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1. Lancet, T., Artificial intelligence in healthcare: Within touching distance. Lancet, 390, 10114, 2739, 2018.

2. Kantarjian, H. and Yu, P.P., Artificial Intelligence, Big Data, and Cancer. JAMA Oncol., 1, 5, 573–574, 2015.

3. Topol, E.J., High-performance medicine: The convergence of human and artificial intelligence. Nat. Med., 25, 1, 44–56, 2019.

4. Kanasi, E., Ayilavarapu, S., Jone, J., The aging population: Demographics and the biology of aging. Periodontol. 2000, 72, 1, 13–18, 2016.

5. Naughton, M.J., Brunner, R.L., Hogan, P.E., Danhauer, S.C., Brenes, G.A., Bowen, D.J. et al., Global quality of life among WHI women aged 80 years and older. J. Gerontol. A Biol. Sci. Med. Sci., 71 Suppl. 1, S72–8, 2016.

6. Cohen, C., Kampel, T., Verloo, H., Acceptability among community health-care nurses of intelligent wireless sensor-system technology for the rapid detection of health issues in home-dwelling older adults. Open Nurs. J., 11, 54–63, 2017.

7. Labovitz, D.L., Shafner, L., Reyes, G.M., Virmani, D., Hanina, A., Using artificial intelligence to reduce the risk of nonadherence in patients on anticoagulation therapy. Stroke, 48, 5, 1416–1419, 2017.

8. Ching, T., Himmelstein, D.S., Beaulieu-Jones, B.K. et al., Opportunities and obstacles for deep learning in biology and medicine. J. R. Soc. Interface, 15, 141, pii:20170387, 2018.

9. Goh, G.B., Hodas, N.O., Vishnu, A., Deep learning for computational chemistry. J. Comput. Chem., 38, 16, 1291–1307, 2017.

10. Ramsundar, B., Liu, B., Wu, Z. et al., Is multi task deep learning practical for pharma? J. Chem. Inf. Model., 57, 8, 2068–2076, 2017.

11. So, H.C. and Sham, P.C., Improving polygenic risk prediction from summary statistics by an empirical Bayes approach. Sci. Rep., 7, 41262, 2017.

12. English, A.C., Salerno, W.J., Hampton, O.A., GonzagaJauregui, C., Ambreth, S., Ritter, D.I., Beck, C.R., Davis, C.F., Dahdouli, M., Ma, S. et al., Assessing structural variation in a personal genome—Towards a human reference diploid genome. BMC Genomics, 16, 286, 2015.

13. Angermueller, C., Parnamaa, T., Parts, L., Stegle, O., Deep learning for computational biology. Mol. Syst. Biol., 12, 878, 2016.

14. Meuwissen, T. and Goddard, M., Accurate Prediction of Genetic Values for Complex Traits by Whole-Genome Resequencing. Genetics, 185, 623–631, 2010.

15. Pérez-Enciso, M., Rincón, J.C., Legarra, A., Sequence- vs. chip-assisted genomic selection: Accurate Biological information is advised. Genet. Sel. Evol., 47, 1–14, 2015.

16. Heidaritabar, M., Calus, M.P.L., Megens, H.-J., Vereijken, A., Groenen, M.A.M., Bastiaansen, J.W.M., Accuracy of genomic prediction using imputed whole-genome sequence data in white layers. J. Anim. Breed. Genet., 133, 167–179, 2016.

17. Ainscough, B.J., Barnell, E.K., Ronning, P., Campbell, K.M., Wagner, A.H., Fehniger, T.A., Dunn, G.P., Uppaluri, R., Govindan, R., Rohan, T.E. et al., A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data. Nat. Genet., 50, 1735–1743, 2018.

18. Sundaram, L., Gao, H., Padigepati, S.R., McRae, J.F., Li, Y., Kosmicki, J.A., Fritzilas, N., Hakenberg, J., Dutta, A., Shon, J. et al., Predicting the clinical impact of human mutation with deep neural networks. Nat. Genet., 50, 1161–1170, 2018.

19. Zhou, J., Theesfeld, C.L., Yao, K., Chen, K.M., Wong, A.K., Troyanskaya, O.G., Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk. Nat. Genet., 50, 1171–1179, 2018.

20. Torkamani, A., Andersen, K.G., Steinhubl, S.R., Topol., E.J., High-definition medicine. Cell, 170, 828–4, 2017.

21. Este va, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K. et al., A guide to deep learning in healthcare. Nat. Med., 25, 24–9, 2019.

22. Fraser, K.C., Meltzer, J.A., Rudzicz, F., Linguistic features identify Alzheimer’s disease in narrative speech. J. Alzheimers Dis., 49, 407–22, 2016.

23. Rajkomar, A., Oren, E., Chen, K., Dai, A.M., Hajaj, N., Liu, P.J. et al., Scalable and accurate deep learning for electronic health records. NPJ Digit. Med., 1, 18, 2018, https://doi.org/10.1038/s41746-018-0029-1.

24. Zou, J., Huss, M., Abid, A., Mohammadi, P., Torkamani, A., Telenti, A., A primer on deep learning in genomics. Nat. Genet., 51, 12–8, 2019.

25. Eraslan, G., Avsec, Ž., Gagneur, J., Theis, F.J., Deep learning: New computational modelling techniques for genomics. Nat. Rev. Genet., 20, 389–403, 2019.

26. Yang, J., Cao, R., Si, D., EMNets: A Convolutional Autoencoder is made available under a CC-BY-NC-ND 4.0 International license. bioRxiv, preprint, 2018, https://doi.org/10.1101/561027. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. It for Protein Surface Retrieval Based on Cryo-Electron Microscopy Imaging,” in Proceedings of the ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics—BCB ‘18, Washington, DC, USA, pp. 639–644.

27. Ng, A. and Si, D., Beta-Barrel Detection for Medium Resolution CryoElectron Microscopy Density Maps Using Genetic Algorithms and Ray Tracing. J. Comput. Biol., 25, 6, 326–336, 2018.

28. Li, R., Si, D., Zeng, T., Ji, S., He, J., Deep Convolutional Neural Networks for Detecting Secondary Structures in Protein Density Maps from Cryo-Electron Microscopy. Proceedings, pp. 41–46, 2016.

29. Si, D., Ji, S., Nasr, K.A., He, J., A machine learning approach for the identification of protein secondary structure elements from electron cryo-microscopy density maps. Biopolymers, 97, 9, 698–708, 2012.

30. Huang, Q., Zhang, P., Wu, D., Zhang, L., Turbo Learning for CaptionBot and DrawingBot, in: Advances in Neural Information Processing Systems, vol. 20, pp. 6456–6466, Curran Associates Inc., USA, 2018.

31. Xu, T. et al., AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks, in: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.

32. Kosylo, N. et al., Artificial Intelligence on Job-Hopping Forecasting: AI on Job-Hopping, in: Portland International Conference on Management of Engineering and Technology (PICMET), 2018.

33. Keasar, C. et al., An analysis and evaluation of the WeFold collaborative for protein structure prediction and its pipelines in CASP11 and CASP12. Sci. Rep., 8, 1, 9939, 2018.

34. Hou, J., Wu, T., Cao, R., Cheng, J., Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13. bioRxiv, Open Access 552–422, 15 April 2019, https://doi.org/10.1002/prot.25697.

35. Pauling, L. and Corey, R.B., The pleated sheet, a new layer configuration of the polypeptide chain. Proc. Natl. Acad. Sci., 37, 251–256, 1951.

36. Pauling, L., Corey, R.B., Branson, H.R., The structure of proteins: Two hydrogen bonded helical configurations of the polypeptide chain. Proc. Natl. Acad. Sci., 37, 205–211, 1951.

37. Kendrew, J.C., Dickerson, R.E., Strandberg, B.E., Hart, R.J., Davies, D.R., Phillips, D.C., Shore, V.C., Structure of myoglobin: A three-dimensional Fourier synthesis at 2_a resolution. Nature, 185, 422–427, 1960.

38. Perutz, M.F., Rossmann, M.G., Cullis, A.F., Muirhead, G., Will, G., North, A.T., Structure of haemoglobin: A three-dimensional Fourier synthesis at 5.5 Angstrom resolution, obtained by x-ray analysis. Nature, 185, 416–422, 1960.

39. Dill, K.A., Dominant forces in protein folding. Biochemistry, 31, 7134–7155, 1990.

40. Laskowski, R.A., Watson, J.D., Thornton, J.M., From protein structure to biochemical function? J. Struct. Funct. Genomics, 4, 167–177, 2003.

41. Travers, DNA conformation and protein binding. Annu. Rev. Biochem., 58, 427–452, 1989.

42. Bjorkman, P.J. and Parham, P., Structure, function and diversity of class I major histocompatibility complex molecules. Annu. Rev. Biochem., 59, 253– 288, 1990.

43. Yang, J., Cao, R., Si, D., EMNets: A Convolutional Autoencoder for Protein Surface Retrieval Based on Cryo-Electron Microscopy Imaging, in: Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics—BCB ‘18, Washington, DC, USA, pp. 639–644, 2018.

44. Ng, A. and Si, D., Beta-Barrel Detection for Medium Resolution CryoElectron Microscopy Density Maps Using Genetic Algorithms and Ray Tracing. J. Comput. Biol., 25, 3, 326–336, Mar. 2018.

45. Li, R., Si, D., Zeng, T., Ji, S., He, J., Deep Convolutional Neural Networks for Detecting Secondary Structures in Protein Density Maps from Cryo-Electron Microscopy. Proceedings, 2016, 41–46, Dec. 2016.

46. Si, D., Ji, S., Nasr, K.A., He, J., A machine learning approach for the identification of protein secondary structure elements from electron cryo-microscopy density maps. Biopolymers, 97, 9, 698–708, Sep. 2012.

47. Huang, Q., Zhang, P., Wu, D., Zhang, L., Turbo Learning for CaptionBot and DrawingBot, in: Advances in Neural Information Processing Systems, vol. 31, S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, R. Garnett (Eds.), pp. 6456–6466, Curran Associates, Inc., USA, 2018.

48. Xu, T. et al., AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks, in: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.

49. Kosylo, N. et al., Artificial Intelligence on Job-Hopping Forecasting: AI on Job-Hopping, in: 2018 Portland International Conference on Management of Engineering and Technology (PICMET), 2018.

50. Keasar, C. et al., An analysis and evaluation of the WeFold collaborative for protein structure prediction and its pipelines in CASP11 and CASP12. Sci. Rep., 8, 1, 9939, Jul. 2018.

51. Hou, J., Wu, T., Cao, R., Cheng, J., Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13. bioRxiv, Open Access 552422, 15 April 2019, https://doi.org/10.1002/prot.25697.

52. Moult, J., Fidelis, K., Kryshtafovych, A., Schwede, T., Tramontano, A., Critical assessment of methods of protein structure prediction (CASP)-Round XII. Proteins, 86, Suppl 1, 7–15, Mar. 2018.

53. Kendrew, J.C., Dickerson, R.E., Strandberg, B.E., Hart, R.J., Davies, D.R., Phillips, D.C., Shore, V.C., Structure of myoglobin: A three-dimensional Fourier synthesis at 2_a resolution. Nature, 185, 422–427, 1960.

54. Perutz, M.F., Rossmann, M.G., Cullis, A.F., Muirhead, G., Will, G., North, A.T., Structure of haemoglobin: A three-dimensional Fourier synthesis at 5.5 Angstrom resolution, obtained by X-ray analysis. Nature, 185, 416–422, 1960.

55. Travers, A., DNA conformation and protein binding. Annu. Rev. Biochem., 58, 427–452, 1989.

56. Bjorkman, P.J. and Parham, P., Structure, function and diversity of class I major histocompatibility complex molecules. Annu. Rev. Biochem., 59, 253– 288, 1990.

57. Bernstein, F.C., Koetzle, T.F., Williams, G.J., Meyer, E.F., Brice, M.D., Rodgers, J.R., Kennard, O., Shimanouchi, T., Tasumi, M., The protein data bank. Eur. J. Biochem., 80, 319–324, 1977. [CrossRef] [PubMed].

58. Consortium, U., The universal protein resource (UniProt). Nucleic Acids Res., 36, D190–D195, 2008. [CrossRef] [PubMed].

59. Kabsch, W. and Sander, C., Dictionary of protein secondary structure: Pattern recognition of hydrogen-bonded and geometrical features. Biopolymers, 22, 2577–2637, 1983. [CrossRef] [PubMed].

60. Murzin, A.G., Brenner, S.E., Hubbard, T., Chothia, C., Scop: A structural classification of proteins database for the investigation of sequences and structures. J. Mol. Biol., 247, 536–540, 1995. [CrossRef].

61. Andreeva, A., Howorth, D., Chothia, C., Kulesha, E., Murzin, A.G., SCOP2 prototype: A new approach to protein structure mining. Nucleic Acids Res., 42, 310–314, 2014. [CrossRef] [PubMed].

62. Sillitoe, I., Lewis, T.E., Cuff, A., Das, S., Ashford, P., Dawson, N.L., Furnham, N., Laskowski, R.A., Lee, D., Lees, J.G., Cath: Comprehensive structural and functional annotations for genome sequences. Nucleic Acids Res., 43, 376– 381, 2015.

63. Karplus, K., Barrett, C., Hughey, R., Hidden Markov models for detecting remote protein homologies. Bioinfo., 14, 10, 846–856, 1998.

64. Eddy, S.R., Profile hidden Markov models. Bioinfo., 14, 755–763, 1998.

65. Soeding, J., Protein homology detection by HMM–HMM comparison. Bioinfo., 21, 951–960, 2005.

66. Rost, B. and Sander, C., Prediction of protein secondary structure at better than 70% accuracy. J. Mol. Biol., 232, 2, 584–599, 1993, https://doi.org/10.1006/jmbi.1993.1413.

67. Rost, B., PHD: Predicting one-dimensional protein structure by profile based neural networks. Methods Enzymol., 266, 525–539, 1996, https://doi.org/10.1016/s0076-6879(96)66033-9.

68. Jones, D.T., Protein secondary structure prediction based on positionspecific scoring matrices. J. Mol. Biol., 292, 2, 195–202, 1999.

69. Cuff, J.A., Clamp, M.E., Siddiqui, A.S., Finlaym, M., Barton, G.J., JPred: A consensus secondary structure prediction server. Bioinformatics, 14, 10, 892– 893, 1998.

70. LeCun, Y., Bengio, Y., Hinton, G., Deep learning. Nature, 521, 7553, 436–444, 2015.

71. Zhu, J., Wang, S., Bu, D., Xu, J., Protein threading using residue covariation and deep learning. Bioinformatics, 34, 13, i263–i273, 2018.

72. Xu, J. and Wang, S., Analysis of distance-based protein structure prediction by deep learning in CASP13. Proteins, 87, 12, 1069–1081, 2019, https://doi.org/10.1002/prot.25810.

73. Xu, J., Distance-based protein folding powered by deep learning. Proc. Natl. Acad. Sci. U.S.A., 116, 34, 16856–16865, 2019.

74. Greener, J.G., Kandathil, S.M., Jones, D.T., Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints. Nat. Commun., 10, 3977, 2019.

75. Senior, A.W., Evans, R., Jumper, J. et al., Protein structure prediction using multiple deep neural networks in CASP13. Proteins, 87, 12, 1041–1048, 2019, https://doi.org/10.1002/prot.25834. [2] Cao, R., Freitas, C., Chan, L., Sun, M., Jiang, H., Chen, Z., ProLanGO: Protein Function Prediction Using Neural Machine Translation Based on a Recurrent Neural Network. Molecules, 22, 10, 1732, 2017.

76. Qiu, J., Sheffler, W., Baker, D., Noble, W.S., Ranking predicted protein structures with support vector regression. Proteins, 71, 1175–1182, 2007.

77. Joo, H. and Tsai, J., An amino acid code for β-sheet packing structure. Proteins: Structure, Function, and Bioinformatics, Volume 82 (9) – Sep. 1, 2014.

78. Crick, F.H., The packing of α-helices: simple coiled-coils. Acta Crystallogr., 6, 689–697, 1953.

79. von Mering, C., Krause, R., Sne, B. et al., Comparative assessment of large scale data sets of protein–protein interactions. Nature, 417, 6887, 399–403, 2002.

80. Hakes, L., Lovell, S.C., Oliver, S.G. et al., Specificity in protein interactions and its relationship with sequence diversity and coevolution. PNAS, 104, 19, 7999–8004, 2007.

81. Harwell, L.H., Hopfield, J.J., Leibler, S., Murray, A.W., From molecular to modular cell biology. Nature, 402, c47–c52, 999.

82. Jeong, H., Mason, S., Barabási, A.L. et al., Lethality and centrality in protein networks. Nature, 411, 6833, 41–42, 2001.

83. Giot, L. et al., A protein interaction map of Drosophila melanogaster. Science, 302, 1727–1736, 2003.

84. Li, S., Armstrong, C., Bertin, N., A map of the interactome network of the metazoan. Science, 303, 5657, 540–543, 2004.

85. Wuchty, S., Scale-free behavior in protein domain networks. Mol. Biol. Evol., 18, 9, 1694–1702, 2001.

86. del Sol, A. and O’Meara, P., Small-world network approach to identify key residues in protein–protein interaction. Proteins, 58, 3, 672–682, 2004.

87. del Sol, A., Fujihashi, H., O’Meara, P., Topology of small-world networks of protein–protein complex structures. Bioinformatics, 21, 8, 1311–131, 2005.

88. Brohée, S. and van Helden, J., Evaluation of clustering algorithms for protein–protein interaction networks. BMC Bioinf., 7, 48, 2006.

89. Spirin, V. and Mirny, L.A., Protein complexes and functional modules in molecular networks. PNAS, 100, 12123–12128, 2003.

90. Bu, D., Zhao, Y., Cai, L. et al., Topological structure analysis of the protein– protein interaction network in budding yeast. Nucleic Acids Res., 31, 9, 2443– 2450, 2003.

91. Nicolas, J., Artificial intelligence and bioinformatics. 2018, https://doi.org/10.1007/978-3-030-06170-8_7.

92. Dimova, D. and Bajorath, J., Advances in activity cliff research. Mol. Inf., 35, 5, 181–191, 2016.

93. Stumpfe, D., Hu, H., Bajorath, J., Evolving Concept of Activity Cliffs. ACS Omega, 4, 11, 14360–14368, 2019, Published 2019 Aug 26.

94. Kitchen, D.B., Decornez, H., Furr, J.R., Bajorath, J., Docking and scoring in virtual screening for drug discovery: Methods and applications. Nat. Rev. Drug Discovery, 3, 11, 935, 2004.

95. Ferreira, L.G., dos Santos, R.N., Oliva, G., Andricopulo, A.D., Molecular docking and structure-based drug design strategies. Molecules, 20, 7, 13384– 13421, 2015.

96. Dos Santos, R.N., Ferreira, L.G., Andricopulo, A.D., Practices in Molecular Docking and Structure-Based Virtual Screening. Methods Mol. Biol. (Clifton, N.J.), 1762, 31–50, 2018, https://doi.org/10.1007/978-1-4939-7756-7_3.

97. Brown, J.B., Niijima, S., Okuno, Y., Compound–Protein Interaction Prediction Within Chemogenomics: Theoretical Concepts, Practical Usage, and Future Directions. Mol. Inf., 32, 906–921, 2013.

98. Qiu, T., Qiu, J., Feng, J., Wu, D., Yang, Y., Tang, K., Cao, Z., Zhu, R., The recent progress in proteochemometric modelling: Focusing on target descriptors, cross-term descriptors and application scope. Brief Bioinform., 18, 1, 125– 136, 2017.

99. Jackson, M.J., Esnouf, M.P., Winzor, D., Duewer, D., Defining and measuring biological activity: Applying the principles of metrology. Accredit. Qual. Assur., 12, 6, 283–29, 2007, https://doi.org/10.1007/s00769-006-0254-1.

100. Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., Li, B., Madabhushi, A., Shah, P., Spitzer, M., Zhao, S., Applications of machine learning in drug discovery and development. Nat. Rev. Drug Discovery, 18, 6, 463–477, 2019, https://doi.org/10.1038/s41573-019-0024-5.

101. Sidey-Gibbons, J. and Sidey-Gibbons, C.J., Machine learning in medicine: A practical introduction. BMC Med. Res. Method., 19, 1, 64, 2019, https://doi.org/10.1186/s12874-019-0681-4.

102. Greene, N., Judson, P.N., Langowski, J.J., Marchantm, C.A., Knowledge-based expert systems for toxicity and metabolism prediction: DEREK, StAR and METEOR. SAR QSAR Environ. Res., 10, 2–3, 299–314, 1999.

103. Raies, A.B. and Bajic, V.B., In silico toxicology: computational methods for the prediction of chemical toxicity. Wiley Interdiscip. Rev. Comput. Mol. Sci., 6, 2, 147–172, 2016, https://doi.org/10.1002/wcms.1240.

104. Patlewicz, G., Jeliazkova, N., Safford, R.J., Worth, A.P., Aleksiev, B., An evaluation of the implementation of the Cramer classification scheme in the Toxtree software. SAR QSAR Environ. Res., 19, 5–6, 495–524, 2008.

105. Agrahari, R., Foroushani, A., Docking, T.R. et al., Applications of Bayesian network models in predicting types of hematological malignancies. Sci. Rep., 8, 6951, 2018, https://doi.org/10.1038/s41598-018-24758-5.

106. Ahmed, A., Abdo, A., Salim, N., Ligand-based virtual screening using Bayesian inference network and reweighted fragments. Sci. World J., Drug Discovery Today, 01 Jun 2002, 7(11):597–598, 410914, 2012, https://doi.org/10.1016/s1359-6446(02)02316-4.

107. Madhukar, N.S., Khade, P.K., Huang, L. et al., A Bayesian machine learning approach for drug target identification using diverse data types. Nat. Commun., 10, 5221, 2019, https://doi.org/10.1038/s41467-019-12928-6.

108. Hinselmann, G., Rosenbaum, L., Jahn, A., Fechner, N., Ostermann, C., and Zell, A., 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, 2011.

109. Mahé, P. and Vert, J., Graph kernels based on tree patterns for molecules. Mach. Learn., 75, 3–35, 2009, https://doi.org/10.1007/s10994-008-5086-2.

110. Byvatov, E., Fechner, U., Sadowski, J., Schneider, G., Comparison of support vector machine and artificial neural network systems for drug/non-drug classification. J. Chem. Inf. Comput. Sci., 43, 6, 1882–1889, 2003, https://doi.org/10.1021/ci0341161.

111. Sakiyama, Y., Yuki, H., Moriya, T. et al., Predicting human liver microsomal stability with machine learning techniques. J. Mol. Graph. Model., 26, 6, 907–915, 2008.

112. Wang, C. and Zhang, Y., Improving scoring-docking-screening powers of protein–ligand scoring functions using random forest. J. Comput. Chem., 38, 3, 169–177, 2017.

113. Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., Blaschke, T., The rise of deep learning in drug discovery. Drug Discovery Today, 23, 6, 1241–1250, 2018.

114. Marini, F., Roncaglioni, A., Novic, M., Variable selection and interpretation in structure-affinity correlation modeling of estrogen receptor binders. J. Chem. Inf. Model., 45, 6, 1507–1519, 2005.

115. Kazius, J., Nijssen, S., Kok, J.N., Bäck, T., IJzerman, A.P., Substructure Mining Using Elaborate Chemical Representation. J. Chem. Inf. Model., 46, 2, 597– 605, 2006.

116. Raschka, S., Scott, A.M., Huertas, M., Li, W., Kuhn, L.A., Automated Inference of Chemical Discriminants of Biological Activity. Methods Mol. Biol., 1762, 307–338, 2018.

117. Ramraj, T. and Prabhakar, R., Frequent Subgraph Mining Algorithms—A Survey. Proc. Comput. Sci., 47, 197–204, 2015, https://doi.org/10.1016/j.procs.2015.03.198.

118. Mrzic, A., Meysman, P., Bittremieux, W. et al., (Grasping frequent subgraph mining for bioinformatics applications. BioData Min., 11, 20, 2018.

1 *Corresponding author: anjali.p@srmuniversity.ac.in