784
    • (2013) Environ. Health Perspect. , vol.121 , pp. 756-765
    • Tice, R.R.1    Austin, C.P.2    Kavlock, R.J.3    Bucher, J.R.4
  • 35
    • 84958640223 scopus 로고    scopus 로고
    • Toxicity prediction using deep learning
    • arXiv.
    • Unterthiner, T., Mayr, A., Klambauer, G., and Hochreiter, S. (2015). Toxicity prediction using deep learning. arXiv. Available online at: http://arxiv.org/abs/1503.01445
    • (2015)
    • Unterthiner, T.1    Mayr, A.2    Klambauer, G.3    Hochreiter, S.4
  • 36
    • 84870494675 scopus 로고    scopus 로고
    • Predictive computational toxicology to support drug safety assessment
    • Valerio, L. G. Jr. (2013). Predictive computational toxicology to support drug safety assessment. Methods Mol. Biol. 930, 341-354. doi: 10.1007/978-1-62703-059-5_15
    • (2013) Methods Mol. Biol. , vol.930 , pp. 341-354
    • Valerio, L.G.Jr.1
  • 38
    • 84925436995 scopus 로고    scopus 로고
    • New publicly available chemical query language, CSRML, to support chemotype representations for application to data mining and modeling
    • Yang, C., Tarkhov, A., Marusczyk, J., Bienfait, B., Gasteiger, J., Kleinoeder, T., et al. (2015). New publicly available chemical query language, CSRML, to support chemotype representations for application to data mining and modeling. J. Chem. Inf. Model. 55, 510-528. doi: 10.1021/ci500667v
    • (2015) J. Chem. Inf. Model. , vol.55 , pp. 510-528
    • Yang, C.1    Tarkhov, A.2    Marusczyk, J.3    Bienfait, B.4    Gasteiger, J.5    Kleinoeder, T.6


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.