Recent progress in machine learning approaches for predicting carcinogenicity in drug development

References

• Bourcier T, McGovern T, Stavitskaya L, et al. Improving prediction of carcinogenicity to reduce, refine, and replace the use of experimental animals. J Am Assoc Lab Anim Sci. 2015;54(2):163–169.
   PubMed Web of Science ®Google Scholar
• van der Laan JW, Buitenhuis WHW, Wagenaar L, et al. Prediction of the carcinogenic potential of human pharmaceuticals using repeated dose toxicity data and their pharmacological properties. Front Med. 2016;3. doi: 10.3389/fmed.2016.00045
   Web of Science ®Google Scholar
• Li T, Tong W, Roberts R, et al. DeepCarc: Deep learning-powered carcinogenicity prediction using model-level representation. Front Artif Intell. 2021;4. doi: 10.3389/frai.2021.757780
   Google Scholar
• Tice RR, Bassan A, Amberg A, et al. In silico approaches in carcinogenicity hazard assessment: current status and future needs. Comput Toxicol. 2021;20:100191. doi: 10.1016/j.comtox.2021.100191
   PubMedGoogle Scholar
• Felter SP, Bhat VS, Botham PA, et al. Assessing chemical carcinogenicity: hazard identification, classification, and risk assessment. Insight from a toxicology forum state-of-the-science workshop. Crit Rev Toxicol. 2021;51(8):653–694. doi: 10.1080/10408444.2021.2003295
   PubMed Web of Science ®Google Scholar
• David L, Thakkar A, Mercado R, et al. Molecular representations in AI-driven drug discovery: a review and practical guide. J Cheminform. 2020;12(1):56. doi: 10.1186/s13321-020-00460-5
   PubMed Web of Science ®Google Scholar
• Choudhary N, Bharti R, Sharma R. Role of artificial intelligence in chemistry. Mater Today Proc. 2022;48:1527–1533. doi: 10.1016/j.matpr.2021.09.428
   Google Scholar
• Baum ZJ, Yu X, Ayala PY, et al. Artificial intelligence in chemistry: current trends and future directions. J Chem Inf Model. 2021;61(7):3197–3212. doi: 10.1021/acs.jcim.1c00619
   PubMed Web of Science ®Google Scholar
• Dong S, Wang P, Abbas K. A survey on deep learning and its applications. Comput Sci Rev. 2021;40:100379. doi: 10.1016/j.cosrev.2021.100379
   Web of Science ®Google Scholar
• Le NQK. Potential of deep representative learning features to interpret the sequence information in proteomics. Proteomics. 2022;22(1–2):2100232. doi: 10.1002/pmic.202100232
   Web of Science ®Google Scholar
• Farhana A, et al. Cancer bioenergetics and tumor microenvironments—enhancing chemotherapeutics and targeting resistant niches through nanosystems. Cancers (Basel). 2023;15(15):3836. doi: 10.3390/cancers15153836
   PubMed Web of Science ®Google Scholar
• Doll R, Hill AB. Smoking and carcinoma of the lung. Br Med J. 1950;2(4682):739. doi: 10.1136/bmj.2.4682.739
   PubMed Web of Science ®Google Scholar
• Keller DA, Bassan A, Amberg A, et al. In silico approaches in carcinogenicity hazard assessment: case study of pregabalin, a nongenotoxic mouse carcinogen. Front Toxicol. 2023;5:5. doi: 10.3389/ftox.2023.1234498
   Google Scholar
• Downes N, Foster J. Regulatory forum opinion piece: carcinogen risk assessment: the move from screens to science*. Toxicol Pathol. 2015;43(8):1064–1073. doi: 10.1177/0192623315598578
   PubMed Web of Science ®Google Scholar
• Krska SW, DiRocco DA, Dreher SD, et al. The evolution of chemical high-throughput experimentation to address challenging problems in pharmaceutical synthesis. Acc Chem Res. 2017;50(12):2976–2985. doi: 10.1021/acs.accounts.7b00428
   PubMed Web of Science ®Google Scholar
• Gini G, Lorenzini M, Benfenati E, et al. Predictive carcinogenicity: a model for aromatic compounds, with nitrogen-containing substituents, based on molecular descriptors using an artificial neural network. J Chem Inf Comput Sci. 1999;39(6):1076–1080. doi: 10.1021/ci9903096
   PubMedGoogle Scholar
• Moorthy NSHN, Kumar S, Poongavanam V. Classification of carcinogenic and mutagenic properties using machine learning method. Comput Toxicol. 2017;3:33–43. doi: 10.1016/j.comtox.2017.07.002
   Google Scholar
• Benigni R. Alternatives to the carcinogenicity bioassay for toxicity prediction: are we there yet? Expert Opin Drug Metab Toxicol. 2012;8(4):407–417. doi: 10.1517/17425255.2012.666238
   PubMed Web of Science ®Google Scholar
• Bulitta Jürgen B, Hope WW, Eakin AE, et al. Generating robust and informative nonclinical in vitro and in vivo bacterial infection model efficacy data to support translation to humans. Antimicrob Agents Chemother. 2019;63(5): doi: 10.1128/AAC.02307-18
   Web of Science ®Google Scholar
• Chatterjee N, Alfaro-Moreno E. In vitro cell transformation assays: a valuable approach for carcinogenic potentiality assessment of nanomaterials. Int J Mol Sci. 2023;24(9):8219. doi: 10.3390/ijms24098219
   PubMed Web of Science ®Google Scholar
• Sun H, Xia M, Austin CP, et al. Paradigm shift in toxicity testing and modeling. Aaps J. 2012;14(3):473–480. doi: 10.1208/s12248-012-9358-1
   PubMed Web of Science ®Google Scholar
• Guyton KZ, Kyle AD, Aubrecht J, et al. Improving prediction of chemical carcinogenicity by considering multiple mechanisms and applying toxicogenomic approaches. Mutat Res/Rev Mutat Res. 2009;681(2–3):230–240. doi: 10.1016/j.mrrev.2008.10.001
   PubMed Web of Science ®Google Scholar
• Helma C. Lazy structure-activity relationships (lazar) for the prediction of rodent carcinogenicity and Salmonella mutagenicity. Mol Divers. 2006;10(2):147–158. doi: 10.1007/s11030-005-9001-5
   PubMed Web of Science ®Google Scholar
• Contrera JF, Kruhlak NL, Matthews EJ, et al. Comparison of MC4PC and MDL-QSAR rodent carcinogenicity predictions and the enhancement of predictive performance by combining QSAR models. Regul Toxicol Pharmacol. 2007;49(3):172–182. doi: 10.1016/j.yrtph.2007.07.001
   PubMed Web of Science ®Google Scholar
• Ruiz P, Faroon O, Moudgal CJ, et al. Prediction of the health effects of polychlorinated biphenyls (PCBs) and their metabolites using quantitative structure–activity relationship (QSAR). toxicology letters. Toxicol Lett. 2008;181(1):53–65. doi: 10.1016/j.toxlet.2008.06.870
   PubMed Web of Science ®Google Scholar
• Fjodorova N, Vračko M, Novič M, et al. New public QSAR model for carcinogenicity. Chem Cent J. 2010;4(S1):S3. doi: 10.1186/1752-153X-4-S1-S3
   PubMedGoogle Scholar
• Toropova AP, Toropov AA. CORAL software: prediction of carcinogenicity of drugs by means of the Monte Carlo method. Eur J Pharmaceut Sci. 2014;52:21–25. doi: 10.1016/j.ejps.2013.10.005
   PubMed Web of Science ®Google Scholar
• Wu X, Zhang Q, Wang H, et al. Predicting carcinogenicity of organic compounds based on CPDB. Chemosphere. 2015;139:81–90. doi: 10.1016/j.chemosphere.2015.05.056
   PubMed Web of Science ®Google Scholar
• Abou Hajal A, Al Meslamani AZ. Overcoming barriers to machine learning applications in toxicity prediction. Expert Opin Drug Metab Toxicol. 2023;1–5. doi: 10.1080/17425255.2023.2294939
   PubMed Web of Science ®Google Scholar
• Vatansever S, Schlessinger A, Wacker D, et al. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: state-of-the-arts and future directions. Med Res Rev. 2021;41(3):1427–1473. doi: 10.1002/med.21764
   PubMed Web of Science ®Google Scholar
• Vijayan RSK, Kihlberg J, Cross JB, et al. Enhancing preclinical drug discovery with artificial intelligence. Drug Discovery Today. 2022;27(4):967–984. doi: 10.1016/j.drudis.2021.11.023
   PubMed Web of Science ®Google Scholar
• Chen Z, Zhang L, Sun J, et al. DCAMCP: a deep learning model based on capsule network and attention mechanism for molecular carcinogenicity prediction. J Cellular Mol Medi. 2023;27(20):3117–3126. doi: 10.1111/jcmm.17889
   PubMed Web of Science ®Google Scholar
• Fradkin P, Young A, Atanackovic L, et al. A graph neural network approach for molecule carcinogenicity prediction. Bioinformatics. 2022;38(Supplement_1):i84–i91. doi: 10.1093/bioinformatics/btac266
   PubMed Web of Science ®Google Scholar
• Luan F, Zhang R, Zhao C, et al. Classification of the carcinogenicity of N-Nitroso compounds based on support vector machines and linear discriminant analysis. Chem Res Toxicol. 2005;18(2):198–203. doi: 10.1021/tx049782q
   PubMed Web of Science ®Google Scholar
• Tan NX, Rao HB, Li ZR, et al. Prediction of chemical carcinogenicity by machine learning approaches. SAR and QSAR in Environmental Research. SAR QSAR Environ Res. 2009;20(1–2):27–75. doi: 10.1080/10629360902724085
   PubMed Web of Science ®Google Scholar
• Tanabe K, Lučić B, Amić D, et al. Prediction of carcinogenicity for diverse chemicals based on substructure grouping and SVM modeling. Mol Divers. 2010;14(4):789–802. doi: 10.1007/s11030-010-9232-y
   PubMed Web of Science ®Google Scholar
• Fjodorova N, Vračko M, Tušar M, et al. Quantitative and qualitative models for carcinogenicity prediction for non-congeneric chemicals using CP ANN method for regulatory uses. Mol Divers. 2010;14(3):581–594. doi: 10.1007/s11030-009-9190-4
   PubMed Web of Science ®Google Scholar
• Tanabe K, Kurita T, Nishida K, et al. Improvement of carcinogenicity prediction performances based on sensitivity analysis in variable selection of SVM models. SAR QSAR Environ Res. 2013;24(7):565–580. doi: 10.1080/1062936X.2012.762425
   PubMed Web of Science ®Google Scholar
• Zhong M, Nie X, Yan A, et al. Carcinogenicity prediction of noncongeneric chemicals by a support vector machine. Chem Res Toxicol. 2013;26(5):741–749. doi: 10.1021/tx4000182
   PubMed Web of Science ®Google Scholar
• Singh KP, Gupta S, Rai P. Predicting carcinogenicity of diverse chemicals using probabilistic neural network modeling approaches. Toxicol Appl Pharmacol. 2013;272(2):465–475. doi: 10.1016/j.taap.2013.06.029
   PubMed Web of Science ®Google Scholar
• Li X, Du Z, Wang J, et al. In silico estimation of chemical carcinogenicity with binary and ternary classification methods. Mol Inform. 2015;34(4):228–235. doi: 10.1002/minf.201400127
   PubMed Web of Science ®Google Scholar
• Zhang H, Cao Z-X, Li M, et al. Novel naïve Bayes classification models for predicting the carcinogenicity of chemicals. Food Chem Toxicol. 2016;97:141–149. doi: 10.1016/j.fct.2016.09.005
   PubMed Web of Science ®Google Scholar
• Zhang L, Ai H, Chen W, et al. CarcinoPred-EL: Novel models for predicting the carcinogenicity of chemicals using molecular fingerprints and ensemble learning methods. Sci Rep. 2017;7(1):2118. doi: 10.1038/s41598-017-02365-0
   PubMed Web of Science ®Google Scholar
• Wang Y-W, Huang L, Jiang S-W, et al. CapsCarcino: a novel sparse data deep learning tool for predicting carcinogens. Food Chem Toxicol. 2020;135:110921. doi: 10.1016/j.fct.2019.110921
   PubMed Web of Science ®Google Scholar
• Limbu S, Dakshanamurthy S. Predicting chemical carcinogens using a hybrid neural network deep learning method. Sensors. 2022;22(21):8185. doi: 10.3390/s22218185
   PubMed Web of Science ®Google Scholar
• Hao N, Sun P, Zhao W, et al. Application of a developed triple-classification machine learning model for carcinogenic prediction of hazardous organic chemicals to the US, EU, and WHO based on Chinese database. Ecotoxicol Environ Saf. 2023;255:114806. doi: 10.1016/j.ecoenv.2023.114806
   PubMed Web of Science ®Google Scholar
• Zhang H, Yi H, Hao Y, et al. Deciphering exogenous chemical carcinogenicity through interpretable deep learning: a novel approach for evaluating atmospheric pollutant hazards. J Hazard Mater. 2024;465:133092. doi: 10.1016/j.jhazmat.2023.133092
   PubMed Web of Science ®Google Scholar
• Liu Z, Roberts RA, Lal-Nag M, et al. AI-based language models powering drug discovery and development. Drug Discovery Today. 2021;26(11):2593–2607. doi: 10.1016/j.drudis.2021.06.009
   PubMed Web of Science ®Google Scholar
• Waters MD. Introduction to predictive toxicogenomics for carcinogenicity. In: Thomas RS, and Waters MD, editors. Toxicogenomics in Predictive Carcinogenicity. United Kingdom: The Royal Society of Chemistry; 2016. p. 0.
   Google Scholar
• Singh AV, Chandrasekar V, Paudel N, et al. Integrative toxicogenomics: advancing precision medicine and toxicology through artificial intelligence and OMICs technology. Biomed Pharmacother. 2023;163:114784. doi: 10.1016/j.biopha.2023.114784
   PubMed Web of Science ®Google Scholar
• Qin C, Tanis KQ, Podtelezhnikov AA, et al. Toxicogenomics in drug development: a match made in heaven? Expert Opin Drug Metab Toxicol. 2016;12(8):847–849. doi: 10.1080/17425255.2016.1175437
   PubMed Web of Science ®Google Scholar
• Yang S, Kar S. Application of artificial intelligence and machine learning in early detection of adverse drug reactions (ADRs) and drug-induced toxicity. Artif Intell Che. 2023;1(2):100011. doi: 10.1016/j.aichem.2023.100011
   Google Scholar
• Vo TH, Nguyen NTK, Kha QH, et al. On the road to explainable AI in drug-drug interactions prediction: a systematic review. Computat Struct Biotechnol J. 2022;20:2112–2123. doi: 10.1016/j.csbj.2022.04.021
   PubMed Web of Science ®Google Scholar
• Vittoria Togo M, Mastrolorito F, Orfino A, et al. Where developmental toxicity meets explainable artificial intelligence: state-of-the-art and perspectives. Expert Opin Drug Metab Toxicol. 2023:1–17. doi: 10.1080/17425255.2023.2298827
   PubMed Web of Science ®Google Scholar
• Marzi SJ, Schilder BM, Nott A, et al. Artificial intelligence for neurodegenerative experimental models. Alzheimer’s Dementia. 2023;19(12):5970–5987. doi: 10.1002/alz.13479
   PubMed Web of Science ®Google Scholar
• Ahmed SF, Alam MSB, Hassan M, et al. Deep learning modelling techniques: current progress, applications, advantages, and challenges. Artif Intell Rev. 2023;56(11):13521–13617. doi: 10.1007/s10462-023-10466-8
   Web of Science ®Google Scholar
• Siala H, Wang Y. SHIFTing artificial intelligence to be responsible in healthcare: a systematic review. Soc Sci Med. 2022;296:114782. doi: 10.1016/j.socscimed.2022.114782
   PubMed Web of Science ®Google Scholar
• Jia Z, Chen J, Xu X, et al. The importance of resource awareness in artificial intelligence for healthcare. Nature Mach Intell. 2023;5(7):687–698. doi: 10.1038/s42256-023-00670-0
   Google Scholar
• Xu Y, Liu X, Cao X, et al. Artificial intelligence: a powerful paradigm for scientific research. Innovation. 2021;2(4):100179. doi: 10.1016/j.xinn.2021.100179
   Google Scholar
• Lu M, Yin J, Lin G, et al. Artificial intelligence in pharmaceutical sciences. Engineering. 2023;27:37–69. doi: 10.1016/j.eng.2023.01.014
   Web of Science ®Google Scholar
• Tran T-O, Vo TH, Le NQK. Omics-based deep learning approaches for lung cancer decision-making and therapeutics development. Brief Funct Genomics. 2023:elad031. doi: 10.1093/bfgp/elad031
   Web of Science ®Google Scholar