Abstract
With the success of Convolutional Neural Networks (CNN) in computer vision domain, cheminformatics is slowly moving away from feature Engineering towards Network Engineering. New deep networks and approaches are being proposed to explore the chemical behavior and their properties. In this paper, we propose a deep learning approach using Convolutional Neural Network for predicting the crystallization propensity of an organic molecule. The work is inspired from Chemception and architecture is based on the Inception-Resnet v2 model. The proposed approach only requires a 2D molecular drawing to predict if the molecule has a good probability of forming crystals, without the need of any molecular descriptor, any advanced chemistry knowledge or any study of crystal growth mechanisms. We have evaluated our approach on the Cambridge Structural Database (CSD) and the ZINC datasets. Compared with the machine learning approach of generating molecular descriptors plus SVM classification, our proposed approach gives a better classification accuracy.
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References
Chen, J., Sarma, B., Evans, J.M.B., Myerson, A.S.: Cryst. Growth Des. 11, 887–895 (2011)
Modarressi, H., Dearden, J.C., Modarress, I.: QSPR correlation of melting point for drug compounds based on different sources of molecular descriptors. J. Chem. Inf. Model. 46, 930–936 (2006)
Le, T., Epa, V.C., Burden, F.R., Winkler, D.A.: Chem. Rev. 112, 2889–2919 (2012)
Mitchell, J.B.O.: Machine learning methods in chemoinformatics. WIREs Comput. Mol. Sci. 4, 468–481 (2014)
Bhat, A.U., Merchant, S.S., Bhagwat, S.S.: Prediction of melting points of organic compounds using extreme learning machines. Ind. Eng. Chem. Res. 47, 920–925 (2008)
Palmer, D.S., O’Boyle, N.M., Glen, R.C., Mitchell, J.B.O.: Random forest models to predict aqueous solubility. J. Chem. Inf. Model. 47, 150 (2007)
Varnek, A., Baskin, I.: Machine learning methods for property prediction in chemoinformatics: quo vadis? J. Chem. Inf. Model. 52, 1413–1437 (2012)
Erić, S., Kalinić, M., Popović, A., Zloh, M., Kuzmanovski, I.: Prediction of aqueous solubility of drug-like molecules using a novel algorithm for automatic adjustment of relative importance of descriptors implemented in counter-propagation artificial neural networks. Int. J. Pharm. 437, 232–241 (2012)
Gawehn, E., Hiss, J.A., Schneider, G.: Deep learning in drug discovery. Mol. Inform. 35(1), 3–14 (2016)
Goh, G.B., Hodas, N.O., Vishnu, A.: Deep learning for computational chemistry. J. Comput. Chem. 38(16), 1291–1307 (2017)
Fooshee, D., Mood, A., Gutman, E., Tavakoli, M., Urban, G., Liu, F., Huynh, N., Van Vranken, D., Baldi, P.: Deep learning for chemical reaction prediction. Mol. Syst. Des. Eng. 3, 442–452 (2018)
Mayr, A., Klambauer, G., Unterthiner, T., Hochreiter, S. DeepTox: toxicity prediction using deep learning. Front. Environ. Sci. 3(80) (2015). https://doi.org/10.3389/fenvs.2015.00080
Wicker, J.G.P., Cooper, R.I.: Will it crystallise? Predicting crystallinity of molecular materials. CrystEngComm 17, 1927–1934 (2015)
Landrum, G.: RDKit: Open-source cheminformatics. http://www.rdkit.org/
Suykens, J.A.K., Vanderwalle, J.: Least squares support vector machines. Neural Process. Lett. 9, 293–300 (1999)
Szegedy, C., Ioffe, S., Vanhoucke, V.: Inception-v4, inception-resnet and the impact of residual connections on learning. arXiv:1602.07261 (2016). Inception Resnet v2
Goh, G.B., Siegel, C., Vishnu, A., Hodas, N.O., Baker, N.: Chemception: a deep neural network with minimal chemistry knowledge matches the performance of expert-developed QSAR/QSPR models. arXiv preprint arXiv:1706.06689 (2017)
Allen, F.H.: Acta Crystallogr. B 58, 380–388 (2002)
Irwin, J.J., Shoichet, B.K.: J. Chem. Inf. Model. 45, 177–182 (2005)
Weininger, D.: SMILES, a chemical language and information-system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Comp. Sci. 28(1), 31–36 (1988)
O’Boyle, N.M., Banck, M., James, C.A., Morley, C., Vandermeersch, T., Hutchison, G.R.: J. Cheminf. 3, 33 (2011)
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, AISTATS2010, Proceedings of Machine Learning-Research, Chia Laguna Resort, Sardinia, Italy, May 2010, vol. 9, pp. 249–256 (2010)
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Sharma, A., Khungar, B. (2019). A Deep Learning Approach for Molecular Crystallinity Prediction. In: Abraham, A., Gandhi, N., Pant, M. (eds) Innovations in Bio-Inspired Computing and Applications. IBICA 2018. Advances in Intelligent Systems and Computing, vol 939. Springer, Cham. https://doi.org/10.1007/978-3-030-16681-6_22
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DOI: https://doi.org/10.1007/978-3-030-16681-6_22
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