{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T18:19:54Z","timestamp":1776363594234,"version":"3.51.2"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032184764","type":"print"},{"value":"9783032184771","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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\/978-3-032-18477-1_25","type":"book-chapter","created":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T17:10:15Z","timestamp":1776359415000},"page":"239-249","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["EmSVR-BACE: LLM-Based Molecular Embedding for\u00a0Predicting BACE1 Inhibitors in\u00a0Alzheimer\u2019s Therapy"],"prefix":"10.1007","author":[{"given":"Dhamodharan","family":"Ganesan","sequence":"first","affiliation":[]},{"given":"Pratiti","family":"Bhadra","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,4,17]]},"reference":[{"key":"25_CR1","unstructured":"World\u00a0Health Organization. Risk reduction of cognitive decline and dementia: Who guidelines (2019)"},{"issue":"24","key":"25_CR2","doi-asserted-by":"publisher","first-page":"5789","DOI":"10.3390\/molecules25245789","volume":"25","author":"Z Breijyeh","year":"2020","unstructured":"Breijyeh, Z., Karaman, R.: Comprehensive review on Alzheimer\u2019s disease: causes and treatment. Molecules 25(24), 5789 (2020)","journal-title":"Molecules"},{"key":"25_CR3","doi-asserted-by":"crossref","unstructured":"Davis, D., et al.: Key chemotypes for the rational design of dual AChE\/BACE-1 inhibitors. Curr. Med. Chem. (2025)","DOI":"10.2174\/0109298673350086250310080327"},{"issue":"2","key":"25_CR4","doi-asserted-by":"publisher","DOI":"10.1002\/trc2.12385","volume":"9","author":"J Cummings","year":"2023","unstructured":"Cummings, J., Zhou, Y., Lee, G., Zhong, K., Fonseca, J., Cheng, F.: Alzheimer\u2019s disease drug development pipeline: 2023. Alzheimer\u2019s Dement. 9(2), e12385 (2023)","journal-title":"Alzheimer\u2019s Dement."},{"issue":"24","key":"25_CR5","doi-asserted-by":"publisher","first-page":"8823","DOI":"10.3390\/molecules27248823","volume":"27","author":"FH Bazzari","year":"2022","unstructured":"Bazzari, F.H., Bazzari, A.H.: Bace1 inhibitors for Alzheimer\u2019s disease: the past, present and any future? Molecules 27(24), 8823 (2022)","journal-title":"Molecules"},{"issue":"s1","key":"25_CR6","doi-asserted-by":"publisher","first-page":"S41","DOI":"10.3233\/JAD-231258","volume":"101","author":"EA Watkins","year":"2024","unstructured":"Watkins, E.A., Vassar, R.: BACE inhibitor clinical trials for Alzheimer\u2019s disease. J. Alzheimers Dis. 101(s1), S41\u2013S52 (2024)","journal-title":"J. Alzheimers Dis."},{"key":"25_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.chphi.2024.100754","volume":"9","author":"RD Jawarkar","year":"2024","unstructured":"Jawarkar, R.D., et al.: Cheminformatics-driven prediction of BACE-1 inhibitors: affinity and molecular mechanism exploration. Chem. Phys. Impact 9, 100754 (2024)","journal-title":"Chem. Phys. Impact"},{"key":"25_CR8","doi-asserted-by":"publisher","DOI":"10.3389\/fnagi.2022.878276","volume":"14","author":"N Mukerjee","year":"2022","unstructured":"Mukerjee, N., et al.: Repurposing food molecules as a potential BACE1 inhibitor for Alzheimer\u2019s disease. Front. Aging Neurosci. 14, 878276 (2022)","journal-title":"Front. Aging Neurosci."},{"key":"25_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiolchem.2025.108371","volume":"116","author":"N Kaur","year":"2025","unstructured":"Kaur, N., Gupta, S., Pal, J., Bansal, Y., Bansal, G.: Design of BBB permeable BACE-1 inhibitor as potential drug candidate for Alzheimer disease: 2D-QSAR, molecular docking, ADMET, molecular dynamics, MMGBSA. Comput. Biol. Chem. 116, 108371 (2025)","journal-title":"Comput. Biol. Chem."},{"issue":"1","key":"25_CR10","doi-asserted-by":"publisher","first-page":"9102","DOI":"10.1038\/s41598-019-45522-3","volume":"9","author":"I Ponzoni","year":"2019","unstructured":"Ponzoni, I., et al.: QSAR classification models for predicting the activity of inhibitors of beta-secretase (BACE1) associated with Alzheimer\u2019s disease. Sci. Rep. 9(1), 9102 (2019)","journal-title":"Sci. Rep."},{"issue":"10","key":"25_CR11","doi-asserted-by":"publisher","first-page":"1415","DOI":"10.1142\/S2737416525500309","volume":"24","author":"TJ Sindhu","year":"2025","unstructured":"Sindhu, T.J., James, J.P., Fathima, C.Z., Mathew, B., Kumar, S.: Mechanistic insights into Thiazolidinones as anticholinesterase agents: 3D QSAR pharmacophore modeling, molecular docking, MD simulations, and DFT studies for Alzheimer\u2019s therapy. J. Comput. Biophys. Chem. 24(10), 1415\u20131440 (2025)","journal-title":"J. Comput. Biophys. Chem."},{"key":"25_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemolab.2023.105049","volume":"245","author":"V Kumar","year":"2024","unstructured":"Kumar, V., Banerjee, A., Roy, K.: Machine learning-based q-RASAR approach for the in silico identification of novel multi-target inhibitors against Alzheimer\u2019s disease. Chemom. Intell. Lab. Syst. 245, 105049 (2024)","journal-title":"Chemom. Intell. Lab. Syst."},{"issue":"12","key":"25_CR13","doi-asserted-by":"publisher","first-page":"1109","DOI":"10.1080\/1062936X.2024.2440903","volume":"35","author":"NT Hang","year":"2024","unstructured":"Hang, N.T., et al.: Enhanced prediction of beta-secretase inhibitory compounds with mol2vec technique and machine learning algorithms. SAR QSAR Environ. Res. 35(12), 1109\u20131127 (2024)","journal-title":"SAR QSAR Environ. Res."},{"issue":"3","key":"25_CR14","doi-asserted-by":"publisher","first-page":"1501","DOI":"10.1007\/s11030-021-10282-8","volume":"26","author":"G Dhamodharan","year":"2022","unstructured":"Dhamodharan, G., Mohan, C.G.: Machine learning models for predicting the activity of ACHE and BACE1 dual inhibitors for the treatment of Alzheimer\u2019s disease. Mol. Divers. 26(3), 1501\u20131517 (2022)","journal-title":"Mol. Divers."},{"key":"25_CR15","doi-asserted-by":"crossref","unstructured":"Sangeet, S.: Machine learning-enhanced drug discovery for BACE1: a novel approach to Alzheimer\u2019s therapeutics. bioRxiv (2024)","DOI":"10.1101\/2024.09.24.614844"},{"issue":"16","key":"25_CR16","doi-asserted-by":"publisher","first-page":"3644","DOI":"10.3390\/molecules25163644","volume":"25","author":"T-S Tran","year":"2020","unstructured":"Tran, T.-S., Le, M.-T., Tran, T.-D., Tran, T.-H., Thai, K.-M.: Design of curcumin and flavonoid derivatives with acetylcholinesterase and beta-secretase inhibitory activities using in silico approaches. Molecules 25(16), 3644 (2020)","journal-title":"Molecules"},{"issue":"1","key":"25_CR17","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1021\/acs.jcim.7b00616","volume":"58","author":"S Jaeger","year":"2018","unstructured":"Jaeger, S., Fulle, S., Turk, S.: Mol2vec: unsupervised machine learning approach with chemical intuition. J. Chem. Inf. Model. 58(1), 27\u201335 (2018)","journal-title":"J. Chem. Inf. Model."},{"issue":"4","key":"25_CR18","doi-asserted-by":"publisher","first-page":"1681","DOI":"10.3390\/ijms26041681","volume":"26","author":"S Zheng","year":"2025","unstructured":"Zheng, S., Zhang, C., Chen, Y., Chen, M.: Graph and multi-level sequence fusion learning for predicting the molecular activity of BACE-1 inhibitors. Int. J. Mol. Sci. 26(4), 1681 (2025)","journal-title":"Int. J. Mol. Sci."},{"key":"25_CR19","doi-asserted-by":"crossref","unstructured":"Chakraborty, C., Bhattacharya, M., Pal, S., Chatterjee, S., Das, A., Lee, S.S.: AI-enabled language models (LMs) to large language models (LLMS) and multimodal large language models (MLLMs) in drug discovery and development. J. Adv. Res. (2025)","DOI":"10.1016\/j.jare.2025.02.011"},{"issue":"1","key":"25_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-025-00963-z","volume":"17","author":"B Zdrazil","year":"2025","unstructured":"Zdrazil, B.: Fifteen years of ChEMBL and its role in cheminformatics and drug discovery. J. Cheminform. 17(1), 1\u20139 (2025)","journal-title":"J. Cheminform."},{"issue":"3","key":"25_CR21","doi-asserted-by":"publisher","DOI":"10.1002\/cbf.4007","volume":"42","author":"L Khalef","year":"2024","unstructured":"Khalef, L., Lydia, R., Filicia, K., Moussa, B.: Cell viability and cytotoxicity assays: biochemical elements and cellular compartments. Cell Biochem. Funct. 42(3), e4007 (2024)","journal-title":"Cell Biochem. Funct."},{"issue":"3","key":"25_CR22","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1007\/s12293-024-00414-6","volume":"16","author":"TH Nguyen-Vo","year":"2024","unstructured":"Nguyen-Vo, T.H., Teesdale-Spittle, P., Harvey, J.E., Nguyen, B.P.: Molecular representations in bio-cheminformatics. Memetic Comput. 16(3), 519\u2013536 (2024)","journal-title":"Memetic Comput."},{"issue":"6","key":"25_CR23","doi-asserted-by":"publisher","first-page":"1800082","DOI":"10.1002\/minf.201800082","volume":"38","author":"M Lovri\u0107","year":"2019","unstructured":"Lovri\u0107, M., Molero, J.M., Kern, R.: PySpark and RDKit: moving towards big data in cheminformatics. Mol. Inf. 38(6), 1800082 (2019)","journal-title":"Mol. Inf."},{"issue":"6","key":"25_CR24","doi-asserted-by":"publisher","first-page":"5669","DOI":"10.1007\/s11063-022-10879-6","volume":"54","author":"S Hadiby","year":"2022","unstructured":"Hadiby, S., Ali, Y.M.B.: Deep learning based-virtual screening using 2D pharmacophore fingerprint in drug discovery. Neural Process. Lett. 54(6), 5669\u20135691 (2022)","journal-title":"Neural Process. Lett."},{"issue":"5","key":"25_CR25","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)","journal-title":"J. Chem. Inf. Model."},{"key":"25_CR26","doi-asserted-by":"crossref","unstructured":"Kenneth Ward Church: Word2vec. Nat. Lang. Eng. 23(1), 155\u2013162 (2017)","DOI":"10.1017\/S1351324916000334"},{"key":"25_CR27","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL-HLT. Association for Computational Linguistics (2019)"},{"key":"25_CR28","unstructured":"Ahmad, W., Simon, E., Chithrananda, S., Grand, G., Ramsundar, B.: Chemberta-2: towards chemical foundation models. arXiv preprint arXiv:2209.01712 (2022)"},{"issue":"8","key":"25_CR29","doi-asserted-by":"publisher","first-page":"2489","DOI":"10.1007\/s00429-021-02341-5","volume":"226","author":"V Kumar","year":"2021","unstructured":"Kumar, V., et al.: B3PDB: an archive of blood-brain barrier-penetrating peptides. Brain Struct. Funct. 226(8), 2489\u20132495 (2021)","journal-title":"Brain Struct. Funct."},{"issue":"19","key":"25_CR30","doi-asserted-by":"publisher","first-page":"6765","DOI":"10.1039\/C3CS60460H","volume":"43","author":"AK Ghosh","year":"2014","unstructured":"Ghosh, A.K., Osswald, H.L.: BACE1 ($$\\beta $$-secretase) inhibitors for the treatment of Alzheimer\u2019s disease. Chem. Soc. Rev. 43(19), 6765\u20136813 (2014)","journal-title":"Chem. Soc. Rev."}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Machine Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-18477-1_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T17:10:18Z","timestamp":1776359418000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-18477-1_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032184764","9783032184771"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-18477-1_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"17 April 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PReMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition and Machine Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Delhi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","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":"11 December 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 December 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"premi2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/premi25-git-dev-ashirbad97s-projects.vercel.app\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}