References
- Yang H, Sun L, Li W, et al. In silico prediction of chemical toxicity for drug design using machine learning methods and structural alerts. Front Chem. 2018;6:30. doi: 10.3389/fchem.2018.00030
- Al Meslamani AZ, Jarab AS, Ghattas MA. The role of machine learning in healthcare responses to pandemics: maximizing benefits and filling gaps. J Med Econ. 2023 Dec;26(1):777–780. doi: 10.1080/13696998.2023.2224018
- 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
- Li T, Tong W, Roberts R, et al. DeepCarc: deep learning-powered carcinogenicity prediction using model-level representation. Front Artif Intell. 2021;4:757780. doi: 10.3389/frai.2021.757780
- Ryu JY, Jang WD, Jang J, et al. PredAOT: a computational framework for prediction of acute oral toxicity based on multiple random forest models. BMC Bioinf. 2023;24(1):66. doi:10.1186/s12859-023-05176-5
- Grafton F, Ho J, Ranjbarvaziri S, et al. Deep learning detects cardiotoxicity in a high-content screen with induced pluripotent stem cell-derived cardiomyocytes. Elife. 2021 Aug;10:e68714.
- Kelleci Çelik F, Karaduman G. Machine learning-based prediction of drug-induced hepatotoxicity: an OvA-QSTR Approach. J Chem Inf Model. 2023 Aug;63(15):4602–4614. doi: 10.1021/acs.jcim.3c00687
- Jaganathan K, Tayara H, Chong KT. An explainable supervised machine learning model for predicting respiratory toxicity of chemicals using optimal molecular descriptors. Pharmaceutics. 2022 Apr;14(4). doi: 10.3390/pharmaceutics14040832
- Silva AC, Borba JVVB, Alves VM, et al. Novel computational models offer alternatives to animal testing for assessing eye irritation and corrosion potential of chemicals. Artif Intell Life Sci. 2021;1:100028. doi: 10.1016/j.ailsci.2021.100028
- Collins SP, Barton-Maclaren TS. Novel machine learning models to predict endocrine disruption activity for high-throughput chemical screening. Front Toxicol. 2022;4:981928. doi:10.3389/ftox.2022.981928
- Lysenko A, Sharma A, Boroevich KA, et al. An integrative machine learning approach for prediction of toxicity-related drug safety. Life Sci Alliance. 2018;1(6):e201800098. doi: 10.26508/lsa.201800098
- Mayr A, Klambauer G, Unterthiner T, et al. DeepTox: Toxicity prediction using deep learning. Front Environ Sci. 2016 Feb;3(FEB). doi: 10.3389/fenvs.2015.00080
- Pang YY, Yeo WK, Loh KY, et al. Structure–toxicity relationship and structure–activity relationship study of 2-phenylaminophenylacetic acid derived compounds. Food Chem Toxicol. 2014 Sep;71:207–216. doi: 10.1016/J.FCT.2014.06.013
- Wen M, Zhang Z, Niu S, et al. Deep-learning-based drug–target interaction prediction. J Proteome Res. 2017 Apr;16(4):1401–1409.
- Van Tran TT, Surya Wibowo A, Tayara H, et al. Artificial intelligence in drug toxicity prediction: recent advances, challenges, and future perspectives. J Chem Inf Model. 2023 May;63(9):2628–2643. doi: 10.1021/acs.jcim.3c00200
- 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 Dec;1(2):100011. doi: 10.1016/j.aichem.2023.100011
- Cavasotto CN, Scardino V. Machine learning toxicity prediction: latest advances by toxicity end point. ACS Omega. 2022 Dec;7(51):47536–47546. doi: 10.1021/acsomega.2c05693
- Wu Y, Wang G. Machine learning based toxicity prediction: from chemical structural description to transcriptome analysis. Int J Mol Sci. 2018 Aug;19(8). doi: 10.3390/ijms19082358
- Sharma B, Chenthamarakshan V, Dhurandhar A, et al. Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations. Sci Rep. 2023 Dec;13(1). doi: 10.1038/s41598-023-31169-8
- Wenzel J, Matter H, Schmidt F. Predictive multitask deep neural network models for ADME-Tox properties: learning from large data sets. J Chem Inf Model. 2019 Mar;59(3):1253–1268. doi: 10.1021/acs.jcim.8b00785
- Erickson N, Mueller J, Shirkov A, et al. AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data. 2020 Mar. Available from: http://arxiv.org/abs/2003.06505
- Singh AV, Bansod G, Mahajan M, et al. Digital Transformation in Toxicology: improving communication and efficiency in risk assessment. ACS Omega. 2023 Jun 20; 8(24):21377–21390. American Chemical Society. doi: 10.1021/acsomega.3c00596
- Ying Z, Bourgeois D, You J. M. Z.-A. In neural, and undefined 2019, “Gnnexplainer: generating explanations for graph neural networks. Advances in neural information processing systems, 2019. proceedings.neurips.cc. [cited 2023 Oct. 16] Available from: https://proceedings.neurips.cc/paper_files/paper/2019/hash/d80b7040b773199015de6d3b4293c8ff-Abstract.html
- Ribeiro MT, Singh S, Guestrin C. ‘Why should i trust you?’ explaining the predictions of any classifier. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery; 2016 Aug. p. 1135–1144. doi: 10.1145/2939672.2939778