44
Views
0
CrossRef citations to date
0
Altmetric
Original Research

Machine learning and deep learning approaches for enhanced prediction of hERG blockade: a comprehensive QSAR modeling study

, , , , , , , & show all
Pages 665-684 | Received 27 Feb 2024, Accepted 26 Jun 2024, Published online: 10 Jul 2024

References

  • Craveiro NS, Lopes BS, Tomas L, et al. Drug withdrawal due to safety: a review of the data supporting withdrawal decision. Curr Drug Saf. 2020;15(1):4–12. doi: 10.2174/1574886314666191004092520
  • Kocadal K, Saygi S, Alkas FB, et al. Drug-associated cardiovascular risks: a retrospective evaluation of withdrawn drugs. North Clin Istanb. 2019;6(2):196–202. doi: 10.14744/nci.2018.44977
  • Babcock JJ, Li M. hERG channel function: beyond long QT. Acta Pharmacol Sin. 2013 Mar;34(3):329–335. doi: 10.1038/aps.2013.6
  • Rampe D, Brown AM. A history of the role of the hERG channel in cardiac risk assessment. J Pharmacol Toxicol Methods. 2013 Jul;68(1):13–22. doi: 10.1016/j.vascn.2013.03.005
  • John VH, Dale TJ, Hollands EC, et al. Novel 384-well population patch clamp electrophysiology assays for Ca2±activated K+ channels. J Biomol Screen. 2007 Feb;12(1):50–60. doi: 10.1177/1087057106294920
  • Nomura T. In vitro patch-clamp. Methods Mol Biol. 2024;2794:221–244.
  • Miranda P, Manso DG, Barros F, et al. FRET with multiply labeled HERG K(+) channels as a reporter of the in vivo coarse architecture of the cytoplasmic domains. Bba-Mol Cell Res. 2008 Oct;1783(10):1681–1699. doi: 10.1016/j.bbamcr.2008.06.009
  • Margulis M, Sorota S, Chu I, et al. Protein binding-dependent decreases in hERG channel blocker potency assessed by whole-cell voltage clamp in serum. J Cardiovasc Pharmacol. 2010 Apr;55(4):368–376. doi: 10.1097/FJC.0b013e3181d2ce39
  • Weaver CD. Thallium flux assay for measuring the activity of monovalent cation channels and transporters. Methods Mol Biol. 2018;1684:105–114.
  • Schmalhofer WA, Swensen AM, Thomas BS, et al. A pharmacologically validated, high-capacity, functional thallium flux assay for the human ether-a-go-go related gene potassium channel. Assay Drug Dev Technol. 2010 Dec;8(6):714–726. doi: 10.1089/adt.2010.0351
  • Zhao J, Xia M. Cell-based hERG channel inhibition assay in high-throughput format. Methods Mol Biol. 2022;2474:21–28.
  • Rogers M, Obergrussberger A, Kondratskyi A, et al. Using automated patch clamp electrophysiology platforms in ion channel drug discovery: an industry perspective. Expert Opin Drug Discov. 2024 May;19(5):523–535. doi: 10.1080/17460441.2024.2329104
  • Vicente J. Update on the ECG component of the CiPA initiative. J Electrocardiol. 2018 Nov;51(6S):S98–S102. doi: 10.1016/j.jelectrocard.2018.08.003
  • Priest BT, Bell IM, Garcia ML. Role of hERG potassium channel assays in drug development. Channels (Austin). 2008 Mar;2(2):87–93. doi: 10.4161/chan.2.2.6004
  • Kim T, Chung KC, Park H. Derivation of highly predictive 3D-QSAR models for hERG channel blockers based on the quantum artificial neural network algorithm. Pharmaceuticals (Basel). 2023 Oct 24;16(11):1509. doi: 10.3390/ph16111509
  • Karim A, Lee M, Balle T, et al. CardioTox net: a robust predictor for hERG channel blockade based on deep learning meta-feature ensembles. J Cheminform. 2021 Aug 16;13(1):60. doi: 10.1186/s13321-021-00541-z
  • Ryu JY, Lee MY, Lee JH, et al. DeepHIT: a deep learning framework for prediction of hERG-induced cardiotoxicity. Bioinformatics. 2020 May 1;36(10):3049–3055. doi: 10.1093/bioinformatics/btaa075
  • Delre P, Lavado GJ, Lamanna G, et al. Ligand-based prediction of hERG-mediated cardiotoxicity based on the integration of different machine learning techniques. Front Pharmacol. 2022;13:951083. doi: 10.3389/fphar.2022.951083
  • Krishna S, Borrel A, Huang R, et al. High-throughput chemical screening and structure-based models to predict hERG inhibition. Biology (Basel). 2022 Jan 28;11(2):209. doi: 10.3390/biology11020209
  • Lee HM, Yu MS, Kazmi SR, et al. Computational determination of hERG-related cardiotoxicity of drug candidates. BMC Bioinformatics. 2019 May 29;20(Suppl 10):250. doi: 10.1186/s12859-019-2814-5
  • Braga RC, Alves VM, Silva MF, et al. Pred-hERG: a novel web-accessible computational tool for predicting cardiac toxicity. Mol Inform. 2015 Oct;34(10):698–701. doi: 10.1002/minf.201500040
  • Li X, Zhang Y, Li H, et al. Modeling of the hERG K+ channel blockage using online chemical database and modeling environment (OCHEM). Mol Inform. 2017 Dec;36(12). doi: 10.1002/minf.201700074
  • Siramshetty VB, Nguyen DT, Martinez NJ, et al. Critical assessment of artificial intelligence methods for prediction of hERG channel inhibition in the “Big Data” Era. J Chem Inf Model. 2020 Dec 28;60(12):6007–6019. doi: 10.1021/acs.jcim.0c00884
  • Kim H, Nam H. hERG-att: self-attention-based deep neural network for predicting hERG blockers. Comput Biol Chem. 2020 May 19;87:107286. doi: 10.1016/j.compbiolchem.2020.107286
  • Kim H, Park M, Lee I, et al. BayeshERG: a robust, reliable and interpretable deep learning model for predicting hERG channel blockers. Brief Bioinform. 2022 Jul 18;23(4). doi: 10.1093/bib/bbac211
  • Cai C, Guo P, Zhou Y, et al. Deep learning-based prediction of drug-induced cardiotoxicity. J Chem Inf Model. 2019 Mar 25;59(3):1073–1084. doi: 10.1021/acs.jcim.8b00769
  • Creanza TM, Delre P, Ancona N, et al. Structure-based prediction of hERG-related cardiotoxicity: a benchmark study. J Chem Inf Model. 2021 Sep 27;61(9):4758–4770. doi: 10.1021/acs.jcim.1c00744
  • Liu M, Zhang L, Li S, et al. Prediction of hERG potassium channel blockage using ensemble learning methods and molecular fingerprints. Toxicol Lett. 2020 Oct 10;332:88–96. doi: 10.1016/j.toxlet.2020.07.003
  • Zhang C, Zhou Y, Gu S, et al. In silico prediction of hERG potassium channel blockage by chemical category approaches. Toxicol Res (Camb). 2016 Mar 1;5(2):570–582. doi: 10.1039/C5TX00294J
  • Wang S, Sun H, Liu H, et al. ADMET evaluation in drug discovery. 16. Predicting hERG blockers by combining multiple pharmacophores and machine learning approaches. Mol Pharm. 2016 Aug 1;13(8):2855–2866. doi: 10.1021/acs.molpharmaceut.6b00471
  • Sun H, Huang R, Xia M, et al. Prediction of hERG liability – using SVM classification, bootstrapping and jackknifing. Mol Inform. 2017 Apr;36(4). doi: 10.1002/minf.201600126
  • Chavan S, Abdelaziz A, Wiklander JG, et al. A k-nearest neighbor classification of hERG K(+) channel blockers. J Comput Aided Mol Des. 2016 Mar;30(3):229–236. doi: 10.1007/s10822-016-9898-z
  • Ogura K, Sato T, Yuki H, et al. Support vector machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II. Sci Rep. 2019 Aug 21;9(1):12220. doi: 10.1038/s41598-019-47536-3
  • Racz A, Bajusz D, Miranda-Quintana RA, et al. Machine learning models for classification tasks related to drug safety. Mol Divers. 2021 Aug;25(3):1409–1424. doi: 10.1007/s11030-021-10239-x
  • Lanevskij K, Didziapetris R, Sazonovas A. Physicochemical QSAR analysis of hERG inhibition revisited: towards a quantitative potency prediction. J Comput Aided Mol Des. 2022 Dec;36(12):837–849. doi: 10.1007/s10822-022-00483-0
  • Singh AV, Varma M, Rai M, et al. Advancing predictive risk assessment of chemicals via integrating machine learning, computational modeling, and chemical/nano-quantitative structure-activity relationship approaches. Adv Intell Syst-Ger. 2024 Apr;6(4). doi: 10.1002/aisy.202300366
  • Karim A, Lee M, Balle T, et al. CardioTox training data from: CardioTox net: a robust predictor for hERG channel blockade based on deep learning meta-feature ensembles. 2021 Aug 16 [cited 2023 Jun 21]. Available from: https://github.com/Abdulk084/CardioTox/blob/master/data/train_validation_cardio_tox_data.tar.xz
  • Online SMILES Translator and Structure File Generator. [cited 2023 Jun 28]. Available from: https://cactus.nci.nih.gov/translate/
  • Karim A, Lee M, Balle T, et al. CardioTox external validation dataset-1 from: CardioTox net: a robust predictor for hERG channel blockade based on deep learning meta-feature ensembles. 2021 Aug 16 [cited 2023 Jun 21]. Available from: https://github.com/Abdulk084/CardioTox/blob/master/data/external_test_set_pos.csv
  • Karim A, Lee M, Balle T, et al. CardioTox external validation dataset-2 from: CardioTox net: a robust predictor for hERG channel blockade based on deep learning meta-feature ensembles. 2021 Aug 16 [cited 2023 Jun 21]. Available from: https://github.com/Abdulk084/CardioTox/blob/master/data/external_test_set_new.csv
  • Mold2. [cited 2023 Jun 28]. Available from: https://www.fda.gov/science-research/bioinformatics-tools/mold2
  • Hong H, Xie Q, Ge W, et al. Mold(2), molecular descriptors from 2D structures for chemoinformatics and toxicoinformatics. J Chem Inf Model. 2008 Jul;48(7):1337–1344. doi: 10.1021/ci800038f
  • Hong H, Liu J, Ge W, et al. Mold2 descriptors facilitate development of machine learning and deep learning models for predicting toxicity of chemicals. In: Hong H, editor. Machine learning and deep learning in computational toxicology; Computational methods in engineering & the sciences; 2023. Cham: Springer International Publishing; p. 297–321. doi:10.1007/978-3-031-20730-3_12
  • Chen M, Hong H, Fang H, et al. Quantitative structure-activity relationship models for predicting drug-induced liver injury based on FDA-approved drug labeling annotation and using a large collection of drugs. Toxicol Sci. 2013 Nov;136(1):242–249. doi: 10.1093/toxsci/kft189
  • Shen J, Xu L, Fang H, et al. EADB: an estrogenic activity database for assessing potential endocrine activity. Toxicol Sci. 2013 Oct;135(2):277–291. doi: 10.1093/toxsci/kft164
  • Ng HW, Doughty SW, Luo H, et al. Development and validation of decision forest model for estrogen receptor binding prediction of chemicals using large data sets. Chem Res Toxicol. 2015 Dec 21;28(12):2343–2351. doi: 10.1021/acs.chemrestox.5b00358
  • Sakkiah S, Selvaraj C, Gong P, et al. Development of estrogen receptor beta binding prediction model using large sets of chemicals. Oncotarget. 2017 Nov 3;8(54):92989–93000. doi: 10.18632/oncotarget.21723
  • Hong H, Rua D, Sakkiah S, et al. Consensus modeling for prediction of estrogenic activity of ingredients commonly used in sunscreen products. Int J Environ Res Public Health. 2016 Sep 29;13(10):958. doi: 10.3390/ijerph13100958
  • Hong H, Harvey BG, Palmese GR, et al. Experimental data extraction and in silico prediction of the estrogenic activity of renewable replacements for bisphenol a. Int J Environ Res Public Health. 2016 Jul 12;13(7):705. doi: 10.3390/ijerph13070705
  • Hong H, Shen J, Ng HW, et al. A rat alpha-fetoprotein binding activity prediction model to facilitate assessment of the endocrine disruption potential of environmental chemicals. Int J Environ Res Public Health. 2016 Mar 25;13(4):372. doi: 10.3390/ijerph13040372
  • Hong H, Thakkar S, Chen M, et al. Development of decision forest models for prediction of drug-induced liver injury in humans using a large set of FDA-approved drugs. Sci Rep. 2017 Dec 11;7(1):17311. doi: 10.1038/s41598-017-17701-7
  • Liu J, Guo W, Dong F, et al. Machine learning models for rat multigeneration reproductive toxicity prediction. Front Pharmacol. 2022;13:1018226. doi: 10.3389/fphar.2022.1018226
  • Liu J, Xu L, Guo W, et al. Developing a SARS-CoV-2 main protease binding prediction random forest model for drug repurposing for COVID-19 treatment. Exp Biol Med (Maywood). 2023 Nov;248(21):1927–1936. doi: 10.1177/15353702231209413
  • Godden JW, Stahura FL, Bajorath J. Variability of molecular descriptors in compound databases revealed by Shannon entropy calculations. J Chem Inf Comput Sci. 2000 May;40(3):796–800. doi: 10.1021/ci000321u
  • Shi L, Tong W, Fang H, et al. An integrated “4-phase” approach for setting endocrine disruption screening priorities–phase I and II predictions of estrogen receptor binding affinity. SAR QSAR Environ Res. 2002 Mar;13(1):69–88. doi: 10.1080/10629360290002235
  • Hong H, Tong W, Xie Q, et al. An in silico ensemble method for lead discovery: decision forest. SAR QSAR Environ Res. 2005 Aug;16(4):339–347. doi: 10.1080/10659360500203022
  • Luo H, Ye H, Ng HW, et al. Machine learning methods for predicting HLA-peptide binding activity. Bioinform Biol Insights. 2015;9(Suppl 3):21–29. doi: 10.4137/BBI.S29466
  • Ye H, Luo H, Ng HW, et al. Applying network analysis and Nebula (neighbor-edges based and unbiased leverage algorithm) to ToxCast data. Environ Int. 2016 Apr;89-90:81–92. doi: 10.1016/j.envint.2016.01.010
  • Idakwo G, Thangapandian S, Luttrell J, et al. Structure-activity relationship-based chemical classification of highly imbalanced Tox21 datasets. J Cheminform. 2020 Oct 27;12(1):66. doi: 10.1186/s13321-020-00468-x
  • Huang Y, Li X, Xu S, et al. Quantitative structure-activity relationship models for predicting inflammatory potential of metal oxide nanoparticles. Environ Health Perspect. 2020 Jun;128(6):67010. doi: 10.1289/EHP6508
  • Wang Z, Chen J, Hong H. Developing QSAR models with defined applicability domains on PPARgamma binding affinity using large data sets and machine learning algorithms. Environ Sci Technol. 2021 May 18;55(10):6857–6866. doi: 10.1021/acs.est.0c07040
  • Guo W, Liu J, Dong F, et al. Deep learning models for predicting gas adsorption capacity of nanomaterials. Nanomaterials (Basel). 2022 Sep 27;12(19):3376. doi: 10.3390/nano12193376
  • Liu J, Guo W, Sakkiah S, et al. Machine learning models for predicting liver toxicity. Methods Mol Biol. 2022;2425:393–415.
  • Ji Z, Guo W, Wood EL, et al. Machine learning models for predicting cytotoxicity of nanomaterials. Chem Res Toxicol. 2022 Feb 21;35(2):125–139. doi: 10.1021/acs.chemrestox.1c00310
  • Tang W, Liu W, Wang Z, et al. Machine learning models on chemical inhibitors of mitochondrial electron transport chain. J Hazard Mater. 2022 Mar 15;426:128067. doi: 10.1016/j.jhazmat.2021.128067
  • Guo W, Liu J, Dong F, et al. Review of machine learning and deep learning models for toxicity prediction. Exp Biol Med (Maywood). 2023 Nov;248(21):1952–1973. doi: 10.1177/15353702231209421
  • Python. Available from: https://www.python.org/downloads/release/python-385/
  • Scikit-learn. Available from: https://scikit-learn.org/stable/whats_new/v1.2.html
  • PyTorch. Available from: https://pytorch.org/get-started/pytorch-2.0/
  • Cover TM, Hart PE. Nearest neighbor pattern classification. IEEE T Inf Theory. 1967;13(1):21±. doi: 10.1109/TIT.1967.1053964
  • Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995 Sep;20(3):273–297. doi: 10.1007/BF00994018
  • Breiman L. Random forests. Mach Learn. 2001 Oct;45(1):5–32. doi: 10.1023/A:1010933404324
  • Rosenblatt F. The perceptron – a probabilistic model for information-storage and organization in the brain. Psychol Rev. 1958;65(6):386–408. doi: 10.1037/h0042519
  • Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997 Nov 15;9(8):1735–1780. doi: 10.1162/neco.1997.9.8.1735
  • Vincent P, Larochelle H, Lajoie I, et al. Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res. 2010 Dec;11:3371–3408.
  • Opitz D, Maclin R. Popular ensemble methods: An empirical study. J Artif Intel Res. 1999;11(1):169–198. doi: 10.1613/jair.614
  • Chandrasekar V, Ansari MY, Singh AV, et al. Investigating the use of machine learning models to understand the drugs permeability across placenta. IEEE Access. 2023;11:52726–52739. doi: 10.1109/ACCESS.2023.3272987
  • Kar S, Roy K, Leszczynski J. Applicability domain: a step toward confident predictions and decidability for QSAR modeling. Methods Mol Biol. 2018;1800:141–169.
  • Singh AV, Shelar A, Rai M, et al. Harmonization risks and rewards: nano-QSAR for agricultural nanomaterials. J Agric Food Chem. 2024 Feb 14;72(6):2835–2852. doi: 10.1021/acs.jafc.3c06466
  • Wisniowska B, Mendyk A, Szlek J, et al. Enhanced QSAR models for drug-triggered inhibition of the main cardiac ion currents. J Appl Toxicol. 2015 Sep;35(9):1030–1039. doi: 10.1002/jat.3095
  • Singh AV, Varma M, Laux P, et al. Artificial intelligence and machine learning disciplines with the potential to improve the nanotoxicology and nanomedicine fields: a comprehensive review. Arch Toxicol. 2023 Apr;97(4):963–979. doi: 10.1007/s00204-023-03471-x
  • Park JS, Jeon JY, Yang JH, et al. Introduction to in silico model for proarrhythmic risk assessment under the CiPA initiative. Transl Clin Pharmacol. 2019 Mar;27(1):12–18. doi: 10.12793/tcp.2019.27.1.12
  • Hong HX, Xu L, Liu J, et al. Technical reproducibility of genotyping SNP arrays used in genome-wide association studies. PLOS ONE. 2012 Sep 7;7(9):e44483. doi: 10.1371/journal.pone.0044483
  • Moriwaki H, Tian YS, Kawashita N, et al. Mordred: a molecular descriptor calculator. J Cheminf. 2018 Feb 6;10(1). doi: 10.1186/s13321-018-0258-y
  • Li Z, Huang RL, Xia MH, et al. Fingerprinting interactions between proteins and ligands for facilitating machine learning in drug discovery. Biomol. 2024 Jan;14(1):72. doi: 10.3390/biom14010072

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.