Venkatraman V (2021) FP-ADMET: a compendium of fingerprint-based ADMET prediction models. J Cheminformatics 13:1–12

Article  CAS  Google Scholar 

Simula H, Zhang H, Zhang L, Gao C, Yu R, Kang C (2021) Pharmacophore screening, molecular docking, ADMET prediction and MD simulations for identification of ALK and MEK potential dual inhibitors. J Mol Struct 1245:131066

Article  Google Scholar 

Hua Y, Dai X, Xu Y, Xing G, Liu H, Lu T, Chen Y, Zhang Y (2022) Drug repositioning: Progress and challenges in drug discovery for various diseases. Eur J Med Chem 234:114239

Article  CAS  PubMed  PubMed Central  Google Scholar 

Deore AB, Dhumane JR, Wagh R, Sonawane R (2019) The stages of drug discovery and development process. Asian J Pharmaceutical Res Develop 7(6):62–67

Article  CAS  Google Scholar 

Shaker B, Ahmad S, Lee J, Jung C, Na D (2021) In silico methods and tools for drug discovery. Comput Biol Med 137:104851

Article  PubMed  Google Scholar 

O. Silakari and P. Singh, 2021 “Admet tools: prediction and assessment of chemical admet properties of nces,” Concepts and experimental protocols of modelling and informatics in drug design; Elsevier: Amsterdam, The Netherlands.

Zhang W, Zou H, Luo L, Liu Q, Wu W, Xiao W (2016) Predicting potential side effects of drugs by recommender methods and ensemble learning. Neurocomputing 173:979–987

Article  Google Scholar 

Zhang W, Yue X, Liu F, Chen Y, Tu S, Zhang X (2017) A unified frame of predicting side effects of drugs by using linear neighborhood similarity. BMC Syst Biol 11:23–34

Article  Google Scholar 

Zhang W, Liu X, Chen Y, Wu W, Wang W, Li X (2018) Feature derived graph regularized matrix factorization for predicting drug side effects. Neurocomputing 287:154–162

Article  Google Scholar 

Schwaiblmair M, Behr W, Haeckel T, Märkl B, Foerg W, Berghaus T (2012) Drug induced interstitial lung disease. The Open Respiratory Med J 6:63

Article  Google Scholar 

Jiang C, Yang H, Di P, Li W, Tang Y, Liu G (2019) In silico prediction of chemical reproductive toxicity using machine learning. J Appl Toxicol 39(6):844–854

Article  CAS  PubMed  Google Scholar 

Casey W, Jacobs A, Maull E, Matheson J, Clarke C, Lowit A (2015) A new path forward: the interagency coordinating committee on the validation of alternative methods (iccvam) and national toxicology program’s interagency center for the evaluation of alternative toxicological methods (niceatm). J Am Assoc Lab Anim Sci 54(2):170–173

PubMed  PubMed Central  Google Scholar 

Tropsha A, Isayev O, Varnek A, Schneider G, Cherkasov A (2024) Integrating qsar modelling and deep learning in drug discovery: the emergence of deep qsar. Nat Rev Drug Discovery 23(2):141–155

Article  PubMed  Google Scholar 

Mastrolorito F, Togo MV, Gambacorta N, Trisciuzzi D, Giannuzzi V, Bonifazi F, Liantonio A, Imbrici P, De Luca A, Altomare CD et al (2024) Tisbe: a public web platform for the consensus-based explainable prediction of developmental toxicity. Chem Res Toxicol 37(2):323–339

Article  CAS  PubMed  Google Scholar 

Gunturi S, Ramamurthi N (2014) A novel approach to generate robust classification models to predict developmental toxicity from imbalanced datasets. SAR QSAR Environ Res 25(9):711–727

Article  CAS  PubMed  Google Scholar 

Gramatica P, Papa E, Sangion A (2018) Qsar modeling of cumulative environmental end-points for the prioritization of hazardous chemicals. Environ Sci Process Impacts 20(1):38–47

Article  CAS  PubMed  Google Scholar 

Fenner K, Tratnyek PG (2017) Qsars and computational chemistry methods in environmental chemical sciences. Environ Sci Process Impacts 19(3):185–187

Article  PubMed  Google Scholar 

Wang Z, Zhao P, Zhang X, Xu X, Li W, Liu G, Tang Y (2021) In silico prediction of chemical respiratory toxicity via machine learning. Computational Toxicol 18:100155

Article  CAS  Google Scholar 

Lei T, Chen F, Liu H, Sun H, Kang Y, Li D, Li Y, Hou T (2017) Admet evaluation in drug discovery. Part 17: development of quantitative and qualitative prediction models for chemical-induced respiratory toxicity. Mol Pharm 14(7):2407–2421

Article  CAS  PubMed  Google Scholar 

Zhang H, Ma J-X, Liu C-T, Ren J-X, Ding L (2018) Development and evaluation of in silico prediction model for drug-induced respiratory toxicity by using na¨ıve bayes classifier method. Food Chem Toxicol 121:593–603

Article  CAS  PubMed  Google Scholar 

Jaganathan K, Tayara H, Chong KT (2022) An explainable supervised machine learning model for predicting respiratory toxicity of chemicals using optimal molecular descriptors. Pharmaceutics 14(4):832

Article  PubMed  PubMed Central  Google Scholar 

Shehab E, Nayel H, Taha M (2024) Character N-gram model for toxicity prediction. IAES Int J Artif Intellig (IJ-AI) 13(4):4380–4387

Article  Google Scholar 

Weininger D (1988) Smiles, a chemical language and information system. 1. ntroduction to methodology and encoding rules. J Chem Inf Comput Sci 28(1):31–36

Article  CAS  Google Scholar 

S. B. ˇSegota, N. Aneli´c, I. Lorencin, J. Musulin, D. ˇStifani´c, and Z. Car, 2021 “Preparation of simplified molecular input line entry system notation datasets for use in convolutional neural networks,” in 2021 IEEE 21st International conference on bioinformatics and bioengineering (BIBE). https://doi.org/10.1109/BIBE52308.2021.9635320, pp. 1–6.

Erturan AM, Karaduman G, Durmaz H (2023) Machine learning based approach for efficient prediction of toxicity of chemical gases using feature selection. J Hazard Mater 455:131616

Article  CAS  PubMed  Google Scholar 

T. R. Documentation. [Online]. Available: https://www.rdkit.org/docs/ (accessed on 11 November 2024).

Elreedy D, Atiya AF, Kamalov F (2024) A theoretical distribution analysis of synthetic minority oversampling technique (smote) for imbalanced learning. Mach Learn 113(7):4903–4923

Article  Google Scholar 

Bae S-Y, Lee J, Jeong J, Lim C, Choi J (2021) Effective data balancing methods for class-imbalanced genotoxicity datasets using machine learning algorithms and molecular fingerprints. Computational Toxicol 20:100178

Article  CAS  Google Scholar 

Jeon H, Oh S (2020) Hybrid-recursive feature elimination for efficient feature selection. Appl Sci 10(9):3211

Article  CAS  Google Scholar 

T. Hasan, A. Matin, M. Kamruzzaman, S. Islam, and M. O. F.Goni, 2020 “A comparative analysis of feature extraction methods for human opinion grouping using several machine learning techniques,” in 2020 IEEE International women in engineering (WIE) conference on electrical and computer engineering (WIECON-ECE). pp. 272–275.

F. Firmansyah, W. B. Zulfikar, D. S. Maylawati, N. D. Arianti, L. Muliawaty, M. A. Septiadi, and M. A. Ramdhani, 2020 “Comparing sentiment analysis of Indonesian presidential election 2019 with support vector machine and k-nearest neighbor algorithm,” in 2020 6th International conference on computing engineering and design (ICCED). pp. 1–6.

Srinivas P, Katarya R (2022) hyoptxg: Optuna hyper-parameter optimization framework for predicting cardiovascular disease using xgboost. Biomed Signal Process Control 73:103456

Article  Google Scholar 

PNEUMOTOX. [Online]. Available: https://www.pneumotox.com/drug/index/ (accessed on 20 October 2024)

“Hazardous chemical information system.” [Online]. Available: http://hcis.safeworkaustralia.gov.au/ (accessed on 20 October 2024)

Cai M-C, Xu Q, Pan Y-J, Pan W, Ji N, Li Y-B, Jin H-J, Liu K, Ji Z-L (2015) Adrecs: an ontology database for aiding standardization and hierarchical classification of adverse drug reaction terms. Nucleic Acids Res 43(D1):D907–D913

Article  CAS  PubMed  Google Scholar 

Jarvis J, Seed M, Stocks S, Agius R (2015) A refined qsar model for prediction of chemical asthma hazard. Occup Med 65(8):659–666

Article  CAS  Google Scholar 

Dik S, Ezendam J, Cunningham AR, Carrasquer CA, van Loveren H, Rorije E (2014) Evaluation of in silico models for the identification of respiratory sensitizers. Toxicol Sci 142(2):385–394

Article  CAS  PubMed  Google Scholar 

Kuhn M, Letunic I, Jensen LJ, Bork P (2016) The sider database of drugs and side effects. Nucleic Acids Res 44(D1):D1075–D1079

Article  CAS  PubMed  Google Scholar 

Juan-Blanco T, Duran-Frigola M, Aloy P (2014) Intside: a web server for the chemical and biological examination of drug side effects. Bioinformatics 31(4):612–613

Article  PubMed  Google Scholar 

Alves VM, Capuzzi SJ, Braga RC, Borba JV, Silva AC, Luechtefeld T, Hartung T, Andrade CH, Muratov EN, Tropsha A (2018) A perspective and a new integrated computational strategy for skin sensitization assessment. ACS Sustainable Chem & Eng 6(3):2845–2859

Article  CAS  Google Scholar