257
Views
0
CrossRef citations to date
0
Altmetric
Research Articles

QPoweredCompound2DeNovoDrugPropMax – a novel programmatic tool incorporating deep learning and in silico methods for automated in silico bio-activity discovery for any compound of interest

, &
Pages 1790-1797 | Received 26 Aug 2021, Accepted 26 Dec 2021, Published online: 10 Jan 2022

References

  • Aggarwal, C. C. (2011). An introduction to social network data analytics. In Social network data analytics (pp. 1–15). Springer.
  • Al Hasan, M., & Zaki, M. J. (2011). A survey of link prediction in social networks. In Social network data analytics (pp. 243–275). Springer.
  • Aleksandrowicz, G., Alexander, T., Barkoutsos, P., Bello, L., Ben-Haim, Y., Bucher, D., Cabrera-Hernández, F. J., Carballo-Franquis, J., Chen, A., Chen, C. F., & Chow, J. M. (2019). Qiskit: An open-source framework for quantum computing. Retrieved March 16.
  • Allocati, N., Masulli, M., Di Ilio, C., & Federici, L. (2018). Glutathione transferases: Substrates, inihibitors and pro-drugs in cancer and neurodegenerative diseases. Oncogenesis, 7(1), 8–15. https://doi.org/10.1038/s41389-017-0025-3
  • Almansoori, W., Gao, S., Jarada, T. N., Elsheikh, A. M., Murshed, A. N., Jida, J., Alhajj, R., & Rokne, J. (2012). Link prediction and classification in social networks and its application in healthcare and systems biology. Network Modeling Analysis in Health Informatics and Bioinformatics, 1(1-2), 27–36. https://doi.org/10.1007/s13721-012-0005-7
  • Baell, J. B., & Holloway, G. A. (2010). New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. Journal of Medicinal Chemistry, 53(7), 2719–2740. https://doi.org/10.1021/jm901137j
  • Bergholm, V., Izaac, J., Schuld, M., Gogolin, C., Alam, M. S., Ahmed, S., Arrazola, J. M., Blank, C., Delgado, A., Jahangiri, S., & McKiernan, K. (2018). Pennylane: Automatic differentiation of hybrid quantum-classical computations. arXiv preprint arXiv:1811.04968.
  • Bernardi, A., Faller, R., Reith, D., & Kirschner, K. N. (2019). ACPYPE update for nonuniform 1–4 scale factors: Conversion of the GLYCAM06 force field from AMBER to GROMACS. SoftwareX, 10, 100241. https://doi.org/10.1016/j.softx.2019.100241
  • Brenk, R., Schipani, A., James, D., Krasowski, A., Gilbert, I. H., Frearson, J., & Wyatt, P. G. (2008). Lessons learnt from assembling screening libraries for drug discovery for neglected diseases. ChemMedChem, 3(3), 435–444. https://doi.org/10.1002/cmdc.200700139
  • Cao, Y., Romero, J., Olson, J. P., Degroote, M., Johnson, P. D., Kieferová, M., Kivlichan, I. D., Menke, T., Peropadre, B., Sawaya, N. P. D., Sim, S., Veis, L., & Aspuru-Guzik, A. (2019). Quantum chemistry in the age of quantum computing. Chemical Reviews, 119(19), 10856–10915. https://doi.org/10.1021/acs.chemrev.8b00803
  • Cross, A. (2018). The IBM Q experience and QISKit open-source quantum computing software. In APS March Meeting Abstracts (Vol. 2018, pp. L58-003).
  • da Silva Soares, P. R., & Prudêncio, R. B. C. (2012). Time series based link prediction [Paper presentation]. The 2012 International Joint Conference on Neural Networks (IJCNN). IEEE.
  • Doveston, R. G., Tosatti, P., Dow, M., Foley, D. J., Li, H. Y., Campbell, A. J., House, D., Churcher, I., Marsden, S. P., & Nelson, A. (2015). A unified lead-oriented synthesis of over fifty molecular scaffolds. Organic & Biomolecular Chemistry, 13(3), 859–865. https://doi.org/10.1039/c4ob02287d
  • Fakoor, R. (2013). Using deep learning to enhance cancer diagnosis and classification. Proceedings of the International Conference on Machine Learning (Vol. 28). ACM.
  • Farooq, A. (2017). A deep CNN based multi-class classification of Alzheimer's disease using MRI [Paper presentation]. 2017 IEEE International Conference on Imaging Systems and Techniques (IST), IEEE. https://doi.org/10.1109/IST.2017.8261460
  • Geoffrey A S, B., Madaj, R., Sanker, A., Tresanco, M. S. V., Davidd, H. A., & Roy, G. (2020). A programmatic tool for automatic ease in coronavirus drug discovery through programmatically automated data mining, QSAR and In Silico modelling. ChemRxiv, Preprint.
  • Geoffrey A S, B., Sanker, A., Madaj, R., Tresanco, M. S., Upadhyay, M., & Gracia, J. (2020). A program to automate the discovery of drugs for West Nile and Dengue virus – Programmatic screening of over a billion compounds on PubChem, generation of drug leads and automated In Silico modelling. BioRxiv.
  • Gilson, M. K., Liu, T., Baitaluk, M., Nicola, G., Hwang, L., & Chong, J. (2016). BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Research, 44(D1), D1045–D1053. https://doi.org/10.1093/nar/gkv1072
  • Grover, A., & Leskovec, J. (2016). node2vec: Scalable feature learning for networks. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
  • Hagberg, A., Swart, P., & Chult, D. S. (2008). Exploring network structure, dynamics, and function using NetworkX. No. LA-UR-08-05495; LA-UR-08-5495. Los Alamos National Lab (LANL).
  • Hashemifar, S., Neyshabur, B., Khan, A. A., & Xu, J. (2018). Predicting protein-protein interactions through sequence-based deep learning. Bioinformatics, 34(17), i802–i810. https://doi.org/10.1093/bioinformatics/bty573
  • Hassija, V., Chamola, V., Saxena, V., Chanana, V., Parashari, P., Mumtaz, S., & Guizani, M. (2020). Present landscape of quantum computing. IET Quantum Communication, 1(2), 42–48. https://doi.org/10.1049/iet-qtc.2020.0027
  • Jaghoori, M. M., Bleijlevens, B., & Olabarriaga, S. D. (2016). 1001 ways to run AutoDock Vina for virtual screening. Journal of Computer-Aided Molecular Design, 30(3), 237–249. https://doi.org/10.1007/s10822-016-9900-9
  • Jalili, M., Orouskhani, Y., Asgari, M., Alipourfard, N., & Perc, M. (2017). Link prediction in multiplex online social networks. Royal Society Open Science, 4(2), 160863. https://doi.org/10.1098/rsos.160863
  • Kirsopp, J. J. M., Paola, C. D., Manrique, D. Z., Krompiec, M., Greene-Diniz, G., Guba, W., Meyder, A., Wolf, D., Strahm, M., & Ramo, D. M. (2021). Quantum computational quantification of protein-ligand interactions. arXiv preprint arXiv:2110.08163.
  • Lee, Y. K., Deng, P. X., & DeLorme, L. A. (2013). Automatically generating nodes and edges in an integrated social graph. U.S. Patent No. 8,572,129.
  • Liu, T., Lin, Y., Wen, X., Jorissen, R. N., & Gilson, M. K. (2007). BindingDB: A web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Research, 35(Database issue), D198–D201. https://doi.org/10.1093/nar/gkl999
  • Lyu, B., & Haque, A. (2018). Deep learning based tumor type classification using gene expression data [Paper presentation]. Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics.
  • MacQuarrie, E. R., Simon, C., Simmons, S., & Maine, E. (2020). The emerging commercial landscape of quantum computing. Nature Reviews Physics, 2(11), 596–598. https://doi.org/10.1038/s42254-020-00247-5
  • Madaj, R., Sanker, A., Geoffrey A S, B., David, H. A., Verma, S., Gracia, J., Faletif, A. I., & Yakubu, A. H. (2020). Automated identification of small drug molecules for Hepatitis C virus through a novel programmatic tool and extensive molecular dynamics studies of select drug candidates. bioRxiv.
  • Maxwell, A., Li, R., Yang, B., Weng, H., Ou, A., Hong, H., Zhou, Z., Gong, P., & Zhang, C. (2017). Deep learning architectures for multi-label classification of intelligent health risk prediction. BMC Bioinformatics, 18(Suppl 14), 523. https://doi.org/10.1186/s12859-017-1898-z
  • Murata, T., & Moriyasu, S. (2008). Link prediction based on structural properties of online social networks. New Generation Computing, 26(3), 245–257. https://doi.org/10.1007/s00354-008-0043-y
  • Murthy, V. N. (2016). Deep decision network for multi-class image classification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
  • Ngo, S. T., Vu, K. B., Bui, L. M., & Vu, V. V. (2019). Effective estimation of ligand-binding affinity using biased sampling method. ACS Omega, 4(2), 3887–3893. https://doi.org/10.1021/acsomega.8b03258
  • Pavlopoulos, G. A., Secrier, M., Moschopoulos, C. N., Soldatos, T. G., Kossida, S., Aerts, J., Schneider, R., & Bagos, P. G. (2011). Using graph theory to analyze biological networks. BioData Mining, 4(1), 10. https://doi.org/10.1186/1756-0381-4-10
  • Sekhon, A., Singh, R., & Qi, Y. (2018). DeepDiff: DEEP-learning for predicting DIFFerential gene expression from histone modifications. Bioinformatics, 34(17), i891–i900. https://doi.org/10.1093/bioinformatics/bty612
  • Ståhl, N., Falkman, G., Karlsson, A., Mathiason, G., & Bostrom, J. (2019). Deep reinforcement learning for multiparameter optimization in de novo drug design. Journal of Chemical Information and Modeling, 59(7), 3166–3176. https://doi.org/10.1021/acs.jcim.9b00325
  • Szymański, P., & Kajdanowicz, T. (2017). A scikit-based Python environment for performing multi-label classification. arXiv preprint arXiv:1702.01460.
  • Tang, J., Aggarwal, C., & Liu, H. (2016). Node classification in signed social networks. Proceedings of the 2016 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611974348.7
  • Tasaki, S. (2020). Deep learning decodes the principles of differential gene expression. Nature Machine Intelligence, 1–11.
  • Trinajstic, N. (2018). Chemical graph theory. Routledge.
  • Vazquez, A., Flammini, A., Maritan, A., & Vespignani, A. (2003). Global protein function prediction from protein-protein interaction networks. Nature Biotechnology, 21(6), 697–700. https://doi.org/10.1038/nbt825
  • Wang, E., Sun, H., Wang, J., Wang, Z., Liu, H., Zhang, J. Z., & Hou, T. (2019). End-point binding free energy calculation with MM/PBSA and MM/GBSA: Strategies and applications in drug design. Chemical Reviews, 119(16), 9478–9508. https://doi.org/10.1021/acs.chemrev.9b00055
  • Wang, S., Raju, A., & Huang, J. (2017). Deep learning based multi-label classification for surgical tool presence detection in laparoscopic videos [Paper presentation]. 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), IEEE. https://doi.org/10.1109/ISBI.2017.7950597
  • Wang, Y., Murlidaran, S., & Pearlman, D. A. (2021). Quantum simulations of SARS-CoV-2 main protease Mpro enable high-quality scoring of diverse ligands. Journal of Computer-Aided Molecular Design, 35(9), 963–971. https://doi.org/10.1007/s10822-021-00412-7
  • Xiao, Y., Wu, J., Lin, Z., & Zhao, X. (2018). A deep learning-based multi-model ensemble method for cancer prediction. Computer Methods and Programs in Biomedicine, 153, 1–9. https://doi.org/10.1016/j.cmpb.2017.09.005
  • Xu, Y., Pei, J., & Lai, L. (2017). Deep learning based regression and multiclass models for acute oral toxicity prediction with automatic chemical feature extraction. Journal of Chemical Information and Modeling, 57(11), 2672–2685. https://doi.org/10.1021/acs.jcim.7b00244
  • Zhang, B., Li, J., Quan, L., Chen, Y., & Lü, Q. (2019). Sequence-based prediction of protein-protein interaction sites by simplified long short-term memory network. Neurocomputing, 357, 86–100. https://doi.org/10.1016/j.neucom.2019.05.013
  • Zheleva, E. (2008). Using friendship ties and family circles for link prediction. In International workshop on social network mining and analysis. Springer.
  • Zuegg, J., & Cooper, M. A. (2012). Drug-likeness and increased hydrophobicity of commercially available compound libraries for drug screening. Current Topics in Medicinal Chemistry, 12(14), 1500–1513. https://doi.org/10.2174/156802612802652466

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.