Alves VM, Golbraikh A, Capuzzi SJ et al. (2018). Multi-descriptor read across (MuDRA): A simple and transparent approach for developing accurate quantitative structure-activity relationship models, Journal of Chemical Information and Modeling, 58, 1214-1223.

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

Bahdanau D, Cho K, and Bengio Y (2014). Neural machine translation by jointly learning to align and translate,

Bemis GW and Murcko MA (1999). Properties of known drugs. 2. side chains, Journal of Medicinal Chemistry, 42, 5095-5099.

Coley CW, Barzilay R, Green WH, Jaakkola TS, and Jensen KF (2017). Convolutional embedding of attributed molecular graphs for physical property prediction, Journal of Chemical Information and Modeling, 57, 1757-1772.

Durant JL, Leland BA, Henry DR, and Nourse JG (2002). Reoptimization of MDL keys for use in drug discovery, Journal of Chemical Information and Computer Sciences, 42, 1273-1280.

Duvenaud DK, Maclaurin D, Iparraguirre J, Bombarell R, Hirzel T, Aspuru-Guzik A, and Adams RP (2015). Convolutional networks on graphs for learning molecular fingerprints, Advances in Neural Information Processing Systems, 28, 2224-2232.

Gilmer J, Schoenholz SS, Riley PF, Vinyals O, and Dahl GE (2017). Neural message passing for quantum chemistry, International Conference on Machine Learning, 70, 1263-1272. PMLR.

Hamilton W, Ying Z, and Leskovec J (2017). Inductive representation learning on large graphs, Advances in Neural Information Processing Systems, 30, 1024-1034.

Kipf TN and Welling M (2016). Semi-supervised classification with graph convolutional networks,

OECD (1994). OECD Guidelines for the Testing of Chemicals. OECD,

OECD (2002). Test No. 423: Acute Oral toxicity - Acute Toxic Class Method, OECD Guidelines for the Testing of Chemicals, Section 4, OECD Publishing, Paris,

OECD (2016a). Test No. 422: Combined Repeated Dose Toxicity Study with the Reproduction/Developmental Toxicity Screening Test, OECD Guidelines for the Testing of Chemicals, Section 4, OECD Publishing, Paris, https://doi.org/10.1787/9789264264403-en

OECD (2016b). Test No. 473: In Vitro Mammalian Chromosomal Aberration Test, OECD Guidelines for the Testing of Chemicals, Section 4, OECD Publishing, Paris,

OECD (2016c). Test No. 474: Mammalian Erythrocyte Micronucleus Test, OECD Guidelines for the Testing of Chemicals, Section 4, OECD Publishing, Paris,

OECD (2018). Test No. 414: Prenatal Developmental Toxicity Study, OECD Guidelines for the Testing of Chemicals, Section 4, OECD Publishing, Paris,

OECD (2020). Test No. 471: Bacterial Reverse Mutation Test, OECD Guidelines for the Testing of Chemicals, Section 4, OECD Publishing, Paris,

Landrum G (2012). Fingerprints in the RDKit,

Landrum G (2019). Rdkit documentation,

Luechtefeld T, Marsh D, Rowlands C, and Hartung T (2018). Machine learning of toxicological big data enables read-across structure activity relationships (RASAR) outperforming animal test reproducibility, Toxicological Sciences, 165, 198-212.

Mayr A, Klambauer G, Unterthiner T, and Hochreiter S (2016). DeepTox: Toxicity prediction using deep learning, Frontiers in Environmental Science, 3, 80.

National Research Council (2007). Toxicity Testing in the 21st Century: A Vision and a Strategy, National Academies Press, Washington D.C.

Rogers D and Hahn M (2010). Extended-connectivity fingerprints, Journal of Chemical Information and Modeling, 50, 742-754.

Rong Y, Bian Y, Xu T, Xie W, Wei Y, Huang W, and Huang J (2020). Self-supervised graph transformer on large-scale molecular data, Advances in Neural Information Processing Systems, 33, 12559-12571.

Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, and Monfardini G (2008). The graph neural network model, IEEE Transactions on Neural Networks, 20, 61-80.

Schütt K, Kindermans PJ, Sauceda Felix HE, Chmiela S, Tkatchenko A, and Müller KR (2017). Schnet: A continuous-filter convolutional neural network for modeling quantum interactions, Advances in Neural Information Processing Systems, 30.

Silva AC, Borba JV, Alves VM et al. (2021). Novel computational models offer alternatives to animal testing for assessing eye irritation and corrosion potential of chemicals, Artificial Intelligence in the Life Sciences, 1, 100028.

Vaswani A, Shazeer N, Parmar N et al. (2017). Attention is all you need, Advances in Neural Information Processing Systems, 30, 5998-6008.

Wu Z, Ramsundar B, Feinberg EN, Gomes J, Geniesse C, Pappu AS, Leswingd K, and Pande V (2018). MoleculeNet: A benchmark for molecular machine learning, Chemical Science, 9, 513-530.

Xu K, HuW, Leskovec J, and Jegelka S (2018). How powerful are graph neural networks?,

Zhang Z, Liu Q, Wang H, Lu C, and Lee CK (2021). Motif-based graph self-supervised learning for molecular property prediction, Advances in Neural Information Processing Systems, 34, 15870-15882.