{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T16:10:32Z","timestamp":1743005432244,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030862572"},{"type":"electronic","value":"9783030862589"}],"license":[{"start":{"date-parts":[[2021,8,28]],"date-time":"2021-08-28T00:00:00Z","timestamp":1630108800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,8,28]],"date-time":"2021-08-28T00:00:00Z","timestamp":1630108800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-030-86258-9_15","type":"book-chapter","created":{"date-parts":[[2021,8,27]],"date-time":"2021-08-27T14:06:00Z","timestamp":1630073160000},"page":"145-154","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Comparison of Different Compound Representations for Drug Sensitivity Prediction"],"prefix":"10.1007","author":[{"given":"Delora","family":"Baptista","sequence":"first","affiliation":[]},{"given":"Jo\u00e3o","family":"Correia","sequence":"additional","affiliation":[]},{"given":"Bruno","family":"Pereira","sequence":"additional","affiliation":[]},{"given":"Miguel","family":"Rocha","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,8,28]]},"reference":[{"key":"15_CR1","unstructured":"Abadi, M., et al.: TensorFlow: a system for large-scale machine learning. In: Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation, vol.\u00a016, pp. 265\u2013283 (2016)"},{"issue":"1","key":"15_CR2","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1038\/s41698-020-0122-1","volume":"4","author":"G Adam","year":"2020","unstructured":"Adam, G., Ramp\u00e1\u0161ek, L., Safikhani, Z., Smirnov, P., Haibe-Kains, B., Goldenberg, A.: Machine learning approaches to drug response prediction: challenges and recent progress. NPJ Precis. Oncol. 4(1), 19 (2020). https:\/\/doi.org\/10.1038\/s41698-020-0122-1","journal-title":"NPJ Precis. Oncol."},{"issue":"1","key":"15_CR3","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1007\/s12551-018-0446-z","volume":"11","author":"M Ali","year":"2018","unstructured":"Ali, M., Aittokallio, T.: Machine learning and feature selection for drug response prediction in precision oncology applications. Biophys. Rev. 11(1), 31\u201339 (2018). https:\/\/doi.org\/10.1007\/s12551-018-0446-z","journal-title":"Biophys. Rev."},{"issue":"1","key":"15_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-020-00456-1","volume":"12","author":"AP Bento","year":"2020","unstructured":"Bento, A.P., et al.: An open source chemical structure curation pipeline using RDKit. J. Cheminformatics 12(1), 1\u201316 (2020). https:\/\/doi.org\/10.1186\/s13321-020-00456-1","journal-title":"J. Cheminformatics"},{"issue":"2","key":"15_CR5","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1021\/ci00046a002","volume":"25","author":"RE Carhart","year":"1985","unstructured":"Carhart, R.E., Smith, D.H., Venkataraghavan, R.: Atom pairs as molecular features in structure-activity studies: definition and applications. J. Chem. Inf. Comput. Sci. 25(2), 64\u201373 (1985). https:\/\/doi.org\/10.1021\/ci00046a002","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"15_CR6","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.ymeth.2014.08.005","volume":"71","author":"A Cereto-Massagu\u00e9","year":"2015","unstructured":"Cereto-Massagu\u00e9, A., Ojeda, M.J., Valls, C., Mulero, M., Garcia-Vallv\u00e9, S., Pujadas, G.: Molecular fingerprint similarity search in virtual screening. Methods 71, 58\u201363 (2015). https:\/\/doi.org\/10.1016\/j.ymeth.2014.08.005","journal-title":"Methods"},{"key":"15_CR7","unstructured":"Chollet, F.: Others: Keras (2015). https:\/\/keras.io"},{"issue":"1","key":"15_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-019-0364-5","volume":"11","author":"I Cort\u00e9s-Ciriano","year":"2019","unstructured":"Cort\u00e9s-Ciriano, I., Bender, A.: KekuleScope: prediction of cancer cell line sensitivity and compound potency using convolutional neural networks trained on compound images. J. Cheminformatics 11(1), 1\u201316 (2019). https:\/\/doi.org\/10.1186\/s13321-019-0364-5","journal-title":"J. Cheminformatics"},{"issue":"6","key":"15_CR9","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1021\/ci010132r","volume":"42","author":"JL Durant","year":"2002","unstructured":"Durant, J.L., Leland, B.A., Henry, D.R., Nourse, J.G.: Reoptimization of MDL keys for use in drug discovery. J. Chem. Inf. Comput. Sci. 42(6), 1273\u20131280 (2002). https:\/\/doi.org\/10.1021\/ci010132r","journal-title":"J. Chem. Inf. Comput. Sci."},{"issue":"2","key":"15_CR10","first-page":"399","volume":"56","author":"D Duvenaud","year":"2015","unstructured":"Duvenaud, D., et al.: Convolutional networks on graphs for learning molecular fingerprints. J. Chem. Inf. Model. 56(2), 399\u2013411 (2015)","journal-title":"J. Chem. Inf. Model."},{"issue":"10","key":"15_CR11","doi-asserted-by":"publisher","first-page":"4371","DOI":"10.1021\/acs.molpharmaceut.7b01144","volume":"15","author":"P Hop","year":"2018","unstructured":"Hop, P., Allgood, B., Yu, J.: Geometric deep learning autonomously learns chemical features that outperform those engineered by domain experts. Mol. Pharm. 15(10), 4371\u20134377 (2018). https:\/\/doi.org\/10.1021\/acs.molpharmaceut.7b01144","journal-title":"Mol. Pharm."},{"issue":"3","key":"15_CR12","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1021\/acs.jcim.7b00616","volume":"93","author":"S Jaeger","year":"2018","unstructured":"Jaeger, S., Fulle, S., Turk, S.: Mol2vec: unsupervised machine learning approach with chemical intuition. J. Chem. Inf. Model. 93(3), 297\u2013312 (2018). https:\/\/doi.org\/10.1021\/acs.jcim.7b00616","journal-title":"J. Chem. Inf. Model."},{"key":"15_CR13","doi-asserted-by":"publisher","unstructured":"Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1746\u20131751. Association for Computational Linguistics, Stroudsburg, PA, USA (2014). https:\/\/doi.org\/10.3115\/v1\/D14-1181","DOI":"10.3115\/v1\/D14-1181"},{"key":"15_CR14","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Proceedings of the 3rd International Conference on Learning Representations (2014)"},{"key":"15_CR15","unstructured":"Kipf, T.N., Welling, M.: Semi-Supervised Classification with Graph Convolutional Networks. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, 24\u201326 April 2017, Conference Track Proceedings. OpenReview.net (2017)"},{"key":"15_CR16","unstructured":"Landrum, G., Others: RDKit: Open-source cheminformatics (2006)"},{"issue":"24","key":"15_CR17","doi-asserted-by":"publisher","first-page":"5441","DOI":"10.1039\/C8SC00148K","volume":"9","author":"A Mayr","year":"2018","unstructured":"Mayr, A., et al.: Large-scale comparison of machine learning methods for drug target prediction on ChEMBL. Chem. Sci. 9(24), 5441\u20135451 (2018). https:\/\/doi.org\/10.1039\/C8SC00148K","journal-title":"Chem. Sci."},{"issue":"D1","key":"15_CR18","doi-asserted-by":"publisher","first-page":"D930","DOI":"10.1093\/nar\/gky1075","volume":"47","author":"D Mendez","year":"2019","unstructured":"Mendez, D., et al.: ChEMBL: towards direct deposition of bioassay data. Nucleic Acids Res. 47(D1), D930\u2013D940 (2019). https:\/\/doi.org\/10.1093\/nar\/gky1075","journal-title":"Nucleic Acids Res."},{"issue":"2","key":"15_CR19","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1021\/c160017a018","volume":"5","author":"HL Morgan","year":"1965","unstructured":"Morgan, H.L.: The generation of a unique machine description for chemical structures-a technique developed at chemical abstracts service. J. Chem. Doc. 5(2), 107\u2013113 (1965). https:\/\/doi.org\/10.1021\/c160017a018","journal-title":"J. Chem. Doc."},{"issue":"11","key":"15_CR20","doi-asserted-by":"publisher","first-page":"3783","DOI":"10.1016\/j.patcog.2015.05.019","volume":"48","author":"S Pan","year":"2015","unstructured":"Pan, S., Wu, J., Zhu, X., Long, G., Zhang, C.: Finding the best not the most: regularized loss minimization subgraph selection for graph classification. Pattern Recogn. 48(11), 3783\u20133796 (2015). https:\/\/doi.org\/10.1016\/j.patcog.2015.05.019","journal-title":"Pattern Recogn."},{"key":"15_CR21","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2012","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine Learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2012)","journal-title":"J. Mach. Learn. Res."},{"key":"15_CR22","volume-title":"Deep Learning for the Life Sciences","author":"B Ramsundar","year":"2019","unstructured":"Ramsundar, B., Eastman, P., Walters, P., Pande, V., Leswing, K., Wu, Z.: Deep Learning for the Life Sciences. O\u2019Reilly Media, Newton (2019)"},{"issue":"5","key":"15_CR23","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1021\/ci100050t","volume":"50","author":"D Rogers","year":"2010","unstructured":"Rogers, D., Hahn, M.: Extended-connectivity fingerprints. J. Chem. Inf. Model. 50(5), 742\u2013754 (2010). https:\/\/doi.org\/10.1021\/ci100050t","journal-title":"J. Chem. Inf. Model."},{"issue":"1","key":"15_CR24","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"15_CR25","unstructured":"Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., Bengio, Y.: Graph attention networks. In: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, 30 April\u20133 May 3, 2018, Conference Track Proceedings. OpenReview.net (2018)"},{"issue":"2","key":"15_CR26","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1039\/C7SC02664A","volume":"9","author":"Z Wu","year":"2018","unstructured":"Wu, Z., et al.: MoleculeNet: a benchmark for molecular machine learning. Chem. Sci. 9(2), 513\u2013530 (2018). https:\/\/doi.org\/10.1039\/C7SC02664A","journal-title":"Chem. Sci."},{"issue":"16","key":"15_CR27","doi-asserted-by":"publisher","first-page":"8749","DOI":"10.1021\/acs.jmedchem.9b00959","volume":"63","author":"Z Xiong","year":"2020","unstructured":"Xiong, Z., et al.: Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism. J. Med. Chem. 63(16), 8749\u20138760 (2020). https:\/\/doi.org\/10.1021\/acs.jmedchem.9b00959","journal-title":"J. Med. Chem."}],"container-title":["Lecture Notes in Networks and Systems","Practical Applications of Computational Biology &amp; Bioinformatics, 15th International Conference (PACBB 2021)"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86258-9_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,27]],"date-time":"2021-08-27T14:09:18Z","timestamp":1630073358000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86258-9_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,28]]},"ISBN":["9783030862572","9783030862589"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86258-9_15","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2021,8,28]]},"assertion":[{"value":"28 August 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PACBB","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Practical Applications of Computational Biology & Bioinformatics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Salamanca","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pacbb2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pacbb.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}