skip to main content
10.1145/3378904.3378920acmotherconferencesArticle/Chapter ViewAbstractPublication PagesbdetConference Proceedingsconference-collections
research-article

Molecular Similarity Searching Based on Deep Belief Networks with Different Molecular Descriptors

Published: 09 April 2020 Publication History

Abstract

Molecular 2D similarity searching is one of the most widely used techniques for ligand-based virtual screening (LBVS). This study has used the concepts of deep learning by adapted deep belief networks (DBN) and data fusion concept with DBN to enhance the molecular similarity searching of chemical compounds in LBVS. The MDDR Datasets represented by different descriptors to convert the molecule shape to numerical values and each descriptor has different important features rather than the others. The DBN with data fusion is adapted to obtain a lower detection error probability and a higher reliability by using data from multiple distributed descriptors and analyzing the performance of combination and individual descriptors target by target and showed that the combination descriptor did better than both original descriptors. The overall results of this research showed that the use of DBN with data fusion in similarity-based is found to significantly outperform the conventional, industry-standard Tanimoto-based similarity search systems and some others benchmarks witch have been adapted by others researchers, with 1 % and 5% performance improvement in the average recall rates.

References

[1]
Rollinger, J.M., H. Stuppner, and T. Langer, Virtual screening for the discovery of bioactive natural products, in Natural Compounds as Drugs Volume I. 2008, Springer. p. 211--249.
[2]
Sirci, F., et al., Ligand-, structure-and pharmacophore-based molecular fingerprints: a case study on adenosine A1, A2A, A2B, and A3 receptor antagonists. Journal of computer-aided molecular design, 2012. 26(11): p. 1247--1266.
[3]
Walters, W.P., M.T. Stahl, and M.A. Murcko, Virtual screening---an overview. Drug Discovery Today, 1998. 3(4): p. 160--178.
[4]
Chen, C., et al., Combining structure-based pharmacophore modeling, virtual screening, and in silico ADMET analysis to discover novel tetrahydro-quinoline based pyruvate kinase isozyme M2 activators with antitumor activity. Drug design, development and therapy, 2014. 8: p. 1195.
[5]
Drwal, M.N. and R. Griffith, Combination of ligand-and structure-based methods in virtual screening. Drug Discovery Today: Technologies, 2013. 10(3): p. e395-e401.
[6]
Willett, P., Combination of similarity rankings using data fusion. Journal of chemical information and modeling, 2013. 53(1): p. 1--10.
[7]
Klon, A.E., et al., Finding more needles in the haystack: A simple and efficient method for improving high-throughput docking results. Journal of medicinal chemistry, 2004. 47(11): p. 2743--2749.
[8]
Chen, X. and C.H. Reynolds, Performance of similarity measures in 2D fragment-based similarity searching: comparison of structural descriptors and similarity coefficients. J Chem Inf Comput Sci, 2002. 42.
[9]
Sakkiah, S., et al., Theoretical approaches to identify the potent scaffold for human sirtuin1 activator: Bayesian modeling and density functional theory. Medicinal Chemistry Research, 2014. 23(9): p. 3998--4010.
[10]
Hall, D.L. and S.A. McMullen, Mathematical techniques in multisensor data fusion. 2004: Artech House.
[11]
Liggins II, M., D. Hall, and J. Llinas, Handbook of multisensor data fusion: theory and practice. 2017: CRC press.
[12]
Brey, R.L., et al., Neuropsychiatric syndromes in lupus: prevalence using standardized definitions. Neurology, 2002. 58(8): p. 1214--1220.
[13]
LeCun, Y., Y. Bengio, and G. Hinton, Deep learning. Nature, 2015. 521(7553): p. 436--444.
[14]
Maharani, D., H. Murfi, and Y. Satria, Performance of Deep Neural Network for Tabular Data A Case Study of Loss Cost Prediction in Fire Insurance. International Journal of Machine Learning and Computing, 2019. 9(6).
[15]
Ngiam, J., et al. Multimodal deep learning. in Proceedings of the 28th international conference on machine learning (ICML-11). 2011.
[16]
Glorot, X. and Y. Bengio. Understanding the difficulty of training deep feedforward neural networks. in Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics. 2010.
[17]
Erhan, D., et al., Why does unsupervised pre-training help deep learning? Journal of Machine Learning Research, 2010. 11(Feb): p. 625--660.
[18]
Le Roux, N. and Y. Bengio, Representational power of restricted Boltzmann machines and deep belief networks. Neural computation, 2008. 20(6): p. 1631--1649.
[19]
Bengio, Y., Learning deep architectures for AI. Foundations and trends® in Machine Learning, 2009. 2(1): p. 1--127.
[20]
Salido, J.A.A. and C. Ruiz, Using deep learning for melanoma detection in dermoscopy images. International Journal of Machine Learning and Computing, 2018. 8(1): p. 61--68.
[21]
Hinton, G.E., S. Osindero, and Y.-W. Teh, A fast learning algorithm for deep belief nets. Neural computation, 2006. 18(7): p. 1527--1554.
[22]
Rumelhart, D., McClelland. Parallel distributed processing. 1986, Cambridge, MA: MIT Press.
[23]
Klinger, S. and J. Austin, Weighted superstructures for chemical similarity searching, in Proceedings of the 9th Joint Conference on Information Sciences. 2006.
[24]
Arif, S.M., J.D. Holliday, and P. Willett. The Use of Weighted 2D Fingerprints in Similarity-Based Virtual Screening. in Elsevier Inc. 2016.
[25]
Abdo, A. and N. Salim, New fragment weighting scheme for the bayesian inference network in ligand-based virtual screening. Journal of chemical information and modeling, 2010. 51(1): p. 25--32.
[26]
Ahmed, A., A. Abdo, and N. Salim, Ligand-based Virtual screening using Bayesian inference network and reweighted fragments. The Scientific World Journal, 2012.
[27]
Vogt, M., A.M. Wassermann, and J. Bajorath, Application of information---Theoretic concepts in chemoinformatics. Information, 2010. 1(2): p. 60--73.
[28]
Unity. Tripos Inc.
[29]
Matter, H. and T. Pötter, Comparing 3D pharmacophore triplets and 2D fingerprints for selecting diverse compound subsets. Journal of chemical information and computer sciences, 1999. 39(6): p. 1211--1225.
[30]
James, C., D. Weininger, and J. Delany, Daylight theory manual. Daylight Chemical Information Systems. Inc., Irvine, CA, 1995.
[31]
Xue, L., et al., Mini-fingerprints detect similar activity of receptor ligands previously recognized only by three-dimensional pharmacophore-based methods. Journal of chemical information and computer sciences, 2001. 41(2): p. 394--401.
[32]
Xue, L., et al., Profile scaling increases the similarity search performance of molecular fingerprints containing numerical descriptors and structural keys. Journal of chemical information and computer sciences, 2003. 43(4): p. 1218--1225.
[33]
Peng, Z., et al., Deep Boosting: Joint feature selection and analysis dictionary learning in hierarchy. Neurocomputing, 2016. 178: p. 36--45.
[34]
Semwal, V.B., K. Mondal, and G.C. Nandi, Robust and accurate feature selection for humanoid push recovery and classification: deep learning approach. Neural Computing and Applications, 2017. 28(3): p. 565--574.
[35]
Suk, H.-I., et al., Deep sparse multi-task learning for feature selection in Alzheimer's disease diagnosis. Brain Structure and Function, 2016. 221(5): p. 2569--2587.
[36]
Zou, Q., et al., Deep learning based feature selection for remote sensing scene classification. IEEE Geoscience and Remote Sensing Letters, 2015. 12(11): p. 2321--2325.
[37]
Ibrahim, R., et al. Multi-level gene/MiRNA feature selection using deep belief nets and active learning. in 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2014. IEEE.
[38]
Dahl, G.E., N. Jaitly, and R. Salakhutdinov, Multi-task neural networks for QSAR predictions. arXiv preprint arXiv:1406.1231, 2014.
[39]
Hamza, H., et al. Bioactivity Prediction Using Convolutional Neural Network. in International Conference of Reliable Information and Communication Technology. 2019. Springer.
[40]
Unterthiner, T., et al. Deep learning as an opportunity in virtual screening. in Proceedings of the Deep Learning Workshop at NIPS. 2014.
[41]
Unterthiner, T., et al., Toxicity prediction using deep learning. arXiv preprint arXiv:1503.01445, 2015.
[42]
Ramsundar, B., et al., Massively multitask networks for drug discovery. arXiv preprint arXiv:1502.02072, 2015.
[43]
Lusci, A., G. Pollastri, and P. Baldi, Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules. Journal of chemical information and modeling, 2013. 53(7): p. 1563--1575.
[44]
Duvenaud, D.K., et al. Convolutional networks on graphs for learning molecular fingerprints. in Advances in neural information processing systems. 2015.
[45]
Nasser, M., et al. Deep Belief Network for Molecular Feature Selection in Ligand-Based Virtual Screening. in International Conference of Reliable Information and Communication Technology. 2018. Springer.
[46]
Turtle, H. and W.B. Croft, Evaluation of an inference network-based retrieval model. ACM Transactions on Information Systems (TOIS), 1991. 9(3): p. 187--222.
[47]
Bartell, B.T., G.W. Cottrell, and R.K. Belew. Automatic combination of multiple ranked retrieval systems. in SIGIR'94. 1994. Springer.
[48]
Belkin, H.E., C.R. Kilburn, and B. De Vivo, Chemistry of the lavas and tephra from the recent (AD 1631-1944) Vesuvius (Italy) volcanic activity. 1993: US Department of the Interior, US Geological Survey.
[49]
Hull, D.A., J.O. Pedersen, and H. Schütze. Method combination for document filtering. in Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval. 1996. Citeseer.
[50]
Ginn, C.M., P. Willett, and J. Bradshaw, Combination of molecular similarity measures using data fusion, in Virtual Screening: An Alternative or Complement to High Throughput Screening? 2000, Springer. p. 1--16.
[51]
Croft, W.B., H.R. Turtle, and D.D. Lewis. The use of phrases and structured queries in information retrieval. in Proceedings of the 14th annual international ACM SIGIR conference on Research and development in information retrieval. 1991. ACM.
[52]
Rajashekar, T. and W.B. Croft, Combining automatic and manual index representations in probabilistic retrieval. Journal of the American society for information science, 1995. 46(4): p. 272--283.
[53]
Al-Dabbagh, M.M., et al., A quantum-based similarity method in virtual screening. Molecules, 2015. 20(10): p. 18107--18127.
[54]
Himmat, M., et al., Adapting document similarity measures for ligand-based virtual screening. Molecules, 2016. 21(4): p. 476.
[55]
Pipeline Pilot Software: SciTegic Accelrys Inc. http://www.accelrys.com/. San Diego Accelrys Inc; 2008.
[56]
Semwal, V.B., K. Mondal, and G. Nandi, Robust and accurate feature selection for humanoid push recovery and classification: deep learning approach. Neural Computing and Applications, 2015: p. 1--10.
[57]
Hinton, G., A practical guide to training restricted Boltzmann machines. Momentum, 2010. 9(1): p. 926.
[58]
Yuan, C., X. Sun, and R. Lv, Fingerprint liveness detection based on multi-scale LPQ and PCA. China Communications, 2016. 13(7): p. 60--65.

Cited By

View all
  • (2022)Hybrid-Enhanced Siamese Similarity Models in Ligand-Based Virtual ScreenBiomolecules10.3390/biom1211171912:11(1719)Online publication date: 20-Nov-2022
  • (2022)Feature Reduction for Molecular Similarity Searching Based on Autoencoder Deep LearningBiomolecules10.3390/biom1204050812:4(508)Online publication date: 27-Mar-2022
  • (2022)Similarity-Based Virtual Screen Using Enhanced Siamese Deep Learning MethodsACS Omega10.1021/acsomega.1c045877:6(4769-4786)Online publication date: 3-Feb-2022
  • Show More Cited By

Index Terms

  1. Molecular Similarity Searching Based on Deep Belief Networks with Different Molecular Descriptors

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    BDET '20: Proceedings of the 2020 2nd International Conference on Big Data Engineering and Technology
    January 2020
    126 pages
    ISBN:9781450376839
    DOI:10.1145/3378904
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    In-Cooperation

    • Natl University of Singapore: National University of Singapore
    • Southwest Jiaotong University

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 09 April 2020

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. Deep learning
    2. data fusion
    3. deep belief networks (DBN)
    4. ligand-based virtual screening (LBVS)
    5. similarity searching

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Conference

    BDET 2020

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)11
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 13 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2022)Hybrid-Enhanced Siamese Similarity Models in Ligand-Based Virtual ScreenBiomolecules10.3390/biom1211171912:11(1719)Online publication date: 20-Nov-2022
    • (2022)Feature Reduction for Molecular Similarity Searching Based on Autoencoder Deep LearningBiomolecules10.3390/biom1204050812:4(508)Online publication date: 27-Mar-2022
    • (2022)Similarity-Based Virtual Screen Using Enhanced Siamese Deep Learning MethodsACS Omega10.1021/acsomega.1c045877:6(4769-4786)Online publication date: 3-Feb-2022
    • (2021)Similarity-Based Virtual Screen Using Enhanced Siamese Multi-Layer PerceptronMolecules10.3390/molecules2621666926:21(6669)Online publication date: 3-Nov-2021

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media