Adams J (1993) Structure-activity and dose-response relationships in the neural and behavioral teratogenesis of retinoids. Neurotoxicol Teratol 15:193–202. https://doi.org/10.1016/0892-0362(93)90015-G

Article  CAS  PubMed  Google Scholar 

Altman NS (1992) An introduction to kernel and nearest-neighbor nonparametric regression. Am Stat 46:175–185. https://doi.org/10.2307/2685209

Article  Google Scholar 

Aouichaoui ARN, Fan F, Mansouri SS, Abildskov J, Sin G (2023) Combining group-contribution concept and graph neural networks toward interpretable molecular property models. J Chem Inf Model 63:725–744. https://doi.org/10.1021/acs.jcim.2c01091

Article  CAS  PubMed  Google Scholar 

Ballester PJ, Mitchell JBO (2010) A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking. Bioinformatics 26:1169–1175. https://doi.org/10.1093/bioinformatics/btq112

Article  CAS  PubMed  Google Scholar 

Basant N, Gupta S, Singh KP (2016) QSAR modeling for predicting reproductive toxicity of chemicals in rats for regulatory purposes. Toxicol Res 5:1029–1038. https://doi.org/10.1039/c6tx00083e

Article  CAS  Google Scholar 

Beekhuijzen M (2017) The era of 3Rs implementation in developmental and reproductive toxicity (DART) testing: current overview and future perspectives. Reprod Toxicol 72:86–96. https://doi.org/10.1016/j.reprotox.2017.05.006

Article  CAS  PubMed  Google Scholar 

Begum TF, Carpenter D (2022) Health effects associated with phthalate activity on nuclear receptors. Rev Environ Health 37:567–583. https://doi.org/10.1515/reveh-2020-0162

Article  CAS  PubMed  Google Scholar 

Bon M, Bilsland A, Bower J, McAulay K (2022) Fragment-based drug discovery—the importance of high-quality molecule libraries. Mol Oncol 16:3761–3777. https://doi.org/10.1002/1878-0261.13277

Article  CAS  PubMed  PubMed Central  Google Scholar 

Carbery A, Skyner R, von Delft F, Deane CM (2022) Fragment libraries designed to be functionally diverse recover protein binding information more efficiently than standard structurally diverse libraries. J Med Chem 65:11404–11413. https://doi.org/10.1021/acs.jmedchem.2c01004

Article  CAS  PubMed  PubMed Central  Google Scholar 

Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M, Dearden J, Gramatica P, Martin YC, Todeschini R, Consonni V, Kuz’min VE, Cramer R, Benigni R, Yang C, Rathman J, Terfloth L, Gasteiger J, Richard A, Tropsha A (2014) QSAR modeling: Where have you been? Where are you going to? J Med Chem 57:4977–5010. https://doi.org/10.1021/jm4004285

Article  CAS  PubMed  PubMed Central  Google Scholar 

Cheung J, Rudolph MJ, Burshteyn F, Cassidy MS, Gary EN, Love J, Franklin MC, Height JJ (2012) Structures of human acetylcholinesterase in complex with pharmacologically important ligands. J Med Chem 55:10282–10286. https://doi.org/10.1021/jm300871x

Article  CAS  PubMed  Google Scholar 

Choudhary K, DeCost B, Chen C, Jain A, Tavazza F, Cohn R, Park CW, Choudhary A, Agrawal A, Billinge SJL, Holm E, Ong SP, Wolverton C (2022) Recent advances and applications of deep learning methods in materials science. NPJ Comput Mater 8:59. https://doi.org/10.1038/s41524-022-00734-6

Article  Google Scholar 

Degen J, Wegscheid-Gerlach C, Zaliani A, Rarey M (2008) On the art of compiling and using’drug-like’chemical fragment spaces. ChemMedChem 3:1503. https://doi.org/10.1002/cmdc.200800178

Article  CAS  PubMed  Google Scholar 

Diao Y, Hu F, Shen Z, Li H (2023) MacFrag: segmenting large-scale molecules to obtain diverse fragments with high qualities. Bioinformatics. https://doi.org/10.1093/bioinformatics/btad012

Article  PubMed  PubMed Central  Google Scholar 

Durant JL, Leland BA, Henry DR, Nourse JG (2002) Reoptimization of MDL keys for use in drug discovery. J Chem Inf Comput Sci 42:1273–1280. https://doi.org/10.1021/ci010132r

Article  CAS  PubMed  Google Scholar 

ECHA (2023) European Chemicals Agency. https://echa.europa.eu/. Accessed 8 May 2024.

ElMazoudy RH, Attia AA (2012) Endocrine-disrupting and cytotoxic potential of anticholinesterase insecticide, diazinon in reproductive toxicity of male mice. J Hazard Mater 209–210:111–120. https://doi.org/10.1016/j.jhazmat.2011.12.073

Article  CAS  PubMed  Google Scholar 

Fang X, Liu L, Lei J, He D, Zhang S, Zhou J, Wang F, Wu H, Wang H (2022) Geometry-enhanced molecular representation learning for property prediction. Nat Mach Intell 4:127–134. https://doi.org/10.1038/s42256-021-00438-4

Article  Google Scholar 

Feldman H, Gauthier S, Hecker J, Vellas B, Subbiah P, Whalen E, Group* tDMSI (2001) A 24-week, randomized, double-blind study of donepezil in moderate to severe Alzheimer’s disease. Neurology 57:613–620. https://doi.org/10.1212/wnl.57.4.613

Article  CAS  PubMed  Google Scholar 

Feng H, Zhang L, Li S, Liu L, Yang T, Yang P, Zhao J, Arkin IT, Liu H (2021) Predicting the reproductive toxicity of chemicals using ensemble learning methods and molecular fingerprints. Toxicol Lett 340:4–14. https://doi.org/10.1016/j.toxlet.2021.01.002

Article  CAS  PubMed  Google Scholar 

Ghorbanzadeh M, Zhang J, Andersson PL (2016) Binary classification model to predict developmental toxicity of industrial chemicals in zebrafish. J Chemom 30:298–307. https://doi.org/10.1002/cem.2791

Article  CAS  Google Scholar 

GHS (2023) Globally Harmonized System of Classification and Labelling of Chemicals. https://unece.org/transport/dangerous-goods/ghs-rev10-2023. Accessed 8 May 2024.

Giacomini AC, Bueno BW, Marcon L, Scolari N, Genario R, Demin KA, Kolesnikova TO, Kalueff AV, de Abreu MS (2020) An acetylcholinesterase inhibitor, donepezil, increases anxiety and cortisol levels in adult zebrafish. J Psychopharmacol 34:1449–1456. https://doi.org/10.1177/0269881120944155

Article  CAS  PubMed  Google Scholar 

Halgren TA (1996) Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94. J Comput Chem 17:490–519. https://doi.org/10.1002/(SICI)1096-987X(199604)17:5/6%3c490::AID-JCC1%3e3.0.CO;2-P

Article  CAS  Google Scholar 

He J-H, Gao J-M, Huang C-J, Li C-Q (2014) Zebrafish models for assessing developmental and reproductive toxicity. Neurotoxicol Teratol 42:35–42. https://doi.org/10.1016/j.ntt.2014.01.006

Article  CAS  PubMed  Google Scholar 

Hemmerich J, Ecker GF (2020) In silico toxicology: from structure–activity relationships towards deep learning and adverse outcome pathways. Wires Comput Mol Sci 10:e1475. https://doi.org/10.1002/wcms.1475

Article  CAS  Google Scholar 

Hornberg JJ, Laursen M, Brenden N, Persson M, Thougaard AV, Toft DB, Mow T (2014) Exploratory toxicology as an integrated part of drug discovery. Part I: Why and how. Drug Discovery Today 19:1131–1136. https://doi.org/10.1016/j.drudis.2013.12.008

Article  CAS  PubMed  Google Scholar 

Hukkerikar AS, Kalakul S, Sarup B, Young DM, Sin G, Gani R (2012) Estimation of environment-related properties of chemicals for design of sustainable processes: development of group-contribution+ (GC+) property models and uncertainty analysis. J Chem Inf Model 52:2823–2839. https://doi.org/10.1021/ci300350r

Article  CAS  PubMed  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 39:844–854. https://doi.org/10.1002/jat.3772

Article  CAS  PubMed  Google Scholar 

Jiang D, Wu Z, Hsieh C-Y, Chen G, Liao B, Wang Z, Shen C, Cao D, Wu J, Hou T (2021) Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models. J Cheminform 13:1–23. https://doi.org/10.1186/s13321-020-00479-8

Article  CAS  Google Scholar