Abstract
Toxicity in drug design is a very important step prior to human or animal evaluation phases. Establishing drug toxicity involves the modification or redesign of the drug into an analog to suppress or reduce the toxicity. In this work, two different deep neural networks architectures and a proposed model to classify drug toxicity were evaluated. Three datasets of molecular descriptors were build based on SMILES from the Tox21 database and the AhR protein to test the accuracy prediction of the models. All models were tested with different sets of hyperparameters. The proposed model showed higher accuracy and lower loss compared to the other architectures. The number of descriptors played a key roll in the accuracy of the proposed model along with the Adam optimizer.
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References
Atkinson Jr., A.J., Markey, S.P.: Biochemical mechanisms of drug toxicity. In: Principles of Clinical Pharmacology, pp. 249–271. Elsevier (2007)
Bania, R.K.: COVID-19 public tweets sentiment analysis using TF-IDF and inductive learning models. INFOCOMP J. Comput. Sci. 19(2), 23–41 (2020)
Bottou, L.: Large-scale machine learning with stochastic gradient descent. In: Lechevallier, Y., Saporta, G. (eds.) Proceedings of COMPSTAT 2010, pp. 177–186. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-7908-2604-3_16
Collins, F.S., Gray, G.M., Bucher, J.R.: Transforming environmental health protection. Science 319(5865), 906–907 (2008). https://doi.org/10.1126/science.1154619, https://science.sciencemag.org/content/319/5865/906
Dearden, J.C.: In silico prediction of drug toxicity. J. Comput. Aided Mol. Des. 17(2–4), 119–127 (2003). https://doi.org/10.1023/A:1025361621494
Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945). https://doi.org/10.2307/1932409, https://esajournals.onlinelibrary.wiley.com/doi/abs/10.2307/1932409
Karim, A., Mishra, A., Newton, M.H., Sattar, A.: Efficient toxicity prediction via simple features using shallow neural networks and decision trees. ACS Omega 4(1), 1874–1888 (2019)
Karim, A., Singh, J., Mishra, A., Dehzangi, A., Newton, M.A.H., Sattar, A.: Toxicity prediction by multimodal deep learning. In: Ohara, K., Bai, Q. (eds.) PKAW 2019. LNCS (LNAI), vol. 11669, pp. 142–152. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30639-7_12
Kavlock, R.J., Austin, C.P., Tice, R.R.: Toxicity testing in the 21st century: implications for human health risk assessment. Risk Anal. 29(4), 485–487 (2009). https://doi.org/10.1111/j.1539-6924.2008.01168.x, https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1539-6924.2008.01168.x
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2017)
Lemaître, G., Nogueira, F., Aridas, C.K.: Imbalanced-learn: a python toolbox to tackle the curse of imbalanced datasets in machine learning. J. Mach. Learn. Res. 18(17), 1–5 (2017)
Mayr, A., Klambauer, G., Unterthiner, T., Hochreiter, S.: DeepTox: toxicity prediction using deep learning. Front. Environ. Sci. 3, 80 (2016)
McKinney, W., et al.: Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference, vol. 445, pp. 51–56 (2010)
Muster, W., Breidenbach, A., Fischer, H., Kirchner, S., Müller, L., Pähler, A.: Computational toxicology in drug development. Drug Discovery Today 13(7–8), 303–310 (2008)
O’Boyle, N.M., Morley, C., Hutchison, G.R.: Pybel: a Python wrapper for the OpenBabel cheminformatics toolkit. Chem. Cent. J. 2(1), 1–7 (2008)
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Saldívar-González, F., Prieto-Martínez, F.D., Medina-Franco, J.L.: Descubrimiento y desarrollo de fármacos: un enfoque computacional. Educación química 28(1), 51–58 (2017)
Sid, K., Batouche, M.C.: Big data analytics techniques in virtual screening for drug discovery. In: Lazaar, M., Tabii, Y., Chrayah, M., Achhab, M.A. (eds.) Proceedings of the 2nd International Conference on Big Data, Cloud and Applications, BDCA 2017, Tetouan, Morocco, 29–30 March 2017, pp. 9:1–9:7. ACM (2017). https://doi.org/10.1145/3090354.3090363
Thomas, R.S., et al.: The US Federal Tox21 program: a strategic and operational plan for continued leadership. ALTEX - Altern. Anim. Exp. 35(2), 163–168 (2018)
Tice, R.R., Austin, C.P., Kavlock, R.J., Bucher, J.R.: Improving the human hazard characterization of chemicals: a Tox21 update. Environ. Health Perspect. 121(7), 756–765 (2013). https://doi.org/10.1289/ehp.1205784, https://ehp.niehs.nih.gov/doi/abs/10.1289/ehp.1205784
Verbist, B., et al.: Using transcriptomics to guide lead optimization in drug discovery projects: lessons learned from the QSTAR project. Drug Discovery Today 20(5), 505–513 (2015)
Wang, X., Song, K., Li, L., Chen, L.: Structure-based drug design strategies and challenges. Curr. Top. Med. Chem. 18(12), 998–1006 (2018)
Zhang, L., Tan, J., Han, D., Zhu, H.: From machine learning to deep learning: progress in machine intelligence for rational drug discovery. Drug Discovery Today 22(11), 1680–1685 (2017)
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Varela-Salinas, G., Camacho-Cruz, H.E., Saldivar, A.J., Martinez-Rodriguez, J.L., Rodriguez-Rodriguez, J., Garcia-Perez, C. (2021). A Binary Classification Model for Toxicity Prediction in Drug Design. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2021. Lecture Notes in Computer Science(), vol 12886. Springer, Cham. https://doi.org/10.1007/978-3-030-86271-8_13
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