Skip to content
2000
  1. Home
  2. A-Z Publications
  3. Natural Products Journal, The
  4. Fast Track Articles
  5. Fast Track Article
image of Deep Learning Approaches for Predicting Bioactivity of Natural Compounds

Abstract

The investigation of computational techniques to forecast the bioactivity of natural substances has been spurred by the growing interest in utilizing their medicinal potential. A branch of artificial intelligence called deep learning has been particularly useful for predicting outcomes in a variety of fields, such as bioactivity prediction and drug discovery, by evaluating large amounts of complex data. An overview of current developments in the application of deep learning techniques to the prediction of natural chemical bioactivity has been presented in this article. The advantages provided by deep learning approaches, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and graph neural networks (GNNs), have been highlighted, and the difficulties connected with conventional methods of bioactivity prediction have been examined. Moreover, a variety of molecular representations—such as molecular fingerprints, graph representations, and molecular descriptors—that are fed into deep learning models have been studied. Additionally, included in this study is the integration of many data sources, including omics data, chemical structures, and biological tests, to enhance the precision and resilience of bioactivity prediction models. Furthermore, this review covers the uses of deep learning in target prediction, virtual screening, and poly-pharmacology study of natural substances. The paper concludes by discussing the field's present issues and potential paths forward, such as the requirement for standardized benchmark datasets, the interpretability of deep learning models, and the incorporation of experimental validation techniques. All things considered, this study sheds light on the most recent developments in deep learning techniques for estimating the bioactivity of natural substances and their possible effects on drug development and discovery.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/npj/10.2174/0122103155332267241122143118
2025-01-10
2025-04-24

  • From This Site

    /content/journals/npj/10.2174/0122103155332267241122143118
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Paul D. Sanap G. Shenoy S. Kalyane D. Kalia K. Tekade R.K. Artificial intelligence in drug discovery and development. Drug Discov. Today 2021 26 1 80 93 10.1016/j.drudis.2020.10.010 33099022
    [Google Scholar]
  2. Ashraf F.B. Akter S. Mumu S.H. Islam M.U. Uddin J. Bio-activity prediction of drug candidate compounds targeting SARS-Cov-2 using machine learning approaches. PLoS One 2023 18 9 e0288053 10.1371/journal.pone.0288053 37669264
    [Google Scholar]
  3. Yoo S. Yang H.C. Lee S. Shin J. Min S. Lee E. Song M. Lee D. A deep learning-based approach for identifying the medicinal uses of plant-derived natural compounds. Front. Pharmacol. 2020 11 584875 10.3389/fphar.2020.584875 33519445
    [Google Scholar]
  4. Shin S.H. Oh S.M. Yoon Park J.H. Lee K.W. Yang H. OptNCMiner: A deep learning approach for the discovery of natural compounds modulating disease-specific multi-targets. BMC Bioinformatics 2022 23 1 218 10.1186/s12859‑022‑04752‑5 35672685
    [Google Scholar]
  5. Park J. Beck B.R. Kim H.H. Lee S. Kang K. A brief review of machine learning-based bioactive compound research. Appl. Sci. 2022 12 6 2906 10.3390/app12062906
    [Google Scholar]
  6. Jain P. Sharma M. Dhingra D. Bioactivity prediction in drug discovery. IRJMETS 2022 4787 2582 5208
    [Google Scholar]
  7. Nothias L.F. Nothias-Esposito M. da Silva R. Wang M. Protsyuk I. Zhang Z. Sarvepalli A. Leyssen P. Touboul D. Costa J. Paolini J. Alexandrov T. Litaudon M. Dorrestein P.C. Bioactivity-based molecular networking for the discovery of drug leads in natural product bioassay-guided fractionation. J. Nat. Prod. 2018 81 4 758 767 10.1021/acs.jnatprod.7b00737 29498278
    [Google Scholar]
  8. Alberga D. Trisciuzzi D. Montaruli M. Leonetti F. Mangiatordi G.F. Nicolotti O. A new approach for drug target and bioactivity prediction: The multifingerprint similarity search algorithm (MuSSeL). J. Chem. Inf. Model. 2019 59 1 586 596 10.1021/acs.jcim.8b00698 30485097
    [Google Scholar]
  9. Zhang R. Li X. Zhang X. Qin H. Xiao W. Machine learning approaches for elucidating the biological effects of natural products. Nat. Prod. Rep. 2021 38 2 346 361 10.1039/D0NP00043D 32869826
    [Google Scholar]
  10. van Tilborg D. Alenicheva A. Grisoni F. Exposing the Limitations of Molecular Machine Learning with Activity Cliffs Exposing the limitations of molecular machine learning with activity cliffs. J. Chem. Inf. Model. 2022 62 23 5938 5951 10.1021/acs.jcim.2c01073 36456532
    [Google Scholar]
  11. Ge Y. Zhou C. Ma Y. Wang Z. Wang S. Wang W. Wu B. Advancing natural product discovery: A structure-oriented fractions screening platform for compound annotation and isolation. Anal. Chem. 2024 96 14 5399 5406 10.1021/acs.analchem.3c05057 38523322
    [Google Scholar]
  12. Moret M. Pachon Angona I. Cotos L. Yan S. Atz K. Brunner C. Baumgartner M. Grisoni F. Schneider G. Leveraging molecular structure and bioactivity with chemical language models for de novo drug design. Nat. Commun. 2023 14 1 114 10.1038/s41467‑022‑35692‑6 36611029
    [Google Scholar]
  13. Agarwal A. Prajapati P. Prajapati B.G. Chemical biology-based toolset for artificial intelligence usage in drug design. Artificial Intelligence in Bioinformatics and Chemoinformatics. CRC Press 2023 101 115 10.1201/9781003353768‑6
    [Google Scholar]
  14. Muhasina K.M. Ghosh P. Swaroop A.K. Selvaraj J. Prajapati B.G. Duraiswamy B. Palaniswamy D.S. Machine learning and data mining: Uses and challenges in bioinformatics Artificial Intelligence in Bioinformatics and Chemoinformatics CRC Press 2024 117 138
    [Google Scholar]
  15. LeCun Y. Bengio Y. Hinton G. Deep learning. Nature 2015 521 7553 436 444 10.1038/nature14539 26017442
    [Google Scholar]
  16. Janiesch C. Zschech P. Heinrich K. Machine learning and deep learning. Electron. Mark. 2021 31 3 685 695 10.1007/s12525‑021‑00475‑2
    [Google Scholar]
  17. Madhava Krishna G. G. Madhava Krishna Dr. Pradeepa D Advanced machine learning and deep learning approaches for predicting avian influenza outbreaks. (IJRD) 2024 9 21 30 [IJRD]. 10.36713/epra18191
    [Google Scholar]
  18. Paliwal S. Sharma A. Jain S. Sharma S. Machine learning and deep learning in bioinformatics. Bioinformatics and Computational Biology. Boca Raton Chapman and Hall/CRC 2023 63 74 10.1201/9781003331247‑7
    [Google Scholar]
  19. Alzubaidi L. Zhang J. Humaidi A.J. Al-Dujaili A. Duan Y. Al-Shamma O. Santamaría J. Fadhel M.A. Al-Amidie M. Farhan L. Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. J. Big Data 2021 8 1 53 10.1186/s40537‑021‑00444‑8 33816053
    [Google Scholar]
  20. Sarker I.H. Deep learning: A comprehensive overview on techniques, taxonomy, applications and research directions. SN Comput. Sci. 2021 2 6 420 10.1007/s42979‑021‑00815‑1 34426802
    [Google Scholar]
  21. Zhang Y. Ye T. Xi H. Juhas M. Li J. Deep learning driven drug discovery: Tackling severe acute respiratory syndrome coronavirus 2. Front. Microbiol. 2021 12 739684 10.3389/fmicb.2021.739684 34777286
    [Google Scholar]
  22. Askr H. Elgeldawi E. Aboul Ella H. Elshaier Y.A.M.M. Gomaa M.M. Hassanien A.E. Deep learning in drug discovery: An integrative review and future challenges. Artif. Intell. Rev. 2023 56 7 5975 6037 10.1007/s10462‑022‑10306‑1 36415536
    [Google Scholar]
  23. Nag S. Baidya A.T.K. Mandal A. Mathew A.T. Das B. Devi B. Kumar R. Deep learning tools for advancing drug discovery and development. Biotech 2022 12 1 2 10.1007/s13205‑022‑03165‑8
    [Google Scholar]
  24. Blanco-González A. Cabezón A. Seco-González A. Conde-Torres D. Antelo-Riveiro P. Piñeiro Á. Garcia-Fandino R. The role of AI in drug discovery: Challenges, opportunities, and strategies. Pharmaceuticals 2023 16 6 891 10.3390/ph16060891 37375838
    [Google Scholar]
  25. Naik N. Hameed B.M.Z. Shetty D.K. Swain D. Shah M. Paul R. Aggarwal K. Ibrahim S. Patil V. Smriti K. Shetty S. Rai B.P. Chlosta P. Somani B.K. Legal and ethical consideration in artificial intelligence in healthcare: Who takes responsibility? Front. Surg. 2022 9 862322 10.3389/fsurg.2022.862322 35360424
    [Google Scholar]
  26. Farhud D.D. Zokaei S. Ethical issues of artificial intelligence in medicine and healthcare. Iran. J. Public Health 2021 50 11 i v 10.18502/ijph.v50i11.7600 35223619
    [Google Scholar]
  27. Atanasov A.G. Zotchev S.B. Dirsch V.M. Supuran C.T. Banach M. Rollinger J.M. Barreca D. Weckwerth W. Bauer R. Bayer E.A. Majeed M. Bishayee A. Bochkov V. Bonn G.K. Braidy N. Bucar F. Cifuentes A. D’Onofrio G. Bodkin M. Diederich M. Dinkova-Kostova A.T. Efferth T. El Bairi K. Arkells N. Fan T.P. Fiebich B.L. Freissmuth M. Georgiev M.I. Gibbons S. Godfrey K.M. Gruber C.W. Heer J. Huber L.A. Ibanez E. Kijjoa A. Kiss A.K. Lu A. Macias F.A. Miller M.J.S. Mocan A. Müller R. Nicoletti F. Perry G. Pittalà V. Rastrelli L. Ristow M. Russo G.L. Silva A.S. Schuster D. Sheridan H. Skalicka-Woźniak K. Skaltsounis L. Sobarzo-Sánchez E. Bredt D.S. Stuppner H. Sureda A. Tzvetkov N.T. Vacca R.A. Aggarwal B.B. Battino M. Giampieri F. Wink M. Wolfender J.L. Xiao J. Yeung A.W.K. Lizard G. Popp M.A. Heinrich M. Berindan-Neagoe I. Stadler M. Daglia M. Verpoorte R. Supuran C.T. International Natural Product Sciences Taskforce Natural products in drug discovery: Advances and opportunities. Nat. Rev. Drug Discov. 2021 20 3 200 216 10.1038/s41573‑020‑00114‑z 33510482
    [Google Scholar]
  28. Dzobo K. The role of natural products as sources of therapeutic agents for innovative drug discovery. Comprehensive Pharmacology. Elsevier 2022 408 422 10.1016/B978‑0‑12‑820472‑6.00041‑4
    [Google Scholar]
  29. Harvey A. Natural products in drug discovery. Drug Discov. Today 2008 13 19-20 894 901 10.1016/j.drudis.2008.07.004 18691670
    [Google Scholar]
  30. Ge Y. Ma Y. Zhao M. Wei J. Wu X. Zhang Z. Yang H. Lei H. Wu B. Exploring gabosine and chlorogentisyl alcohol derivatives from a marine-derived fungus as EcGUS inhibitors with informatic assisted approaches. Eur. J. Med. Chem. 2022 242 114699 10.1016/j.ejmech.2022.114699 36001934
    [Google Scholar]
  31. Koehn F.E. Carter G.T. The evolving role of natural products in drug discovery. Nat. Rev. Drug Discov. 2005 4 3 206 220 10.1038/nrd1657 15729362
    [Google Scholar]
  32. Dias D.A. Urban S. Roessner U. A historical overview of natural products in drug discovery. Metabolites 2012 2 2 303 336 10.3390/metabo2020303 24957513
    [Google Scholar]
  33. Simoben C.V. Babiaka S.B. Moumbock A.F.A. Namba-Nzanguim C.T. Eni D.B. Medina-Franco J.L. Günther S. Ntie-Kang F. Sippl W. Challenges in natural product-based drug discovery assisted with in silico -based methods. RSC Advances 2023 13 45 31578 31594 10.1039/D3RA06831E 37908659
    [Google Scholar]
  34. Barba-Ostria C. Carrera-Pacheco S.E. Gonzalez-Pastor R. Heredia-Moya J. Mayorga-Ramos A. Rodríguez-Pólit C. Zúñiga-Miranda J. Arias-Almeida B. Guamán L.P. Evaluation of biological activity of natural compounds: Current trends and methods. Molecules 2022 27 14 4490 10.3390/molecules27144490 35889361
    [Google Scholar]
  35. Ahmadu T. Ahmad K. An introduction to bioactive natural products and general applications. Bioactive Natural Products for Pharmaceutical Applications. Springer 2021 41 91 10.1007/978‑3‑030‑54027‑2_2
    [Google Scholar]
  36. Sytar O. Smetanska I. Special issue “Bioactive compounds from natural sources (2020, 2021)”. Molecules 2022 27 6 1929 10.3390/molecules27061929 35335293
    [Google Scholar]
  37. Atas Guvenilir H. Doğan T. How to approach machine learning-based prediction of drug/compound–target interactions. J. Cheminform. 2023 15 1 16 10.1186/s13321‑023‑00689‑w 36747300
    [Google Scholar]
  38. Periwal V. Bassler S. Andrejev S. Gabrielli N. Patil K.R. Typas A. Patil K.R. Bioactivity assessment of natural compounds using machine learning models trained on target similarity between drugs. PLOS Comput. Biol. 2022 18 4 e1010029 10.1371/journal.pcbi.1010029 35468126
    [Google Scholar]
  39. Béquignon O.J.M. Bongers B.J. Jespers W. IJzerman A.P. van der Water B. van Westen G.J.P. Papyrus: A large-scale curated dataset aimed at bioactivity predictions. J. Cheminform. 2023 15 1 3 10.1186/s13321‑022‑00672‑x 36609528
    [Google Scholar]
  40. Tian T. Li S. Zhang Z. Chen L. Zou Z. Zhao D. Zeng J. Benchmarking compound activity prediction for real-world drug discovery applications. Commun. Chem. 2024 7 1 127 10.1038/s42004‑024‑01204‑4 38834746
    [Google Scholar]
  41. Huang Y. Gao B. Jia Y. Ma H. Ma W-Y. Zhang Y-Q. Lan Y. SIU: A million-scale structural small molecule-protein interaction dataset for unbiased bioactivity prediction. Biomolecules 2024 1 16
    [Google Scholar]
  42. Huckvale E.D. Powell C.D. Jin H. Moseley H.N.B. Benchmark dataset for training machine learning models to predict the pathway involvement of metabolites. Metabolites 2023 13 11 1120 10.3390/metabo13111120 37999216
    [Google Scholar]
  43. Ó Conchúir S. Barlow K.A. Pache R.A. Ollikainen N. Kundert K. O’Meara M.J. Smith C.A. Kortemme T. A web resource for standardized benchmark datasets, metrics, and rosetta protocols for macromolecular modeling and design. PLoS One 2015 10 9 e0130433 10.1371/journal.pone.0130433
    [Google Scholar]
  44. Stokes J.M. Yang K. Swanson K. Jin W. Cubillos-Ruiz A. Donghia N.M. MacNair C.R. French S. Carfrae L.A. Bloom-Ackermann Z. Tran V.M. Chiappino-Pepe A. Badran A.H. Andrews I.W. Chory E.J. Church G.M. Brown E.D. Jaakkola T.S. Barzilay R. Collins J.J. A deep learning approach to antibiotic discovery. Cell 2020 180 4 688 702.e13 10.1016/j.cell.2020.01.021 32084340
    [Google Scholar]
  45. Yamashita R. Nishio M. Do R.K.G. Togashi K. Convolutional neural networks: An overview and application in radiology. Insights Imaging 2018 9 4 611 629 10.1007/s13244‑018‑0639‑9 29934920
    [Google Scholar]
  46. Hu Z. Tang J. Wang Z. Zhang K. Zhang L. Sun Q. Deep learning for image-based cancer detection and diagnosis − A survey. Pattern Recognit. 2018 83 134 149 10.1016/j.patcog.2018.05.014
    [Google Scholar]
  47. Mayr A. Klambauer G. Unterthiner T. Hochreiter S. DeepTox: Toxicity prediction using deep learning. Front. Environ. Sci. 2016 3 10.3389/fenvs.2015.00080
    [Google Scholar]
  48. Zhang S. Tong H. Xu J. Maciejewski R. Graph convolutional networks: A comprehensive review. Comput. Soc. Netw. 2019 6 1 11 10.1186/s40649‑019‑0069‑y 37915858
    [Google Scholar]
  49. Schmidt R.M. Recurrent neural networks (RNNs): A gentle introduction and overview arXiv 2019
    [Google Scholar]
  50. Van Houdt G. Mosquera C. Nápoles G. A review on the long short-term memory model. Artif. Intell. Rev. 2020 53 8 5929 5955 10.1007/s10462‑020‑09838‑1
    [Google Scholar]
  51. Michelucci U. An introduction to autoencoders. ArXiv 2022
    [Google Scholar]
  52. Xie L. Xu L. Kong R. Chang S. Xu X. Improvement of prediction performance with conjoint molecular fingerprint in deep learning. Front. Pharmacol. 2020 11 606668 10.3389/fphar.2020.606668 33488387
    [Google Scholar]
  53. Hentabli H. Bengherbia B. Saeed F. Salim N. Nafea I. Toubal A. Nasser M. Convolutional neural network model based on 2D fingerprint for bioactivity prediction. Int. J. Mol. Sci. 2022 23 21 13230 10.3390/ijms232113230 36362018
    [Google Scholar]
  54. Gaudelet T. Day B. Jamasb A.R. Soman J. Regep C. Liu G. Hayter J.B.R. Vickers R. Roberts C. Tang J. Roblin D. Blundell T.L. Bronstein M.M. Taylor-King J.P. Utilizing graph machine learning within drug discovery and development. Brief. Bioinform. 2021 22 6 bbab159 10.1093/bib/bbab159 34013350
    [Google Scholar]
  55. El-Attar N.E. Hassan M.K. Alghamdi O.A. Awad W.A. Deep learning model for classification and bioactivity prediction of essential oil-producing plants from Egypt. Sci. Rep. 2020 10 1 21349 10.1038/s41598‑020‑78449‑1 33288845
    [Google Scholar]
  56. Liu R. Laxminarayan S. Reifman J. Wallqvist A. Enabling data-limited chemical bioactivity predictions through deep neural network transfer learning. J. Comput. Aided Mol. Des. 2022 36 12 867 878 10.1007/s10822‑022‑00486‑x 36272041
    [Google Scholar]
  57. Raschka S. Kaufman B. Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition. Methods 2020 180 89 110 10.1016/j.ymeth.2020.06.016 32645448
    [Google Scholar]
  58. Sturm N. Mayr A. Le Van T. Chupakhin V. Ceulemans H. Wegner J. Golib-Dzib J.F. Jeliazkova N. Vandriessche Y. Böhm S. Cima V. Martinovic J. Greene N. Vander Aa T. Ashby T.J. Hochreiter S. Engkvist O. Klambauer G. Chen H. Industry-scale application and evaluation of deep learning for drug target prediction. J. Cheminform. 2020 12 1 26 10.1186/s13321‑020‑00428‑5 33430964
    [Google Scholar]
  59. Brownlee J. Develop deep learning models on theano and tensorflow using keras. J. Chem. Inf. Model. 2019 53 1689 1699
    [Google Scholar]
  60. Ruder S. An overview of gradient descent optimization algorithms. arXiv 2016
    [Google Scholar]
  61. Bergstra J. Bengio Y. Random search for hyper-parameter optimization. J. Mach. Learn. Res. 2012 13 281 305 10.5555/2188385.2188395
    [Google Scholar]
  62. Srivastava N. Hinton G. Krizhevsky A. Sutskever I. Salakhutdinov R. Dropout: A simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 2014 15 1929 1958 10.5555/2627435.2670313
    [Google Scholar]
  63. Sokolova M. Lapalme G. A systematic analysis of performance measures for classification tasks. Inf. Process. Manage. 2009 45 4 427 437 10.1016/j.ipm.2009.03.002
    [Google Scholar]
  64. Lundberg S.M. Lee S-I. A unified approach to interpreting model predictions. Adv. Neural Inf. Process. Syst. 2017 1 10 10.5555/3295222.3295230
    [Google Scholar]
  65. Acuña-Guzman V. Montoya-Alfaro M.E. Negrón-Ballarte L.P. Solis-Calero C. A machine learning approach for predicting caco-2 cell permeability in natural products from the biodiversity in Peru. Pharmaceuticals 2024 17 6 750 10.3390/ph17060750 38931417
    [Google Scholar]
  66. Gahl M. Kim H.W. Glukhov E. Gerwick W.H. Cottrell G.W. PECAN Predicts Patterns of Cancer Cell Cytostatic Activity of Natural Products Using Deep Learning PECAN predicts patterns of cancer cell cytostatic activity of natural products using deep learning. J. Nat. Prod. 2024 87 3 567 575 10.1021/acs.jnatprod.3c00879 38349959
    [Google Scholar]
  67. Lai J. Hu J. Wang Y. Zhou X. Li Y. Zhang L. Liu Z. Privileged scaffold analysis of natural products with deep learning‐based indication prediction model. Mol. Inform. 2020 39 11 2000057 10.1002/minf.202000057 32406179
    [Google Scholar]
  68. Liu Z. Huang D. Zheng S. Song Y. Liu B. Sun J. Niu Z. Gu Q. Xu J. Xie L. Deep learning enables discovery of highly potent anti-osteoporosis natural products. Eur. J. Med. Chem. 2021 210 112982 10.1016/j.ejmech.2020.112982 33158578
    [Google Scholar]
  69. Hamza H. Nasser M. Salim N. Saeed F. Bioactivity prediction using convolutional neural network. Emerging Trends in Intelligent Computing and Informatics Springer 2020 341 351 10.1007/978‑3‑030‑33582‑3_33
    [Google Scholar]
  70. Kumari M. Subbarao N. Deep learning model for virtual screening of novel 3C-like protease enzyme inhibitors against SARS coronavirus diseases. Comput. Biol. Med. 2021 132 104317 10.1016/j.compbiomed.2021.104317 33721736
    [Google Scholar]
  71. Tang H. Optimization model performance through pruning techniques. International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023), 2023, pp. 43–54. 10.2991/978‑94‑6463‑370‑2_6
    [Google Scholar]
  72. Galushka M. Swain C. Browne F. Mulvenna M.D. Bond R. Gray D. Prediction of chemical compounds properties using a deep learning model. Neural Comput. Appl. 2021 33 20 13345 13366 10.1007/s00521‑021‑05961‑4
    [Google Scholar]
/content/journals/npj/10.2174/0122103155332267241122143118
Loading
Deep Learning Approaches for Predicting Bioactivity of Natural Compounds
Bentham Science Publishers ; https://doi.org/10.2174/0122103155332267241122143118
/content/journals/npj/10.2174/0122103155332267241122143118
/content/journals/npj/10.2174/0122103155332267241122143118
Loading

Data & Media loading...


  • Article Type:     Review Article
Keywords: drug discovery ; bioactivity prediction ; Deep learning ; neural networks ; natural compounds ; computational biology

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test