Chemistry

Exploiting machine studying for end-to-end drug discovery and growth


1.

Butler, L. D. et al. Present nonclinical testing paradigms in assist of secure medical trials: an IQ Consortium DruSafe perspective. Regul. Toxicol. Pharmacol. 87, S1–S15 (2017).

2.

Kola, I. & Landis, J. Can the pharmaceutical trade scale back attrition charges. Nat. Rev. Drug. Discov. three, 711–715 (2004).

three.

Bowes, J. et al. Lowering safety-related drug attrition: the usage of in vitro pharmacological profiling. Nat. Rev. Drug. Discov. 11, 909–922 (2012).

four.

DiMasi, J. A., Grabowski, H. G. & Hansen, R. W. Innovation within the pharmaceutical trade: new estimates of R&D prices. J. Well being Econ. 47, 20–33 (2016).

5.

Kenna, J. G. Human biology-based drug security analysis: scientific rationale, present standing and future challenges. Knowledgeable Opin. Drug Metab. Toxicol. 13, 567–574 (2017).

6.

Gayvert, Okay. M., Madhukar, N. S. & Elemento, O. An information-driven strategy to predicting successes and failures of medical trials. Cell Chem. Biol. 23, 1294–1301 (2016).

7.

Wagner, J. A. et al. Utility of a dynamic map for studying, speaking, navigating, and bettering therapeutic growth. Clin. Transl. Sci. 11, 166–174 (2018).

eight.

Paul, S. M. et al. The best way to enhance R&D productiveness: the pharmaceutical trade’s grand problem. Nat. Rev. Drug Discov. 9, 203–214 (2010).

9.

Zhavoronkov, A. Synthetic intelligence for drug discovery, biomarker growth, and technology of novel chemistry. Mol. Pharm. 15, 4311–4313 (2018).

10.

Davies, D. W., Butler, Okay. T., Isayev, O. & Walsh, A. Supplies discovery by chemical analogy: function of oxidation states in construction prediction. Faraday Talk about. 211, 553–568 (2018).

11.

Drouin, A. et al. Predictive computational phenotyping and biomarker discovery utilizing reference-free genome comparisons. BMC Genom. 17, 754 (2016).

12.

Chen, H., Engkvist, O., Wang, Y., Olivecrona, M. & Blaschke, T. The rise of deep studying in drug discovery. Drug Discov. At present. 23, 1241–1250 (2018).

13.

Ekins, S. et al. Machine studying fashions and pathway genome information base for trypanosoma cruzi drug discovery. PLoS Negl. Trop. Dis. 9, e0003878 (2015).

14.

Lampa, S. et al. Predicting off-target binding profiles with confidence utilizing conformal prediction. Entrance. Pharmacol. 9, 1256 (2018).

15.

Reker, D., Rodrigues, T., Schneider, P. & Schneider, G. Figuring out the macromolecular targets of de novo-designed chemical entities by self-organizing map consensus. Proc. Natl Acad. Sci. USA 111, 4067–4072 (2014).

16.

Kim, S. et al. PubChem substance and compound databases. Nucleic Acids Res. 44, D1202–1213 (2016).

17.

Gaulton, A. et al. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 40, D1100–1107 (2012).

18.

Mayr, A. et al. Giant-scale comparability of machine studying strategies for drug goal prediction on ChEMBL. Chem. Sci. 9, 5441–5451 (2018).

19.

Clark, A. M., Williams, A. J. & Ekins, S. Machines first, people second: on the significance of algorithmic interpretation of open chemistry information. J. Cheminform. 7, 9 (2015).

20.

Christianini, N. & Shawe-Taylor, J. Assist Vector Machines and Different Kernel-Based mostly Studying Strategies (Cambridge Univ. Press, 2000).

21.

Shen, M., Xiao, Y., Golbraikh, A., Gombar, V. Okay. & Tropsha, A. Growth and validation of Okay-nearest neighbour QSPR fashions of metabolic stability of drug candidates. J. Med. Chem. 46, 3013–3020 (2003).

22.

Bender, A. et al. Evaluation of pharmacology information and the prediction of opposed drug reactions and off-target results from chemical construction. ChemMedChem 2, 861–873 (2007).

23.

Susnow, R. G. & Dixon, S. L. Use of sturdy classification strategies for the prediction of human cytochrome P450 2D6 inhibition. J. Chem. Inf. Comput. Sci. 43, 1308–1315 (2003).

24.

Mitchell, J. B. Machine studying strategies in chemoinformatics. Wiley Interdiscip. Rev. Comput. Mol. Sci. four, 468–481 (2014).

25.

Schmidhuber, J. Deep studying in neural networks: an summary. Neural Netw. 61, 85–117 (2015).

26.

Aliper, A. et al. Deep studying functions for predicting pharmacological properties of medicine and drug repurposing utilizing transcriptomic information. Mol. Pharm. 13, 2524–2530 (2016).

27.

Ma, J., Sheridan, R. P., Liaw, A., Dahl, G. E. & Svetnik, V. Deep neural nets as a technique for quantitative structure-activity relationships. J. Chem. Inf. Mannequin. 55, 263–274 (2015).

28.

Wu, Okay., Zhao, Z., Wang, R. & Wei, G.-W. TopP–S: Persistent homology-based multi-task deep neural networks for simultaneous predictions of partition coefficient and aqueous solubility. J. Comput. Chem. 39, 1444–1454 (2018).

29.

Wen, M. et al. Deep-learning-based drug-target interplay prediction. J. Proteome Res. 16, 1401–1409 (2017).

30.

Ekins, S. The subsequent period: Deep studying in pharmaceutical analysis. Pharm. Res. 33, 2594–2603 (2016).

31.

Wu, Z. et al. MoleculeNet: a benchmark for molecular machine studying. Chem. Sci. 9, 513–530 (2018).

32.

Altae-Tran, H., Ramsundar, B., Pappu, A. S. & Pande, V. Low information drug discovery with one-shot studying. ACS Cent. Sci. three, 283–293 (2017).

33.

Kadurin, A., Nikolenko, S., Khrabrov, Okay., Aliper, A. & Zhavoronkov, A. druGAN: a sophisticated generative adversarial autoencoder mannequin for de novo technology of recent molecules with desired molecular properties in silico. Mol. Pharm. 14, 3098–3104 (2017).

34.

Butler, Okay. T., Davies, D. W., Cartwright, H., Isayev, O. & Walsh, A. Machine studying for molecular and supplies science. Nature 559, 547–555 (2018).

35.

Rifaioglu, A. S. et al. Current functions of deep studying and machine intelligence on in silico drug discovery: strategies, instruments and databases. Transient Bioinform. https://doi.org/10.1093/bib/bby061 (2018).

36.

Popova, M., Isayev, O. & Tropsha, A. Deep reinforcement studying for de novo drug design. Sci. Adv. four, eaap7885 (2018).

37.

Putin, E. et al. Adversarial threshold neural pc for molecular de novo design. Mol. Pharm. 15, 4386–4397 (2018).

38.

McGaughey, G. B. et al. Comparability of topological, form, and docking strategies in digital screening. J. Chem. Inf. Mannequin. 47, 1504–1519 (2007).

39.

Johnson, Okay. W. et al. Enabling precision cardiology by multiscale biology and methods drugs. JACC Fundamental Transl. Sci. 2, 311–327 (2017).

40.

Rajkomar, A. et al. Scalable and correct deep studying with digital well being data. npj Digit. Med. 1, 18 (2018).

41.

Ekins, S. et al. Machine studying fashions determine molecules lively in opposition to Ebola virus in vitro. F1000Analysis four, 1091 (2015).

42.

Perryman, A. L., Stratton, T. P., Ekins, S. & Freundlich, J. S. Predicting mouse liver microsomal stability with “pruned’ machine studying fashions and public information. Pharm. Res. 33, 433–449 (2015).

43.

Clark, A. M. et al. Open supply Bayesian fashions: 1. Utility to ADME/Tox and drug discovery datasets. J. Chem. Inf. Mannequin. 55, 1231–1245 (2015).

44.

Perryman, A. L. et al. Naive Bayesian fashions for vero cell cytotoxicity. Pharm. Res. 35, 170 (2018).

45.

Sandoval, P. J., Zorn, Okay. M., Clark, A. M., Ekins, S. & Wright, S. H. Evaluation of substrate dependent ligand interactions on the natural cation transporter OCT2 utilizing six mannequin substrates. Mol. Pharmacol. 94, 1057–1068 (2018).

46.

Russo, D. P., Zorn, Okay. M., Clark, A. M., Zhu, H. & Ekins, S. Evaluating a number of machine studying algorithms and metrics for estrogen receptor binding prediction. Mol. Pharm. 15, 4361–4370 (2018).

47.

Lusci, A., Pollastri, G. & Baldi, P. Deep architectures and deep studying in chemoinformatics: the prediction of aqueous solubility for drug-like molecules. J. Chem. Inf. Mannequin. 53, 1563–1575 (2013).

48.

Stratton, T. P. et al. Addressing the metabolic stability of antituberculars by machine studying. ACS Med. Chem. Lett. eight, 1099–1104 (2017).

49.

Korotcov, A., Tkachenko, V., Russo, D. P. & Ekins, S. Comparability of deep studying with a number of machine studying strategies and metrics utilizing numerous drug discovery datasets. Mol. Pharm. 14, 4462–4475 (2018).

50.

Lenselink, E. B. et al. Past the hype: deep neural networks outperform established strategies utilizing a ChEMBL bioactivity benchmark set. J. Cheminform. 9, 45 (2017).

51.

Koutsoukas, A., Monaghan, Okay. J., Li, X. & Huan, J. Deep-learning: investigating deep neural networks hyper-parameters and comparability of efficiency to shallow strategies for modeling bioactivity information. J. Cheminform. 9, 42 (2017).

52.

Lane, T. et al. Evaluating and validating machine studying fashions for mycobacterium tuberculosis drug discovery. Mol. Pharm. 15, 4346–4360 (2018).

53.

Ridley, D. B. Priorities for the precedence evaluate voucher. Am. J. Trop. Med. Hyg. 96, 14–15 (2017).

54.

Ekins, S. et al. Bayesian fashions leveraging bioactivity and cytotoxicity info for drug discovery. Chem. Biol. 20, 370–378 (2013).

55.

Hernandez, H. W. et al. Excessive throughput and computational repurposing for uncared for ailments. Pharm. Res. 36, 27 (2018).

56.

Ekins, S. Industrializing uncommon illness remedy discovery and growth. Nat. Biotechnol. 35, 117–118 (2017).

57.

Ekins, S. & Perlstein, E. O. Doing all of it – how households are reshaping uncommon illness analysis. Pharm. Res. 35, 192 (2018).

58.

Chen, B. & Altman, R. B. Alternatives for growing therapies for uncommon genetic ailments: give attention to gain-of-function and allostery. Orphanet. J. Uncommon Dis. 12, 61 (2017).

59.

Trujillano, D. et al. A complete world genotype-phenotype database for uncommon ailments. Mol. Genet. Genomic Med. 5, 66–75 (2017).

60.

Thompson, R. et al. RD-Join: an built-in platform connecting databases, registries, biobanks and medical bioinformatics for uncommon illness analysis. J. Gen. Intern. Med. 29, 780–787 (2014).

61.

Rath, A. et al. Illustration of uncommon ailments in well being info methods: the Orphanet strategy to serve a variety of finish customers. Hum. Mutat. 33, 803–808 (2012).

62.

Uncommon Illness InfoHub https://rarediseases.oscar.ncsu.edu (2018).

63.

Fleming, N. How synthetic intelligence is altering drug discovery. Nature 557, 55–57 (2018).

64.

Chuang, Okay. V. & Keiser, M. J. Adversarial controls for scientific machine studying. ACS Chem. Biol. 13, 2819–2821 (2018).

65.

Marchese Robinson, R. L., Palczewska, A., Palczewski, J. & Kidley, N. Comparability of the predictive efficiency and interpretability of random forest and linear fashions on benchmark information units. J. Chem. Inf. Mannequin. 57, 1773–1792 (2017).

66.

Jones, D. E., Ghandehari, H. & Facelli, J. C. A evaluate of the functions of information mining and machine studying for the prediction of biomedical properties of nanoparticles. Comput. Strategies Applications Biomed. 132, 93–103 (2016).

67.

Shamay, Y. et al. Quantitative self-assembly prediction yields focused nanomedicines. Nat. Mater. 17, 361–368 (2018).

68.

de la Iglesia, D. et al. A machine studying strategy to determine medical trials involving nanodrugs and nanodevices from ClinicalTrials.gov. PLOS ONE 9, e110331 (2014).

69.

Tropsha, A., Mills, Okay. C. & Hickey, A. J. Reproducibility, sharing and progress in nanomaterial databases. Nat. Nanotechnol. 12, 1111–1114 (2017).

70.

Baker, N. C., Ekins, S., Williams, A. J. & Tropsha, A. A bibliometric evaluate of drug repurposing. Drug Discov. At present 23, 661–672 (2018).

71.

Lamb, J. et al. The connectivity map: utilizing gene-expression signatures to attach small molecules, genes, and illness. Science 313, 1929–1935 (2006).

72.

Dudley, J. T. et al. Computational repositioning of the anticonvulsant topiramate for inflammatory bowel illness. Sci. Transl. Med. three, 96ra76 (2011).

73.

Schadt, E. E., Buchanan, S., Brennand, Okay. J. & Service provider, Okay. M. Evolving towards a human-cell based mostly and multiscale strategy to drug discovery for CNS issues. Entrance. Pharmacol. 5, 252 (2014).

74.

Napolitano, F. et al. Drug repositioning: a machine-learning strategy by information integration. J. Cheminform. 5, 30 (2013).

75.

Cruz, S. et al. In silico HCT116 human colon most cancers cell-based fashions en path to the invention of lead-like anticancer medication. Biomolecules eight, 56 (2018).

76.

Fröhlich, H. et al. From hype to actuality: information science enabling personalised drugs. BMC Med. 16, 150 (2018).

77.

Chen, R., Liu, X., Jin, S., Lin, J. & Liu, J. Machine studying for drug-target interplay prediction. Molecules 23, 2208 (2018).

78.

Lin, J. & Wong, Okay. C. Off-target predictions in CRISPR-Cas9 gene enhancing utilizing deep studying. Bioinformatics 34, i656–i663 (2018).

79.

Chang, Y. et al. Most cancers drug response profile scan (CDRscan): a deep studying mannequin that predicts drug effectiveness from most cancers genomic signature. Sci. Rep. eight, 8857 (2018).

80.

Boland, M. R., Polubriaginof, F. & Tatonetti, N. P. Growth of A machine studying algorithm to categorise medication of unknown fetal impact. Sci. Rep. 7, 12839 (2017).

81.

Rannals, M. D. et al. Psychiatric danger gene transcription issue four regulates intrinsic excitability of prefrontal neurons through repression of SCN10a and KCNQ1. Neuron 90, 43–55 (2016).

82.

Zang, Q. et al. In silico prediction of physicochemical properties of environmental chemical substances utilizing molecular fingerprints and machine studying. J. Chem. Inf. Mannequin. 57, 36–49 (2017).

83.

Hong, H., Thakkar, S., Chen, M. & Tong, W. Growth of resolution forest fashions for prediction of drug-induced liver harm in people utilizing a big set of FDA-approved medication. Sci. Rep. 7, 17311 (2017).

84.

Korotcov, A., Tkachenko, V., Russo, D. P. & Ekins, S. Comparability of deep studying with a number of machine studying strategies and metrics utilizing numerous drug discovery information units. Mol. Pharm. 14, 4462–4475 (2017).

85.

Wang, W., Kim, M. T., Sedykh, A. & Zhu, H. Growing enhanced blood-brain barrier permeability fashions: integrating exterior bio-assay information in QSAR modeling. Pharm. Res. 32, 3055–3065 (2015).

86.

Baba, H., Takahara, J., Yamashita, F. & Hashida, M. Modeling and prediction of solvent impact on human pores and skin permeability utilizing assist vector regression and random forest. Pharm. Res. 32, 3604–3617 (2015).

87.

Xu, C. et al. In silico prediction of chemical Ames mutagenicity. J. Chem. Inf. Mannequin. 52, 2840–2847 (2012).

88.

Huang, W. et al. Prediction of human clearance based mostly on animal information and molecular properties. Chem. Biol. Drug Des. 86, 990–997 (2015).

89.

Basant, N., Gupta, S. & Singh, Okay. P. QSAR modeling for predicting reproductive toxicity of chemical substances in rats for regulatory functions. Toxicol. Res. 5, 1029–1038 (2016).

90.

Alhalaweh, A. et al. Computational predictions of glass-forming means and crystallization tendency of drug molecules. Mol. Pharm. 11, 3123–3132 (2014).

91.

Miller, T. H. et al. Prediction of bioconcentration elements in fish and invertebrates utilizing machine studying. Sci. Complete Environ. 648, 80–89 (2019).

92.

Rose, S., Bergquist, S. L. & Layton, T. J. Computational well being economics for identification of unprofitable well being care enrollees. Biostatistics 18, 682–694 (2017).

93.

Calderon, C. P., Daniels, A. L. & Randolph, T. W. Deep convolutional neural community evaluation of movement imaging microscopy information to categorise subvisible particles in protein formulations. J. Pharm. Sci. 107, 999–1008 (2018).

94.

Degardin, Okay., Guillemain, A., Guerreiro, N. V. & Roggo, Y. Close to infrared spectroscopy for counterfeit detection utilizing a big database of pharmaceutical tablets. J. Pharm. Biomed. Anal. 128, 89–97 (2016).

95.

Web page, D. et al. Figuring out opposed drug occasions by relational studying. Proc. Conf. AAAI Artif. Intell. 2012, 790–793 (2012).


Supply hyperlink
asubhan

wordpress autoblog

amazon autoblog

affiliate autoblog

wordpress web site

web site growth

Show More

Related Articles

Leave a Reply

Your email address will not be published. Required fields are marked *

Close