{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T23:32:58Z","timestamp":1777678378278,"version":"3.51.4"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031976315","type":"print"},{"value":"9783031976322","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-97632-2_12","type":"book-chapter","created":{"date-parts":[[2025,7,4]],"date-time":"2025-07-04T00:42:13Z","timestamp":1751589733000},"page":"167-182","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Detecting Potential HIV Inhibitors Using the\u00a0Cross Siamese Network"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-2916-8672","authenticated-orcid":false,"given":"Konrad","family":"Witkowski","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3047-6662","authenticated-orcid":false,"given":"Agnieszka","family":"Duraj","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9973-0673","authenticated-orcid":false,"given":"Piotr S.","family":"Szczepaniak","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,5]]},"reference":[{"key":"12_CR1","unstructured":"Accelrys: Mdl drug data report (mddr). http:\/\/www.accelrys.com, accelrys Inc.: San Diego, CA, USA. Accessed 31 Oct 2021"},{"key":"12_CR2","doi-asserted-by":"publisher","first-page":"6669","DOI":"10.3390\/molecules26216669","volume":"26","author":"MK Altalib","year":"2021","unstructured":"Altalib, M.K., Salim, N.: Similarity-based virtual screen using enhanced siamese multi-layer perceptron. Molecules 26, 6669 (2021). https:\/\/doi.org\/10.3390\/molecules26216669","journal-title":"Molecules"},{"issue":"11","key":"12_CR3","doi-asserted-by":"publisher","first-page":"1719","DOI":"10.3390\/biom12111719","volume":"12","author":"MK Altalib","year":"2022","unstructured":"Altalib, M.K., Salim, N.: Hybrid-enhanced siamese similarity models in ligand-based virtual screen. Biomolecules 12(11), 1719 (2022). https:\/\/doi.org\/10.3390\/biom12111719","journal-title":"Biomolecules"},{"key":"12_CR4","doi-asserted-by":"publisher","unstructured":"Balntas, V., Riba, E., Ponsa, D., Mikolajczyk, K.: Learning local feature descriptors with triplets and shallow convolutional neural networks. BMVC Proceedings pp. 119.1\u2013119.11 (2016). https:\/\/doi.org\/10.5244\/C.30.119","DOI":"10.5244\/C.30.119"},{"key":"12_CR5","unstructured":"Bengio, Y., Glorot, X.: Understanding the difficulty of training deep feed forward neural networks. In: International Conference on Artificial Intelligence and Statistics pp. 249\u2013256 (2010)"},{"key":"12_CR6","unstructured":"Bromley, J., Guyon, I., LeCun, Y., Sickinger, E., Shah, R.: Signature verification using a \"siamese\" time delay neural network. In: Advances in Neural Information Processing Systems. vol.\u00a06, pp. 737\u2013744 (1993)"},{"key":"12_CR7","unstructured":"Chemdbl: Chembl3301361. https:\/\/www.ebi.ac.uk\/chembl\/document_report_card\/CHEMBL3301361\/ Accessed 30 Sept 2024"},{"issue":"3","key":"12_CR8","doi-asserted-by":"publisher","first-page":"1000","DOI":"10.1021\/ci034243x","volume":"44","author":"JS Delaney","year":"2004","unstructured":"Delaney, J.S.: Esol: estimating aqueous solubility directly from molecular structure. J. Chem. Inf. Comput. Sci. 44(3), 1000\u20131005 (2004). https:\/\/doi.org\/10.1021\/ci034243x","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"12_CR9","unstructured":"Google: classification: accuracy, recall, precision, and related metrics. https:\/\/developers.google.com\/machine-learning\/crash-course\/classification\/accuracy-precision-recall Accessed 5 Dec 2024"},{"issue":"47","key":"12_CR10","doi-asserted-by":"publisher","first-page":"44757","DOI":"10.1021\/acsomega.3c05778","volume":"8","author":"MP Heyrati","year":"2023","unstructured":"Heyrati, M.P., Ghorbanali, Z., Akbari, M., Pishgahi, G., Zare-Mirakabad, F.: Bioact-het: a heterogeneous siamese neural network for bioactivity prediction using novel bioactivity representation. ACS Omega 8(47), 44757\u201344772 (2023). https:\/\/doi.org\/10.1021\/acsomega.3c05778","journal-title":"ACS Omega"},{"key":"12_CR11","unstructured":"HIV.gov: What are hiv and aids. https:\/\/www.hiv.gov\/hiv-basics\/overview\/about-hiv-and-aids\/what-are-hiv-and-aids Accessed 26 Sept 2024"},{"key":"12_CR12","unstructured":"Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (2014)"},{"key":"12_CR13","doi-asserted-by":"publisher","first-page":"D1075","DOI":"10.1093\/NAR\/GKV1075","volume":"44","author":"M Kuhn","year":"2016","unstructured":"Kuhn, M., Letunic, I., Jensen, L.J., Bork, P.: The sider database of drugs and side effects. Nucleic Acids Res. 44, D1075\u2013D1079 (2016). https:\/\/doi.org\/10.1093\/NAR\/GKV1075","journal-title":"Nucleic Acids Res."},{"issue":"41","key":"12_CR14","doi-asserted-by":"publisher","first-page":"27233","DOI":"10.1021\/acsomega.1c04017","volume":"6","author":"M Li","year":"2021","unstructured":"Li, M., Zhou, J., Hu, J., Fan, W., Zhang, Y., Gu, Y., Karypis, G.: Dgl-lifesci: an open-source toolkit for deep learning on graphs in life science. ACS Omega 6(41), 27233\u201327238 (2021). https:\/\/doi.org\/10.1021\/acsomega.1c04017","journal-title":"ACS Omega"},{"key":"12_CR15","doi-asserted-by":"publisher","unstructured":"Li, T.H., Wang, C.C., Zhang, L., Chen, X.: Snrmpacdc: computational model focused on siamese network and random matrix projection for anticancer synergistic drug combination prediction. Briefings Bioinform. 24(1), bbac503 (2023). https:\/\/doi.org\/10.1093\/bib\/bbac503","DOI":"10.1093\/bib\/bbac503"},{"issue":"3","key":"12_CR16","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1109\/TNN.2008.2010350","volume":"20","author":"A Micheli","year":"2009","unstructured":"Micheli, A.: Neural network for graphs: a contextual constructive approach. IEEE Trans. Neural Netw. 20(3), 498\u2013511 (2009). https:\/\/doi.org\/10.1109\/TNN.2008.2010350","journal-title":"IEEE Trans. Neural Netw."},{"issue":"7","key":"12_CR17","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1007\/s10822-014-9747-x","volume":"28","author":"DL Mobley","year":"2014","unstructured":"Mobley, D.L., Guthrie, J.P.: FreeSolv: a database of experimental and calculated hydration free energies, with input files. J. Comput. Aided Mol. Des. 28(7), 711\u2013720 (2014). https:\/\/doi.org\/10.1007\/s10822-014-9747-x","journal-title":"J. Comput. Aided Mol. Des."},{"issue":"2","key":"12_CR18","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)","journal-title":"J. Chem. Doc."},{"key":"12_CR19","unstructured":"National center for advancing translational studies: Tox21 data challenge (2014). https:\/\/tripod.nih.gov\/tox21\/challenge\/data.jsp Accessed 1 Oct 2024"},{"key":"12_CR20","doi-asserted-by":"publisher","first-page":"1155","DOI":"10.1158\/1535-7163.MCT-15-0843","volume":"15","author":"J O\u2019Neil","year":"2016","unstructured":"O\u2019Neil, J., et al.: An unbiased oncology compound screen to identify novel combination strategies. Mol. Cancer Ther. 15, 1155\u20131162 (2016)","journal-title":"Mol. Cancer Ther."},{"issue":"2","key":"12_CR21","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1021\/ci8002649","volume":"49","author":"SG Rohrer","year":"2009","unstructured":"Rohrer, S.G., Baumann, K.: Maximum unbiased validation (muv) data sets for virtual screening based on pubchem bioactivity data. J. Chem. Inf. Model. 49(2), 169\u2013184 (2009). https:\/\/doi.org\/10.1021\/ci8002649","journal-title":"J. Chem. Inf. Model."},{"key":"12_CR22","doi-asserted-by":"publisher","unstructured":"Shi, Y., Yang, K., Yang, Z., Zhou, Y.: Chapter two - primer on artificial intelligence. In: Shi, Y., Yang, K., Yang, Z., Zhou, Y. (eds.) Mobile Edge Artificial Intelligence, pp. 7\u201336. Academic Press (2022). https:\/\/doi.org\/10.1016\/B978-0-12-823817-2.00011-5","DOI":"10.1016\/B978-0-12-823817-2.00011-5"},{"key":"12_CR23","unstructured":"The DeepChem Project: Model classes. https:\/\/deepchem.readthedocs.io\/en\/latest\/api_reference\/models.html Accessed 21 Mar 2024"},{"key":"12_CR24","unstructured":"The deepchem project: moleculenet. https:\/\/deepchem.readthedocs.io\/en\/latest\/api_reference\/moleculenet.html Accessed 29 Sept 2024"},{"key":"12_CR25","unstructured":"WHO: Hiv and aids. https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/hiv-aids Accessed 26 Sept 2024"},{"issue":"2","key":"12_CR26","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1039\/c7sc02664a","volume":"9","author":"Z Wu","year":"2017","unstructured":"Wu, Z., Ramsundar, B., Feinberg, E.N., Gomes, J., Geniesse, C., Pappu, A.S., Leswing, K., Pande, V.: Moleculenet: a benchmark for molecular machine learning. Chem. Sci. 9(2), 513\u2013530 (2017). https:\/\/doi.org\/10.1039\/c7sc02664a","journal-title":"Chem. Sci."},{"key":"12_CR27","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1186\/s13321-023-00744-6","volume":"15","author":"Y Zhang","year":"2023","unstructured":"Zhang, Y., Menke, J., He, J., Nittinger, E., Tyrchan, C., Koch, O., Zhao, H.: Similarity-based pairing improves efficiency of siamese neural networks for regression tasks and uncertainty quantification. J. Cheminform. 15, 75 (2023). https:\/\/doi.org\/10.1186\/s13321-023-00744-6","journal-title":"J. Cheminform."}],"container-title":["Lecture Notes in Computer Science","Computational Science \u2013 ICCS 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-97632-2_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T08:27:17Z","timestamp":1777451237000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-97632-2_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031976315","9783031976322"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-97632-2_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"5 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","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":"7 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccs-computsci2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iccs-meeting.org\/iccs2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}