Bioactivity predictions and virtual screening using machine learning predictive model

References

• Ahamad, S., Hema, K., & Gupta, D. (2023). Identification of novel Tau-tubulin kinase 2 inhibitors using computational approaches. ACS Omega, 8(14), 13026–13037, https://doi.org/10.1021/acsomega.3c00225
   PubMed Web of Science ®Google Scholar
• Ahamad, S., Hema, K., Kumar, V., & Gupta, D. (2021). The structural, functional, and dynamic effect of Tau tubulin kinase1 upon a mutation: A neuro‐degenerative hotspot. Journal of Cellular Biochemistry, 122(11), 1653–1664. https://doi.org/10.1002/jcb.30112
   PubMed Web of Science ®Google Scholar
• Ahamad, S., Kanipakam, H., Kumar, V., & Gupta, D. (2021). A molecular journey to check the conformational dynamics of tau tubulin kinase 2 mutations associated with Alzheimer’s disease. RSC Advances, 11(3), 1320–1331, https://doi.org/10.1039/d0ra07659g
   PubMed Web of Science ®Google Scholar
• Alsukaibi, A. K. D., Alenezi, K. M., Haque, A., Ahmad, I., Saeed, M., Verma, M., Ansari, I. A., & Hsieh, M. (2023). Chemical, biological and in silico assessment of date (P. dactylifera L.) fruits grown in Ha’il region. Frontiers in Chemistry, 11, 1138057, https://doi.org/10.3390/ijms17060920
   PubMed Web of Science ®Google Scholar
• Barbosa, G., Camelo, R., Cavalcanti, A. P., Miranda, P., Mello, R. F., Kovanović, V., & Gašević, D. (2020). Towards automatic cross-language classification of cognitive presence in online discussions [Paper presentation]. Proceedings of the Tenth International Conference on Learning Analytics & Knowledge, Frankfurt, Germany (pp. 605–614). https://doi.org/10.1145/3375462.3375496
   Google Scholar
• Bemis, G. W., & Murcko, M. A. (1996). The properties of known drugs. 1. Molecular frameworks. Journal of Medicinal Chemistry, 39(15), 2887–2893. https://doi.org/10.1021/jm9602928
   PubMed Web of Science ®Google Scholar
• Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate - a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B, 57(1), 289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.
   Google Scholar
• Cateni, S., Colla, V., & Vannucci, M. (2014). A method for resampling imbalanced datasets in binary classification tasks for real-world problems. Neurocomputing, 135, 32–41. https://doi.org/10.1016/j.neucom.2013.05.059.
   Web of Science ®Google Scholar
• Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357, https://doi.org/10.1613/jair.953.
   Web of Science ®Google Scholar
• Cheng, F., Li, W., Zhou, Y., Shen, J., Wu, Z., Liu, G., Lee, P. W., & Tang, Y. (2012). ADMET SAR: A comprehensive source and free tool for assessment of chemical ADMET properties. ACS Publications.
   Google Scholar
• Cherkasov, A., Muratov, E. N., Fourches, D., Varnek, A., Baskin, I. I., Cronin, M., Dearden, J., Gramatica, P., Martin, Y. C., Todeschini, R., Consonni, V., Kuz’min, V. E., Cramer, R., Benigni, R., Yang, C., Rathman, J., Terfloth, L., Gasteiger, J., Richard, A., & Tropsha, A. (2014). QSAR modeling: Where have you been? Where are you going to? Journal of Medicinal Chemistry, 57(12), 4977–5010. https://doi.org/10.1021/jm4004285
   PubMed Web of Science ®Google Scholar
• Davis, J., & Goadrich, M. (2006). The relationship between Precision-Recall and ROC curves [Paper presentation]. Proceedings of the 23rd International Conference on Machine Learning, Association for Computing Machinery, New York, NY, United States (pp. 233–240).
   Google Scholar
• Duvenaud, D. K.; Maclaurin, D.; Iparraguirre, J.; Bombarell, R.; Hirzel, T.; Aspuru-Guzik, A.; Adams, R. P., Convolutional networks on graphs for learning molecular fingerprints. RSC Advances, 2015, 28, 1320–1331.
   Google Scholar
• Ekins, S., Puhl, A. C., Zorn, K. M., Lane, T. R., Russo, D. P., Klein, J. J., Hickey, A. J., & Clark, A. M. (2019). Exploiting machine learning for end-to-end drug discovery and development. Nature Materials, 18(5), 435–441. https://doi.org/10.1038/s41563-019-0338-z.
   PubMed Web of Science ®Google Scholar
• Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874, https://doi.org/10.1016/j.patrec.2005.10.010
   Web of Science ®Google Scholar
• Fletcher, S., & Islam, M. Z. (2018). Comparing sets of patterns with the Jaccard index. Australasian Journal of Information Systems, 22, 1-17. https://doi.org/10.3127/ajis.v22i0.1538.
   Google Scholar
• Forman, G., & Scholz, M. (2010). Apples-to-apples in cross-validation studies: Pitfalls in classifier performance measurement. ACM SIGKDD Explorations Newsletter, 12(1), 49–57. https://doi.org/10.1145/1882471.1882479.
   Google Scholar
• Furman, B. T., Leone, E. H., Bell, S. S., Durako, M. J., & Hall, M. O. (2018). Braun-Blanquet data in ANOVA designs: Comparisons with percent cover and transformations using simulated data. Marine Ecology Progress Series, 597, 13–22. https://doi.org/10.3354/meps12604.
   Web of Science ®Google Scholar
• Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. O'Reilly Media, Inc.
   Google Scholar
• Gupta, S., Kapoor, P., Chaudhary, K., Gautam, A., Kumar, R., & Raghava, G. P., Open Source Drug Discovery. (2013). In silico approach for predicting toxicity of peptides and proteins. PLOS One, 8(9), e73957. https://doi.org/10.1371/journal.pone.0073957
   PubMed Web of Science ®Google Scholar
• Hamming, R. (1950). Error detecting and error correcting codes. Bell System Technical Journal, 29(2), 147–160. https://doi.org/10.1002/j.1538-7305.1950.tb00463.
   Google Scholar
• Hastie, T., Tibshirani, R., Friedman, J., Hastie, T., Tibshirani, R., & Friedman, J. (2009). Prediction unsupervised learning (vol. 2, pp. 1–758). Springer.
   Google Scholar
• Heydt, M. (2017). Learning pandas. Packt Publishing Ltd.
   Google Scholar
• Hosmer, D. W., Jr., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (vol. 398). John Wiley & Sons.
   Google Scholar
• Husna, N. A., Bustamam, A., Yanuar, A., Sarwinda, D., & Hermansyah, O. (2020). The comparison of machine learning methods for prediction study of type 2 diabetes mellitus’s drug design. AIP Conference Proceedings. AIP Publishing.
   Google Scholar
• Irizarry, R. A. (2019). Introduction to data science: Data analysis and prediction algorithms with R. CRC Press.
   Google Scholar
• Jang, J., & Kim, C. O., Unstructured borderline self-organizing map: Learning highly imbalanced, high-dimensional datasets for fault detection. Expert Systems with Applications, 2022, 188, 116028, https://doi.org/10.1016/j.eswa.2021.116028.
   Web of Science ®Google Scholar
• Kim, K. H., Kim, N. D., & Seong, B. L. (2010). Pharmacophore-based virtual screening: A review of recent applications. Expert Opinion on Drug Discovery, 5(3), 205–222. https://doi.org/10.1517/17460441003592072.
   PubMed Web of Science ®Google Scholar
• Kubat, M., & Matwin, S. (1997) Addressing the curse of imbalanced training sets: One-sided selection. Proceedings of the 14th International Conference on Machine Learning (pp. 179–186).
   Google Scholar
• Kumar, A., & Zhang, K. Y. (2015). Hierarchical virtual screening approaches in small molecule drug discovery. Methods, 71, 26–37. https://doi.org/10.1016/j.ymeth.2014.07.007.
   PubMed Web of Science ®Google Scholar
• Kurdekar, V., & Jadhav, H. R. (2015). A new open source data analysis python script for QSAR study and its validation. Medicinal Chemistry Research, 24(4), 1617–1625. https://doi.org/10.1007/s00044-014-1240-5.
   Web of Science ®Google Scholar
• Landrum, G. (2006). RDKit: Open-source cheminformatics. In Chemoinformatics drug-design pharmacophores software. Zenodo, https://doi.org/10.5281/zendo.10398.
   Google Scholar
• Ma, J., Sheridan, R. P., Liaw, A., Dahl, G. E., & Svetnik, V. (2015). Deep neural nets as a method for quantitative structure-activity relationships. Journal of Chemical Information and Modeling, 55(2), 263–274. https://doi.org/10.1021/ci500747n.
   PubMed Web of Science ®Google Scholar
• Madhavi Sastry, G., Adzhigirey, M., Day, T., Annabhimoju, R., & Sherman, W. (2013). Protein and ligand preparation: Parameters, protocols, and influence on virtual screening enrichments. Journal of Computer-Aided Molecular Design, 27(3), 221–234. https://doi.org/10.1007/s10822-013-9644-8
   PubMed Web of Science ®Google Scholar
• Mahesh, B. (2020). Machine learning algorithms-a review. International Journal of Science Research, 9(1), 381–386.
   Google Scholar
• Meduru, H., Wang, Y. T., Tsai, J. J., & Chen, Y. C. (2016). Finding a potential dipeptidyl peptidase-4 (DPP-4) inhibitor for type-2 diabetes: Treatment based on molecular docking, pharmacophore generation, and molecular dynamics simulation. International Journal of Molecular Sciences, 17(6), 920, https://doi.org/10.3390/ijms17060920
   PubMed Web of Science ®Google Scholar
• Mitchell, T. M. (2006). The discipline of machine learning. Carnegie Mellon University, School of Computer Science.
   Google Scholar
• Neudert, G., & Klebe, G. (2011). DSX: A knowledge-based scoring function for the assessment of protein-ligand complexes. Journal of Chemical Information and Modeling, 51(10), 2731–2745. https://doi.org/10.1021/ci200274q.
   PubMed Web of Science ®Google Scholar
• Neumann, J., Schnörr, C., & Steidl, G. (2005). Combined SVM-based feature selection and classification. Machine Learning, 61(1–3), 129–150. https://doi.org/10.1007/s10994-005-1505-9
   Web of Science ®Google Scholar
• Ojeda‐Montes, M. J., Gimeno, A., Tomas‐Hernández, S., Cereto‐Massagué, A., Beltrán‐Debón, R., Valls, C., Mulero, M., Pujadas, G., & Garcia‐Vallvé, S. (2018). Activity and selectivity cliffs for DPP‐IV inhibitors: Lessons we can learn from SAR studies and their application to virtual screening. Medicinal Research Reviews, 38(6), 1874–1915. https://doi.org/10.1002/med.21499.
   PubMed Web of Science ®Google Scholar
• Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
   Web of Science ®Google Scholar
• Powers, D. M. (2020). Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation. International Journal of Machine Learning Technology, 2:1, 37–63. https://doi.org/10.48550/arXiv.2010.16061.
   Google Scholar
• Qiao, Z., Welborn, M., Anandkumar, A., Manby, F. R., & Miller, T. F. 3rd, (2020). OrbNet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features. The Journal of Chemical Physics, 153(12), 124111. https://doi.org/10.1063/5.0021955.
   PubMed Web of Science ®Google Scholar
• Shyam, R., & Singh, Y. N. (2014). Evaluation of Eigenfaces and Fisherfaces using Bray Curtis dissimilarity metric [Paper presentation]. 2014 9th International Conference on Industrial and Information Systems (ICIIS) (pp. 1–6). IEEE.
   Google Scholar
• Sliwoski, G., Kothiwale, S., Meiler, J., & Lowe, E. W., Jr. (2014). Computational methods in drug discovery. Pharmacological Reviews, 66(1), 334–395. https://doi.org/10.1124/pr.112.007336.
   PubMed Web of Science ®Google Scholar
• Subhani, A., Arif, N., Hussain, W., & Rasool, N. (2020). In silico discovery of potential inhibitors against dipeptidyl peptidase-4: A major biological target of type-2 diabetes mellitus. International Journal of Clinical Microbiology and Biochemical Technology, 3, 9375–9414. https://doi.org/10.29328/journal.ijcmbt.1001008.
   Google Scholar
• Sud, M. (2016). MayaChemTools: An open source package for computational drug discovery. Journal of Chemical Information and Modeling, 56(12), 2292–2297. https://doi.org/10.1021/acs.jcim.6b00505.
   PubMed Web of Science ®Google Scholar
• Tripathi, S. K., Muttineni, R., & Singh, S. K. (2013). Extra precision docking, free energy calculation and molecular dynamics simulation studies of CDK2 inhibitors. Journal of Theoretical Biology, 334(10), 87–100. https://doi.org/10.1016/j.jtbi.2013.05.014
   PubMedGoogle Scholar
• Wishart, D. S., Feunang, Y. D., Guo, A. C., Lo, E. J., Marcu, A., Grant, J. R., Sajed, T., Johnson, D., Li, C., Sayeeda, Z., Assempour, N., Iynkkaran, I., Liu, Y., Maciejewski, A., Gale, N., Wilson, A., Chin, L., Cummings, R., Le, D., … Wilson, M. (2018). DrugBank 5.0: A major update to the DrugBank database for 2018. Nucleic Acids Research, 46(D1), D1074–D1082. https://doi.org/10.1093/nar/gkx1037.
   PubMed Web of Science ®Google Scholar
• Wu, G., Zhao, T., Kang, D., Zhang, J., Song, Y.; Namasivayam, V., Kongsted, J., Pannecouque, C., De Clercq, E., Poongavanam, V., Liu, X., & Zhan, P. (2019). Overview of recent strategic advances in medicinal chemistry. Journal of Medicinal Chemistry, 62(21), 9375–9414, https://doi.org/10.1021/acs.jmedchem.9b00359
   PubMed Web of Science ®Google Scholar
• Yadav, S., Ahamad, S., Gupta, D., & Mathur, P. Lead optimization, pharmacophore development and scaffold design of protein kinase CK2 inhibitors as potential COVID-19 therapeutics. Journal of Biomolecular Structure and Dynamics, 2023, 41 (5), 1811–1827, https://doi.org/10.1080/07391102.2021.2024449 https://doi.org/10.1021/acsomega.3c00225
   PubMed Web of Science ®Google Scholar
• Yongye, A. B., Waddell, J., & Medina-Franco, J. L. (2012). Molecular scaffold analysis of natural products databases in the public domain. Chemical Biology & Drug Design, 80(5), 717–724. https://doi.org/10.1111/cbdd.12011.
   PubMed Web of Science ®Google Scholar