Login Register Cart 0
Generic placeholder image

Current Medicinal Chemistry

Editor-in-Chief

ISSN (Print): 0929-8673
ISSN (Online): 1875-533X

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Graph Neural Networks with Multi-features for Predicting Cocrystals using APIs and Coformers Interactions

Author(s): Medard Edmund Mswahili, Kyuri Jo, SeungDong Lee and Young-Seob Jeong*

Volume 31, Issue 36, 2024

Published on: 22 May, 2024

Page: [5953 - 5968] Pages: 16

DOI: 10.2174/0109298673290511240404053224

Price: $65

Abstract

Introduction: Active pharmaceutical ingredients (APIs) have gained direct pharmaceutical interest, along with their in vitro properties, and thus utilized as auxiliary solid dosage forms upon FDA guidance and approval on pharmaceutical cocrystals when reacting with coformers, as a potential and attractive route for drug substance development.

Methods: However, screening and selecting suitable and appropriate coformers that may potentially react with APIs to successfully form cocrystals is a time-consuming, inefficient, economically expensive, and labour-intensive task. In this study, we implemented GNNs to predict the formation of cocrystals using our introduced API-coformers relational graph data. We further compared our work with previous studies that implemented descriptor-based models (e.g., random forest, support vector machine, extreme gradient boosting, and artificial neural networks).

Results: All built graph-based models show compelling performance accuracies (i.e., 91.36, 94.60 and 95. 95% for GCN, GraphSAGE, and RGCN respectively). RGCN demonstrated effectiveness and prevailed among the built graph-based models due to its capability to capture intricate and learn nuanced relationships between entities such as non-ionic and non-covalent interactions or link information between APIs and coformers which are crucial for accurate predictions and representations.

Conclusion: These capabilities allows the model to adeptly learn the topological structure inherent in the graph data.

« Previous Next »
[1]
Regulatory classification of pharmaceutical co-crystals guidance for industry. 2018. Available from: https://www.fda.gov/files/drugs/published/Regulatory-Classification-of-Pharmaceutical-Co-Crystals.pdf
[2]
Aitipamula, S. Polymorphs, salts, and cocrystals: What’s in a name? Cryst. Growth Des., 2012, 12(5), 2147-2152.
[3]
Mswahili, M.E.; Lee, M-J.; Martin, G.L.; Kim, J.; Kim, P.; Choi, G.J.; Jeong, Y-S. Cocrystal prediction using machine learning models and descriptors. Appl. Sci., 2021, 11(3), 1323.
[http://dx.doi.org/10.3390/app11031323]
[4]
Berry, D.J.; Steed, J.W. Pharmaceutical cocrystals, salts and multicomponent systems; intermolecular interactions and property based design. Adv. Drug Deliv. Rev., 2017, 117, 3-24.
[http://dx.doi.org/10.1016/j.addr.2017.03.003] [PMID: 28344021]
[5]
Douroumis, D.; Ross, S.A.; Nokhodchi, A. Advanced methodologies for cocrystal synthesis. Adv. Drug Deliv. Rev., 2017, 117, 178-195.
[http://dx.doi.org/10.1016/j.addr.2017.07.008] [PMID: 28712924]
[6]
Sokal, A.; Pindelska, E. Pharmaceutical cocrystals as an opportunity to modify drug properties: From the idea to application: A review. Curr. Pharm. Des., 2018, 24(13), 1357-1365.
[http://dx.doi.org/10.2174/1381612824666171226130828] [PMID: 29278209]
[7]
Bolla, G.; Nangia, A. Pharmaceutical cocrystals: Walking the talk. Chem. Commun., 2016, 52(54), 8342-8360.
[http://dx.doi.org/10.1039/C6CC02943D] [PMID: 27278109]
[8]
Duggirala, N.K.; Perry, M.L.; Almarsson, Ö.; Zaworotko, M.J. Pharmaceutical cocrystals: Along the path to improved medicines. Chem. Commun., 2016, 52(4), 640-655.
[http://dx.doi.org/10.1039/C5CC08216A] [PMID: 26565650]
[9]
Karagianni, A.; Malamatari, M.; Kachrimanis, K. Pharmaceutical cocrystals: New solid phase modification approaches for the formulation of apis. Pharmaceutics, 2018, 10(1), 18.
[http://dx.doi.org/10.3390/pharmaceutics10010018] [PMID: 29370068]
[10]
Wood, P.A.; Feeder, N.; Furlow, M.; Galek, P.T.A.; Groom, C.R.; Pidcock, E. Knowledge-based approaches to co-crystal design. CrystEngComm, 2014, 16(26), 5839-5848.
[http://dx.doi.org/10.1039/c4ce00316k]
[11]
Wicker, J.G.P.; Crowley, L.M.; Robshaw, O.; Little, E.J.; Stokes, S.P.; Cooper, R.I.; Lawrence, S.E. Will they co-crystallize? CrystEngComm, 2017, 19(36), 5336-5340.
[http://dx.doi.org/10.1039/C7CE00587C]
[12]
Xiao, F.; Cheng, Y.; Wang, J.R.; Wang, D.; Zhang, Y.; Chen, K.; Mei, X.; Luo, X. Cocrystal prediction of bexarotene by graph convolution network and bioavailability improvement. Pharmaceutics, 2022, 14(10), 2198.
[http://dx.doi.org/10.3390/pharmaceutics14102198] [PMID: 36297633]
[13]
Desiraju, G.R. Supramolecular synthons in crystal engineering-A new organic synthesis. Angew. Chem. Int. Ed. Engl., 1995, 34(21), 2311-2327.
[http://dx.doi.org/10.1002/anie.199523111]
[14]
Almarsson, O.; Zaworotko, M.J. Crystal engineering of the composition of pharmaceutical phases. Do pharmaceutical co-crystals represent a new path to improved medicines? Chem. Commun., 2004, (17), 1889-1896.
[http://dx.doi.org/10.1039/b402150a] [PMID: 15340589]
[15]
Aakeröy, C.B.; Salmon, D.J. Building co-crystals with molecular sense and supramolecular sensibility. CrystEngComm, 2005, 7(72), 439-448.
[http://dx.doi.org/10.1039/b505883j]
[16]
Groom, C.R.; Bruno, I.J.; Lightfoot, M.P.; Ward, S.C. The cambridge structural database. Acta Crystallogr. B Struct. Sci. Cryst. Eng. Mater., 2016, 72(Pt 2), 171-179.
[http://dx.doi.org/10.1107/S2052520616003954] [PMID: 27048719]
[17]
Taylor, R.; Wood, P.A. A million crystal structures: The whole is greater than the sum of its parts. Chem. Rev., 2019, 119(16), 9427-9477.
[http://dx.doi.org/10.1021/acs.chemrev.9b00155] [PMID: 31244003]
[18]
Galek, P.T.A.; Allen, F.H.; Fábián, L.; Feeder, N. Knowledge-based H-bond prediction to aid experimental polymorph screening. CrystEngComm, 2009, 11(12), 2634-2639.
[http://dx.doi.org/10.1039/b910882c]
[19]
Delori, A.; Galek, P.T.A.; Pidcock, E.; Patni, M.; Jones, W. Knowledge-based hydrogen bond prediction and the synthesis of salts and cocrystals of the anti-malarial drug pyrimethamine with various drug and GRAS molecules. CrystEngComm, 2013, 15(15), 2916-2928.
[http://dx.doi.org/10.1039/c3ce26765b]
[20]
Kumar, A.; Nanda, A. In-silico methods of cocrystal screening: A review on tools for rational design of pharmaceutical cocrystals. J. Drug Deliv. Sci. Technol., 2021, 63, 102527.
[http://dx.doi.org/10.1016/j.jddst.2021.102527]
[21]
Fábián, L. Cambridge structural database analysis of molecular complementarity in cocrystals. Cryst. Growth Des., 2009, 9(3), 1436-1443.
[http://dx.doi.org/10.1021/cg800861m]
[22]
Devogelaer, J.J.; Meekes, H.; Vlieg, E.; de Gelder, R. Cocrystals in the cambridge structural database: A network approach. Acta Crystallogr. B Struct. Sci. Cryst. Eng. Mater., 2019, 75(Pt 3), 371-383.
[http://dx.doi.org/10.1107/S2052520619004694] [PMID: 32830659]
[23]
Devogelaer, J.J.; Brugman, S.J.T.; Meekes, H.; Tinnemans, P.; Vlieg, E.; de Gelder, R. Cocrystal design by network-based link prediction. CrystEngComm, 2019, 21(44), 6875-6885.
[http://dx.doi.org/10.1039/C9CE01110B]
[24]
Devogelaer, J.J.; Charpentier, M.D.; Tijink, A.; Dupray, V.; Coquerel, G.; Johnston, K.; Meekes, H.; Tinnemans, P.; Vlieg, E.; Ter Horst, J.H.; de Gelder, R. Cocrystals of praziquantel: Discovery by network-based link prediction. Cryst. Growth Des., 2021, 21(6), 3428-3437.
[http://dx.doi.org/10.1021/acs.cgd.1c00211] [PMID: 34276256]
[25]
Cabeza, C.A.J. Acid–base crystalline complexes and the pKa rule. CrystEngComm, 2012, 14(20), 6362-6365.
[http://dx.doi.org/10.1039/c2ce26055g]
[26]
Lemmerer, A.; Govindraju, S.; Johnston, M.; Motloung, X.; Savig, K.L. Co-crystals and molecular salts of carboxylic acid/pyridine complexes: Can calculated p Ka 's predict proton transfer? A case study of nine complexes. CrystEngComm, 2015, 17(19), 3591-3595.
[http://dx.doi.org/10.1039/C5CE00102A]
[27]
Taylor, C.R.; Day, G.M. Evaluating the energetic driving force for cocrystal formation. Cryst. Growth & Desig., 2018, 18, 892-904.
[28]
Cruz-Cabeza, A.J.; Day, G.M.; Jones, W. Towards prediction of stoichiometry in crystalline multicomponent complexes. Chemistry, 2008, 14(29), 8830-8836.
[http://dx.doi.org/10.1002/chem.200800668] [PMID: 18752227]
[29]
Issa, N.; Karamertzanis, P.G.; Welch, G.W.A.; Price, S.L. Can the formation of pharmaceutical cocrystals be computation- ally predicted? i. comparison of lattice energies. Cryst. Growth Des., 2009, 9(1), 442-453.
[http://dx.doi.org/10.1021/cg800685z]
[30]
Karamertzanis, P.G.; Kazantsev, A.V.; Issa, N.; Welch, G.W.A.; Adjiman, C.S.; Pantelides, C.C.; Price, S.L. Can the formation of pharmaceutical cocrystals be computationally predicted? 2. crystal structure prediction. J. Chem. Theory Comput., 2009, 5(5), 1432-1448.
[http://dx.doi.org/10.1021/ct8004326] [PMID: 26609729]
[31]
Hunter, C.A. Quantifying intermolecular interactions: Guidelines for the molecular recognition toolbox. Angew. Chem. Int. Ed., 2004, 43(40), 5310-5324.
[http://dx.doi.org/10.1002/anie.200301739] [PMID: 15468180]
[32]
McKenzie, J.; Feeder, N.; Hunter, C.A. H-bond competition experiments in solution and the solid state. CrystEngComm, 2016, 18(3), 394-397.
[http://dx.doi.org/10.1039/C5CE02223A]
[33]
Musumeci, D.; Hunter, C.A.; Prohens, R.; Scuderi, S.; McCabe, J.F. Virtual cocrystal screening. Chem. Sci., 2011, 2(5), 883-890.
[http://dx.doi.org/10.1039/c0sc00555j]
[34]
Grecu, T.; Hunter, C.A.; Gardiner, E.J.; McCabe, J.F. Validation of a computational cocrystal prediction tool: Comparison of virtual and experimental cocrystal screening results. Cryst. Growth & Desig., 2014, 14, 165-171.
[35]
Salem, A.; Nagy, S.; Pál, S.; Széchenyi, A. Reliability of the Hansen solubility parameters as co-crystal formation prediction tool. Int. J. Pharm., 2019, 558, 319-327.
[http://dx.doi.org/10.1016/j.ijpharm.2019.01.007] [PMID: 30654064]
[36]
Mohammad, M.A.; Alhalaweh, A.; Velaga, S.P. Hansen solubility parameter as a tool to predict cocrystal formation. Int. J. Pharm., 2011, 407(1-2), 63-71.
[http://dx.doi.org/10.1016/j.ijpharm.2011.01.030] [PMID: 21256944]
[37]
Klamt, A. Solvent-screening and co-crystal screening for drug development with cosmo-rs. J. Cheminformat., 2012, 4, 1-2.
[38]
Loschen, C.; Klamt, A. Cocrystal ternary phase diagrams from density functional theory and solvation thermodynamics. Cryst. Growth Des., 2018, 18(9), 5600-5608.
[http://dx.doi.org/10.1021/acs.cgd.8b00923]
[39]
Cysewski, P.; Przybyłek, M. Selection of effective cocrystals former for dissolution rate improvement of active pharmaceutical ingredients based on lipoaffinity index. Eur. J. Pharm. Sci., 2017, 107, 87-96.
[http://dx.doi.org/10.1016/j.ejps.2017.07.004] [PMID: 28687528]
[40]
Roca-Paixão, L.; Correia, N.T.; Affouard, F. Affinity prediction computations and mechanosynthesis of carbamazepine based cocrystals. CrystEngComm, 2019, 21(45), 6991-7001.
[http://dx.doi.org/10.1039/C9CE01160A]
[41]
LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature, 2015, 521(7553), 436-444.
[http://dx.doi.org/10.1038/nature14539] [PMID: 26017442]
[42]
Mswahili, M.E.; Martin, G.L.; Woo, J.; Choi, G.J.; Jeong, Y.S. Antimalarial drug predictions using molecular descriptors and machine learning against Plasmodium falciparum. Biomolecules, 2021, 11(12), 1750.
[http://dx.doi.org/10.3390/biom11121750] [PMID: 34944394]
[43]
Jiang, D. Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models. J. Cheminformat., 2021, 13, 1-23.
[44]
Wu, F. Simplifying graph convolutional networks. arXiv: 1902.07153, 2019.
[45]
Kipf, T.N.; Welling, M. Semi-supervised classification with graph convolutional networks. arXiv: 1609.02907, 2016.
[46]
Schlichtkrull, M. Modeling relational data with graph convolutional networks. 15th International Conference, ESWC 2018, June 3–7, 2018 Heraklion, Crete, Greece, pp. 593-607, 2018.
[http://dx.doi.org/10.1007/978-3-319-93417-4_38]
[47]
Thanapalasingam, T.; van Berkel, L.; Bloem, P.; Groth, P. Relational graph convolutional networks: A closer look. PeerJ Comput. Sci., 2022, 8, e1073.
[http://dx.doi.org/10.7717/peerj-cs.1073] [PMID: 36426239]
[48]
Kim, S.; Bae, S.; Piao, Y.; Jo, K. Graph convolutional network for drug response prediction using gene expression data. Mathematics, 2021, 9(7), 772.
[http://dx.doi.org/10.3390/math9070772]
[49]
Lee, M.J.; Kim, J.Y.; Kim, P.; Lee, I.S.; Mswahili, M.E.; Jeong, Y.S.; Choi, G.J. Novel cocrystals of vonoprazan: Machine learning-assisted discovery. Pharmaceutics, 2022, 14(2), 429.
[http://dx.doi.org/10.3390/pharmaceutics14020429] [PMID: 35214161]
[50]
Kim, P.; Lee, I.S.; Kim, J.Y.; Mswahili, M.E.; Jeong, Y.S.; Yoon, W.J.; Yun, H.S.; Lee, M.J.; Choi, G.J. A study to discover novel pharmaceutical cocrystals of pelubiprofen with a machine learning approach compared. CrystEngComm, 2022, 24(21), 3938-3952.
[http://dx.doi.org/10.1039/D2CE00153E]
[51]
Jiang, Y.; Yang, Z.; Guo, J.; Li, H.; Liu, Y.; Guo, Y.; Li, M.; Pu, X. Coupling complementary strategy to flexible graph neural network for quick discovery of coformer in diverse co-crystal materials. Nat. Commun., 2021, 12(1), 5950.
[http://dx.doi.org/10.1038/s41467-021-26226-7] [PMID: 34642333]
[52]
sklearn.preprocessing.standardscaler. Available from: https://scikit-earn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html (Accessed on: 2023-03-15).
[53]
Moriwaki, H.; Tian, Y.S.; Kawashita, N.; Takagi, T. Mordred: A molecular descriptor calculator. J. Cheminformat., 2018, 10, 1-14.
[54]
RDKit: Open-source cheminformatics software. Available from: http://www.rdkit.org/
[55]
Featurizers. Available from: https://deepchem.readthedocs.io/en/latest/api_reference/featurizers.html# mordreddescriptors (Accessed on: 2023-03-15).
[56]
Ding, Y.; Jiang, X.; Kim, Y. Relational graph convolutional networks for predicting blood-brain barrier penetration of drug molecules. Bioinformatics, 2022, 38(10), 2826-2831.
[PMID: 35561199]
[57]
Hu, Z.; Dong, Y.; Wang, K.; Sun, Y. Heterogeneous graph transformer. arXiv:2003.01332, 2020.
[58]
Creating message passing networks. Available from: https://pytorch-geometric.readthedocs.io/en/latest/notes/create_gnn.html (Accessed on: 2023-03-15).
[59]
Fey, M.; Lenssen, J.E. Fast graph representation learning with pytorch geometric. arXiv preprint arXiv: 1903.02428, 2019.
[60]
Hamilton, W.; Ying, Z.; Leskovec, J. Inductive representation learning on large graphs. arXiv:1706.02216, 2017.
[61]
Devogelaer, J-J.; Meekes, H.; Tinnemans, P.; Vlieg, E.; de Gelder, R. Co-crystal prediction by artificial neural networks. Angew. Chem. Int. Ed. Engl., 2020, 59(48), 21711-21718.
[http://dx.doi.org/10.1002/anie.202009467] [PMID: 32797658]
[62]
Zhou, K. Understanding and resolving performance degradation in deep graph convolutional networks. Proceedings of the 30th ACM International Conference on Information & Knowledge Management, November 1–5, 2021Virtual Event, Australia, 2021, pp. 2728-2737.
[63]
Li, G.; Muller, M.; Thabet, A.; Ghanem, B. Deepgcns: Can gcns go as deep as cnns? Proceedings of the IEEE/CVF International Conference On Computer Vision, 2019-02 November 2019 27 October 2019-02 November 2019 Seoul, Korea, pp. 9267-9276, 2019.
[64]
Rong, Y.; Huang, W.; Xu, T.; Huang, J. Dropedge: Towards deep graph convolutional networks on node classification. arXiv:1907.10903, 2019.
[65]
Li, Q.; Han, Z.; Wu, X-M. Deeper insights into graph convolutional networks for semi-supervised learning. Proceedings of the AAAI Conference on Artificial Intelligence, 2018, pp. 3538-3545.
[66]
Li, G.; Xiong, C.; Thabet, A.; Ghanem, B. Deepergcn: All you need to train deeper gcns. arXiv preprint arXiv:2006.07739, 2020.
[67]
Do we need deep graph neural networks? Available from: https://towardsdatascience.com/ (Accessed on: 2023-03-15).
[68]
Oono, K.; Suzuki, T. Graph neural networks exponentially lose expressive power for node classification. arXiv preprint arXiv:1905.10947, 2019.
[69]
Chen, M.; Wei, Z.; Huang, Z.; Ding, B.; Li, Y. Simple and deep graph convolutional networks. Proceedings of the 37th International Conference on Machine Learning, PMLR, 2020, pp. 1725-1735.
[70]
Chen, D. Measuring and relieving the over-smoothing problem for graph neural networks from the topological view. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 3438-3445.
[71]
Alon, U.; Yahav, E. On the bottleneck of graph neural networks and its practical implications. arXiv preprint arXiv:2006.05205, 2020.
[http://dx.doi.org/10.48550/arXiv.2006.05205]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy