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2000
Volume 32, Issue 24
  • ISSN: 0929-8673
  • E-ISSN: 1875-533X

Abstract

Introduction

Identification of drug-target interactions (DTI) is a crucial step in drug development with high specificity and low toxicity. To accelerate the process, computer-aided DTI prediction algorithms have been used to screen compounds or targets rapidly. Furthermore, DTI prediction can be used to identify potential targets for existing drugs, thus uncovering new indications and repositioning them. Therefore, it is of great importance to develop efficient and accurate DTI prediction algorithms.

Methods

Current algorithms usually represent drugs as extracted features, which are learned by convolutional neural networks (CNNs) from its linear representation, or utilize graph neural networks (GNNs) to learn its graph representation. However, these methods either lose information or fail to capture the structural information of the drug. To address this issue, a novel molecule secondary structure representation network (MSSRN) is proposed to learn drug characterization more accurately. Firstly, the network performs relational graph convolutional networks (R-GCNs) on the drug's molecular graph and integrates drug sequence convolutions to learn the sequential information. Secondly, inspired by the attention mechanism, spatial importance weights of the drug sequence are calculated to guide R-GCNs to learn the topological information of the drug.

Results

A drug-target affinity model, called MSSRN-DTA, was then constructed by using MSSRN to learn drug structure and CNN to learn protein sequence.

Conclusion

The effectiveness of the proposed method is verified by comparing it with other alternative methods and baseline models on two benchmark datasets.

© 2025 Bentham Science Publishers
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2025-10-22

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References

  1. HughesJ.P. ReesS. KalindjianS.B. PhilpottK.L. Principles of early drug discovery.Br. J. Pharmacol.201116261239124910.1111/j.1476‑5381.2010.01127.x21091654
     [Google Scholar]
  2. DiMasiJ.A. FeldmanL. SecklerA. WilsonA. Trends in risks associated with new drug development: success rates for investigational drugs.Clin. Pharmacol. Ther.201087327227710.1038/clpt.2009.29520130567
     [Google Scholar]
  3. PaulS.M. MytelkaD.S. DunwiddieC.T. PersingerC.C. MunosB.H. LindborgS.R. SchachtA.L. How to improve R&D productivity: The pharmaceutical industry’s grand challenge.Nat. Rev. Drug Discov.20109320321410.1038/nrd307820168317
     [Google Scholar]
  4. HayM. ThomasD.W. CraigheadJ.L. EconomidesC. RosenthalJ. Clinical development success rates for investigational drugs.Nat. Biotechnol.2014321405110.1038/nbt.278624406927
     [Google Scholar]
  5. SantosR. UrsuO. GaultonA. BentoA.P. DonadiR.S. BologaC.G. KarlssonA. Al-LazikaniB. HerseyA. OpreaT.I. OveringtonJ.P. A comprehensive map of molecular drug targets.Nat. Rev. Drug Discov.2017161193410.1038/nrd.2016.23027910877
     [Google Scholar]
  6. DeansR.M. MorgensD.W. ÖkesliA. PillayS. HorlbeckM.A. KampmannM. GilbertL.A. LiA. MateoR. SmithM. GlennJ.S. CaretteJ.E. KhoslaC. BassikM.C. Parallel shRNA and CRISPR-Cas9 screens enable antiviral drug target identification.Nat. Chem. Biol.201612536136610.1038/nchembio.205027018887
     [Google Scholar]
  7. NascimentoA.C.A. PrudêncioR.B.C. CostaI.G. A multiple kernel learning algorithm for drug-target interaction prediction.BMC Bioinformatics20161714610.1186/s12859‑016‑0890‑326801218
     [Google Scholar]
  8. GashawI. EllinghausP. SommerA. AsadullahK. What makes a good drug target?Drug Discov. Today20111623-241037104310.1016/j.drudis.2011.09.00721945861
     [Google Scholar]
  9. YamanishiY. ArakiM. GutteridgeA. HondaW. KanehisaM. Prediction of drug–target interaction networks from the integration of chemical and genomic spaces.Bioinformatics20082413i232i24010.1093/bioinformatics/btn16218586719
     [Google Scholar]
  10. ChengZ. ZhouS. WangY. LiuH. GuanJ. ChenY.P.P. Effectively identifying compound-protein interactions by learning from positive and unlabeled examples.IEEE/ACM Trans. Comput. Biol. Bioinformatics20181561832184310.1109/TCBB.2016.257021128113437
     [Google Scholar]
  11. PahikkalaT. AirolaA. PietiläS. ShakyawarS. SzwajdaA. TangJ. AittokallioT. Toward more realistic drug-target interaction predictions.Brief. Bioinform.201516232533710.1093/bib/bbu01024723570
     [Google Scholar]
  12. HeT. HeidemeyerM. BanF. CherkasovA. EsterM. SimBoost: a read-across approach for predicting drug–target binding affinities using gradient boosting machines.J. Cheminform.2017912410.1186/s13321‑017‑0209‑z29086119
     [Google Scholar]
  13. HintonG. DengL. YuD. DahlG. MohamedA. JaitlyN. SeniorA. VanhouckeV. NguyenP. SainathT. KingsburyB. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups.IEEE Signal Process. Mag.20122968297[J].10.1109/MSP.2012.2205597
     [Google Scholar]
  14. MaJ. SheridanR.P. LiawA. DahlG.E. SvetnikV. Deep neural nets as a method for quantitative structure-activity relationships.J. Chem. Inf. Model.201555226327410.1021/ci500747n25635324
     [Google Scholar]
  15. HiroharaM. SaitoY. KodaY. SatoK. SakakibaraY. Convolutional neural network based on SMILES representation of compounds for detecting chemical motif.BMC Bioinformatics201819S19Suppl. 1952610.1186/s12859‑018‑2523‑530598075
     [Google Scholar]
  16. ÖztürkH. ÖzgürA. OzkirimliE. DeepDTA: deep drug–target binding affinity prediction.Bioinformatics20183417i821i82910.1093/bioinformatics/bty59330423097
     [Google Scholar]
  17. ZhengX. HeS. SongX. ZhangZ. BoX. DTI-RCNN: New efficient hybrid neural network model to predict drug–target interactions [C].International Conference on Artificial Neural NetworksSpringer, Cham. 2018, vol, 11139., pp. 104-114.10.1007/978‑3‑030‑01418‑6_11
     [Google Scholar]
  18. WangH. WangJ. DongC. LianY. LiuD. YanZ. A novel approach for drug-target interactions prediction based on multimodal deep autoencoder.Front. Pharmacol.202010159210.3389/fphar.2019.0159232047432
     [Google Scholar]
  19. NguyenT. LeH. QuinnT.P. NguyenT. LeT.D. VenkateshS. GraphDTA: predicting drug–target binding affinity with graph neural networks.Bioinformatics20213781140114710.1093/bioinformatics/btaa92133119053
     [Google Scholar]
  20. ZhangZ. ChenL. ZhongF. WangD. JiangJ. ZhangS. JiangH. ZhengM. LiX. Graph neural network approaches for drug-target interactions.Curr. Opin. Struct. Biol.202273102327.10.1016/j.sbi.2021.10232735074533
     [Google Scholar]
  21. LimJ. RyuS. ParkK. ChoeY.J. HamJ. KimW.Y. Predicting drug–target interaction using a novel graph neural network with 3D structure-embedded graph representation.J. Chem. Inf. Model.20195993981398810.1021/acs.jcim.9b0038731443612
     [Google Scholar]
  22. DavidL. ThakkarA. MercadoR. EngkvistO. Molecular representations in AI-driven drug discovery: A review and practical guide.J. Cheminform.20201215610.1186/s13321‑020‑00460‑533431035
     [Google Scholar]
  23. WangL. YouZ.H. ChenX. YanX. LiuG. ZhangW. RFDT: A rotation forest-based predictor for predicting drug-target interactions using drug structure and protein sequence information.Curr. Protein Pept. Sci.201819544545410.2174/138920371866616111411165627842479
     [Google Scholar]
  24. WuY. GaoM. ZengM. ZhangJ. LiM. BridgeDPI: A novel graph neural network for predicting drug-protein interactions.arXiv preprint2021210112547https://arxiv.org/abs/2101.12547
     [Google Scholar]
  25. KipfT.N. WellingM. Semi-supervised classification with graph convolutional networks.arXiv preprint 201616090290710.48550/arXiv.1609.02907
     [Google Scholar]
  26. ElikoviP. CucurullG. CasanovaA. RomeroA. LiòP. BengioY. Graph attention networks.Stat.2017105020
     [Google Scholar]
  27. LiuZ. ZhouJ. Introduction to graph neural networks.Synth. Lect. Artif. Intell. Mach. Learn.2020142112710.1007/978‑3‑031‑01587‑8
     [Google Scholar]
  28. TsubakiM. TomiiK. SeseJ. Compound–protein interaction prediction with end-to-end learning of neural networks for graphs and sequences.Bioinformatics201935230931810.1093/bioinformatics/bty53529982330
     [Google Scholar]
  29. NiuZ. ZhongG. YuH. A review on the attention mechanism of deep learning.Neurocomputing2021452486210.1016/j.neucom.2021.03.091
     [Google Scholar]
  30. LiuZ. LuoF. DuB. RNA secondary structure representation network for RNA-proteins binding prediction[C].Proceedings of the AAAI conference on artificial intelligence2021, 35(1), pp 362-370.10.1609/aaai.v35i1.16112
     [Google Scholar]
  31. DavisM.I. HuntJ.P. HerrgardS. CiceriP. WodickaL.M. PallaresG. HockerM. TreiberD.K. ZarrinkarP.P. Comprehensive analysis of kinase inhibitor selectivity.Nat. Biotechnol.201129111046105110.1038/nbt.199022037378
     [Google Scholar]
  32. TangJ. SzwajdaA. ShakyawarS. XuT. HintsanenP. WennerbergK. AittokallioT. Making sense of large-scale kinase inhibitor bioactivity data sets: A comparative and integrative analysis.J. Chem. Inf. Model.201454373574310.1021/ci400709d24521231
     [Google Scholar]
  33. SchlichtkrullM. KipfT.N. BloemP. BergR.V.D. TitovI. WillingM. Modeling relational data with graph convolutional networks.European Semantic Web Conference2018, vol 10843. Springer, Cham. pp 593-607.10.1007/978‑3‑319‑93417‑4_38
     [Google Scholar]
  34. ÖztürkH. OzkirimliE. ÖzgürA. WideDTA: Prediction of drug-target binding affinity.arXiv preprint 2019190204166
     [Google Scholar]
  35. SigristC.J.A. CeruttiL. de CastroE. Langendijk-GenevauxP.S. BulliardV. BairochA. HuloN. PROSITE, a protein domain database for functional characterization and annotation.Nucleic Acids Res.201038Database issueSuppl. 1D161D16610.1093/nar/gkp88519858104
     [Google Scholar]
  36. JumperJ. EvansR. PritzelA. GreenT. FigurnovM. RonnebergerO. TunyasuvunakoolK. BatesR. ŽídekA. PotapenkoA. BridglandA. MeyerC. KohlS.A.A. BallardA.J. CowieA. Romera-ParedesB. NikolovS. JainR. AdlerJ. BackT. PetersenS. ReimanD. ClancyE. ZielinskiM. SteineggerM. PacholskaM. BerghammerT. BodensteinS. SilverD. VinyalsO. SeniorA.W. KavukcuogluK. KohliP. HassabisD. Highly accurate protein structure prediction with AlphaFold.Nature2021596787358358910.1038/s41586‑021‑03819‑234265844
     [Google Scholar]
  37. LuoY. ZhaoX. ZhouJ. YangJ. ZhangY. KuangW. PengJ. ChenL. ZengJ. A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information.Nat. Commun.20178157310.1038/s41467‑017‑00680‑828924171
     [Google Scholar]
  38. WenM. ZhangZ. NiuS. ShaH. YangR. YunY. LuH. Deep-learning-based drug–target interaction prediction.J. Proteome Res.20171641401140910.1021/acs.jproteome.6b0061828264154
     [Google Scholar]
  39. KamilarisA. Prenafeta-BoldúF.X. Deep learning in agriculture: A survey.Comput. Electron. Agric.2018147709010.1016/j.compag.2018.02.016
     [Google Scholar]
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Drug-target Affinity Prediction by Molecule Secondary Structure Representation Network
Curr. Med. Chem. 32, 5095 (2025); https://doi.org/10.2174/0109298673252287240215103035
/content/journals/cmc/10.2174/0109298673252287240215103035
/content/journals/cmc/10.2174/0109298673252287240215103035
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  • Article Type:     Research Article
Keyword(s): attention mechanism; deep learning; Drug-target affinity; molecular structure representation; neural networks; relational graph convolutional networks

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