{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T20:57:19Z","timestamp":1758056239040,"version":"3.44.0"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032051752","type":"print"},{"value":"9783032051769","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T00:00:00Z","timestamp":1757894400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T00:00:00Z","timestamp":1757894400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-05176-9_29","type":"book-chapter","created":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T13:40:48Z","timestamp":1757943648000},"page":"375-387","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Convolutional Spiking Neural Networks with\u00a0Molecular Fingerprints for\u00a0Drug Discovery"],"prefix":"10.1007","author":[{"given":"Dinu","family":"Bos\u00eei","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luis H. M.","family":"Torres","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joel P.","family":"Arrais","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bernardete","family":"Ribeiro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,15]]},"reference":[{"issue":"6","key":"29_CR1","doi-asserted-by":"publisher","first-page":"1239","DOI":"10.1111\/j.1476-5381.2010.01127.x","volume":"162","author":"JP Hughes","year":"2011","unstructured":"Hughes, J.P., Rees, S.S., Kalindjian, S.B., Philpott, K.L.: Principles of early drug discovery. Br. J. Pharmacol. 162(6), 1239\u20131249 (2011)","journal-title":"Br. J. Pharmacol."},{"key":"29_CR2","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1016\/S1056-8719(00)00107-6","volume":"44","author":"CA Lipinski","year":"2000","unstructured":"Lipinski, C.A.: Drug-like properties and the causes of poor solubility and poor permeability. J. Pharmacol. Toxicol. Methods 44, 235\u2013249 (2000)","journal-title":"J. Pharmacol. Toxicol. Methods"},{"issue":"11","key":"29_CR3","doi-asserted-by":"publisher","first-page":"682","DOI":"10.1038\/nchembio.118","volume":"4","author":"AL Hopkins","year":"2008","unstructured":"Hopkins, A.L.: Network pharmacology: the next paradigm in drug discovery. Nat. Chem. Biol. 4(11), 682\u2013690 (2008)","journal-title":"Nat. Chem. Biol."},{"key":"29_CR4","doi-asserted-by":"publisher","first-page":"967","DOI":"10.1016\/j.drudis.2021.11.023","volume":"27","author":"RSK Vijayan","year":"2022","unstructured":"Vijayan, R.S.K., Kihlberg, J., Cross, J.B., Poongavanam, V.: Enhancing preclinical drug discovery with artificial intelligence. Drug Discov. Today 27, 967\u2013984 (2022)","journal-title":"Drug Discov. Today"},{"key":"29_CR5","doi-asserted-by":"publisher","first-page":"1241","DOI":"10.1016\/j.drudis.2018.01.039","volume":"23","author":"H Chen","year":"2018","unstructured":"Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., Blaschke, T.: The rise of deep learning in drug discovery. Drug Discov. Today 23, 1241\u20131250 (2018)","journal-title":"Drug Discov. Today"},{"key":"29_CR6","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1021\/ci00057a005","volume":"28","author":"D Weininger","year":"1988","unstructured":"Weininger, D.: Smiles, a chemical language and information system: 1: introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci. 28, 31\u201336 (1988)","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"29_CR7","doi-asserted-by":"publisher","first-page":"3370","DOI":"10.1021\/acs.jcim.9b00237","volume":"59","author":"K Yang","year":"2019","unstructured":"Yang, K., et al.: Analyzing learned molecular representations for property prediction. J. Chem. Inf. Model. 59, 3370\u20133388 (2019)","journal-title":"J. Chem. Inf. Model."},{"key":"29_CR8","doi-asserted-by":"publisher","first-page":"1659","DOI":"10.1016\/S0893-6080(97)00011-7","volume":"10","author":"W Maass","year":"1997","unstructured":"Maass, W.: Networks of spiking neurons: the third generation of neural network models. Neural Netw. 10, 1659\u20131671 (1997)","journal-title":"Neural Netw."},{"key":"29_CR9","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1016\/j.neunet.2019.09.036","volume":"122","author":"A Taherkhani","year":"2020","unstructured":"Taherkhani, A., Belatreche, A., Li, Y., Cosma, G., Maguire, L.P., McGinnity, T.M.: A review of learning in biologically plausible spiking neural networks. Neural Netw. 122, 253\u2013272 (2020)","journal-title":"Neural Netw."},{"key":"29_CR10","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/j.patrec.2024.08.002","volume":"189","author":"B Ribeiro","year":"2024","unstructured":"Ribeiro, B., Antunes, F., Perdig\u00e3o, D., Silva, C.: Convolutional spiking neural networks targeting learning and inference in highly imbalanced datasets. Pattern Recogn. Lett. 189, 241\u2013247 (2024)","journal-title":"Pattern Recogn. Lett."},{"key":"29_CR11","doi-asserted-by":"crossref","unstructured":"Yu, W., Mackerell, A.D.: Computer-aided drug design methods. Methods Mol. Biol. (Clifton, N.J.) 1520, 85 (2017)","DOI":"10.1007\/978-1-4939-6634-9_5"},{"key":"29_CR12","doi-asserted-by":"publisher","first-page":"22","DOI":"10.3390\/ph17010022","volume":"17","author":"SK Niazi","year":"2023","unstructured":"Niazi, S.K., Mariam, Z.: Computer-aided drug design and drug discovery: a prospective analysis. Pharmaceuticals 17, 22 (2023)","journal-title":"Pharmaceuticals"},{"key":"29_CR13","doi-asserted-by":"publisher","first-page":"2694","DOI":"10.3762\/bjoc.12.267","volume":"12","author":"SP Leelananda","year":"2016","unstructured":"Leelananda, S.P., Lindert, S.: Computational methods in drug discovery. Beilstein J. Org. Chem. 12, 2694\u20132718 (2016)","journal-title":"Beilstein J. Org. Chem."},{"key":"29_CR14","doi-asserted-by":"crossref","unstructured":"Batool, M., Ahmad, B., Choi, S.: A structure-based drug discovery paradigm. Int. J. Mol. Sci. 20 (2019)","DOI":"10.3390\/ijms20112783"},{"key":"29_CR15","doi-asserted-by":"crossref","unstructured":"Acharya, C., Coop, A., E Polli, J., D MacKerell, A.: Recent advances in ligand-based drug design: relevance and utility of the conformationally sampled pharmacophore approach. Curr. Comput.-Aided Drug Des. 7, 10 (2011)","DOI":"10.2174\/157340911793743547"},{"issue":"2","key":"29_CR16","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1038\/s41573-023-00832-0","volume":"23","author":"A Tropsha","year":"2023","unstructured":"Tropsha, A., Isayev, O., Varnek, A., Schneider, G., Cherkasov, A.: Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR. Nat. Rev. Drug Discov. 23(2), 141\u2013155 (2023)","journal-title":"Nat. Rev. Drug Discov."},{"key":"29_CR17","doi-asserted-by":"crossref","unstructured":"Wang, S., Guo, Y., Wang, Y., Sun, H., Huang, J.: Smiles-BERT: large scale unsupervised pre-training for molecular property prediction. In: ACM-BCB 2019 - Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, pp. 429\u2013436. Association for Computing Machinery, Inc (2019)","DOI":"10.1145\/3307339.3342186"},{"key":"29_CR18","doi-asserted-by":"crossref","unstructured":"Zhang, S., Fan, R., Liu, Y., Chen, S., Liu, Q., Zeng, W.: Applications of transformer-based language models in bioinformatics: a survey. Bioinf. Adv. 3 (2023)","DOI":"10.1093\/bioadv\/vbad001"},{"key":"29_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/bib\/bbab291","volume":"22","author":"B Zagidullin","year":"2021","unstructured":"Zagidullin, B., Wang, Z., Guan, Y., Pitk\u00e4nen, E., Tang, J.: Comparative analysis of molecular fingerprints in prediction of drug combination effects. Brief. Bioinform. 22, 1\u201315 (2021)","journal-title":"Brief. Bioinform."},{"key":"29_CR20","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)","journal-title":"Methods"},{"key":"29_CR21","doi-asserted-by":"publisher","first-page":"863","DOI":"10.3390\/brainsci12070863","volume":"12","author":"K Yamazaki","year":"2022","unstructured":"Yamazaki, K., Vo-Ho, V.K., Bulsara, D., Le, N.: Spiking neural networks and their applications: a review. Brain Sci. 12, 863 (2022)","journal-title":"Brain Sci."},{"key":"29_CR22","doi-asserted-by":"crossref","unstructured":"Kiselev, M.: Rate coding vs. temporal coding - is optimum between? In: Proceedings of the International Joint Conference on Neural Networks, pp. 1355\u20131359 (2016)","DOI":"10.1109\/IJCNN.2016.7727355"},{"issue":"1","key":"29_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-017-07418-y","volume":"7","author":"S Dutta","year":"2017","unstructured":"Dutta, S., Kumar, V., Shukla, A., Mohapatra, N.R., Ganguly, U.: Leaky integrate and fire neuron by charge-discharge dynamics in floating-body MOSFET. Sci. Rep. 7(1), 1\u20137 (2017)","journal-title":"Sci. Rep."},{"key":"29_CR24","doi-asserted-by":"publisher","first-page":"149773","DOI":"10.3389\/fncom.2015.00099","volume":"9","author":"PU Diehl","year":"2015","unstructured":"Diehl, P.U., Cook, M.: Unsupervised learning of digit recognition using spike-timing-dependent plasticity. Front. Comput. Neurosci. 9, 149773 (2015)","journal-title":"Front. Comput. Neurosci."},{"key":"29_CR25","doi-asserted-by":"crossref","unstructured":"Lee, J. H., Delbruck, T., Pfeiffer, M.: Training deep spiking neural networks using backpropagation. Front. Neurosci. 10, 228000 (2016)","DOI":"10.3389\/fnins.2016.00508"},{"key":"29_CR26","doi-asserted-by":"crossref","unstructured":"Kim, S., Park, S., Na, B., Yoon, S.: Spiking-YOLO: spiking neural network for energy-efficient object detection. In: AAAI 2020 - 34th AAAI Conference on Artificial Intelligence, pp. 11270\u201311277. AAAI press (2020)","DOI":"10.1609\/aaai.v34i07.6787"},{"key":"29_CR27","doi-asserted-by":"publisher","first-page":"1342","DOI":"10.3390\/molecules28031342","volume":"28","author":"M Nascimben","year":"2023","unstructured":"Nascimben, M., Rimondini, L.: Molecular toxicity virtual screening applying a quantized computational SNN-based framework. Molecules 28, 1342 (2023)","journal-title":"Molecules"},{"key":"29_CR28","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, 742\u2013754 (2010)","journal-title":"J. Chem. Inf. Model."},{"key":"29_CR29","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, 1273\u20131280 (2002)","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"29_CR30","unstructured":"RDKit. RDKit: open-source cheminformatics software (2020). Accessed 13 Jan 2025"},{"key":"29_CR31","doi-asserted-by":"crossref","unstructured":"Xie, L., Xu, L., Kong, R., Chang, S., Xu, X.: Improvement of prediction performance with conjoint molecular fingerprint in deep learning. Front. Pharmacol. 11 (2020)","DOI":"10.3389\/fphar.2020.606668"},{"key":"29_CR32","unstructured":"Zhenqin, W., et al.: MoleculeNet: a benchmark for molecular machine learning (2018)"},{"key":"29_CR33","doi-asserted-by":"crossref","unstructured":"Rainio, O., Teuho, J., Kl\u00e9n, R.: Evaluation metrics and statistical tests for machine learning. Sci. Rep. 14 (2024)","DOI":"10.1038\/s41598-024-56706-x"}],"container-title":["Lecture Notes in Computer Science","Progress in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-05176-9_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T13:40:52Z","timestamp":1757943652000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05176-9_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,15]]},"ISBN":["9783032051752","9783032051769"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05176-9_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,15]]},"assertion":[{"value":"15 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare that they have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"EPIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"EPIA Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Faro","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"epia2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/epia2025.ualg.pt\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}