{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T23:08:02Z","timestamp":1770332882865,"version":"3.49.0"},"posted":{"date-parts":[[2026,1,26]]},"group-title":"Bioengineering","reference-count":68,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T00:00:00Z","timestamp":1769385600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,1,26]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                <jats:p>Resistance to androgen receptor inhibitors remains a primary challenge in prostate cancer treatment, yet identifying synergis-tic co-therapies is hindered by immense combinatorial search spaces and the limited interpretability of predictive computation models. Here, we developed an integrated discovery-validation axis coupling knowledge-augmented large language models with oxygen-supplemented 3D spheroid assays. By leveraging inherent model stochasticity, our framework measures the degree of consensus across independent predictions to establish a formal metric for predictive accuracy. This principle enables high-throughput assessment of complex signaling crosstalk, yielding mechanistic rationales for all predictions and defining a high-confidence zone that minimizes experimental attrition. Utilizing this approach to screen 3,592 natural products, we identified a previously unrecognized synergy between berberine and enzalutamide that re-sensitizes resistant cells. Validation confirms that berberine perturbs the PI3K\/AKT\/mTOR and AMPK axes, a finding consistent with the mechanistic rationales computationally derived by the framework. Integrating interpretable AI with physiologically relevant 3D screening provides a scalable methodology for the rational discovery of synergistic therapies.<\/jats:p>\n                <jats:sec>\n                  <jats:title>Significance<\/jats:title>\n                  <jats:p>Integrating mechanistic AI with oxygenated 3D screening, we identify a novel berberine-enzalutamide synergy. This framework resolves complex signaling dependencies, providing a scalable, transparent methodology for the rational discovery of effective combination therapies.<\/jats:p>\n                <\/jats:sec>","DOI":"10.64898\/2026.01.24.701539","type":"posted-content","created":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T05:35:17Z","timestamp":1769492117000},"source":"Crossref","is-referenced-by-count":0,"title":["Mechanistic Language Modeling and Oxygenated 3D Screening Reveal Berberine and Enzalutamide Synergy in Resistant Prostate Cancer"],"prefix":"10.64898","author":[{"given":"Chih-Hui","family":"Lo","sequence":"first","affiliation":[{"name":"Bioengineering Department, University of California Los Angeles, Los Angeles, CA, USA"},{"name":"College of Pharmacy, National Defense Medical University, Taipei, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Katie","family":"Shi","sequence":"additional","affiliation":[{"name":"Bioengineering Department, University of California Los Angeles, Los Angeles, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lina","family":"Kafadarian","sequence":"additional","affiliation":[{"name":"Bioengineering Department, University of California Los Angeles, Los Angeles, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexandra","family":"Bermudez","sequence":"additional","affiliation":[{"name":"Bioengineering Department, University of California Los Angeles, Los Angeles, CA, USA"},{"name":"Mechanical and Aerospace Engineering Department, University of California Los Angeles, Los Angeles, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Johnny","family":"Diaz","sequence":"additional","affiliation":[{"name":"Department of Molecular, Cell, and Developmental Biology, University of California Los Angeles, Los Angeles, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liam","family":"Edwards","sequence":"additional","affiliation":[{"name":"Mechanical and Aerospace Engineering Department, University of California Los Angeles, Los Angeles, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunqi","family":"Hong","sequence":"additional","affiliation":[{"name":"Computer Science Department, University of California Los Angeles, Los Angeles, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziyi","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Molecular and Medical Pharmacology, University of California Los Angeles, Los Angeles, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hyeonji","family":"Hwang","sequence":"additional","affiliation":[{"name":"Department of Molecular and Medical Pharmacology, University of California Los Angeles, Los Angeles, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weihong","family":"Yan","sequence":"additional","affiliation":[{"name":"Department of Chemistry and Biochemistry, University of California, Los Angeles, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alan","family":"Levinson","sequence":"additional","affiliation":[{"name":"Bioengineering Department, University of California Los Angeles, Los Angeles, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert","family":"Damoiseaux","sequence":"additional","affiliation":[{"name":"Bioengineering Department, University of California Los Angeles, Los Angeles, CA, USA"},{"name":"Department of Molecular and Medical Pharmacology, University of California Los Angeles, Los Angeles, CA, USA"},{"name":"California NanoSystems Institute, University of California Los Angeles, Los Angeles, CA, USA"},{"name":"Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California Los Angeles, Los Angeles, CA, USA"},{"name":"Jonsson Comprehensive Cancer Center, University of California Los Angeles, Los Angeles, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cho-Jui","family":"Hsieh","sequence":"additional","affiliation":[{"name":"Computer Science Department, University of California Los Angeles, Los Angeles, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tanya","family":"Stoyanova","sequence":"additional","affiliation":[{"name":"Department of Molecular and Medical Pharmacology, University of California Los Angeles, Los Angeles, CA, USA"},{"name":"Jonsson Comprehensive Cancer Center, University of California Los Angeles, Los Angeles, CA, USA"},{"name":"Department of Urology, University of California Los Angeles, Los Angeles, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrew S.","family":"Goldstein","sequence":"additional","affiliation":[{"name":"Department of Molecular, Cell, and Developmental Biology, University of California Los Angeles, Los Angeles, CA, USA"},{"name":"Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California Los Angeles, Los Angeles, CA, USA"},{"name":"Jonsson Comprehensive Cancer Center, University of California Los Angeles, Los Angeles, CA, USA"},{"name":"Department of Urology, University of California Los Angeles, Los Angeles, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Neil Y.C.","family":"Lin","sequence":"additional","affiliation":[{"name":"Bioengineering Department, University of California Los Angeles, Los Angeles, CA, USA"},{"name":"Mechanical and Aerospace Engineering Department, University of California Los Angeles, Los Angeles, CA, USA"},{"name":"California NanoSystems Institute, University of California Los Angeles, Los Angeles, CA, USA"},{"name":"Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California Los Angeles, Los Angeles, CA, USA"},{"name":"Jonsson Comprehensive Cancer Center, University of California Los Angeles, Los Angeles, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2026020502421800000_2026.01.24.701539v1.1","doi-asserted-by":"publisher","DOI":"10.3322\/CAAC.21834"},{"key":"2026020502421800000_2026.01.24.701539v1.2","doi-asserted-by":"publisher","DOI":"10.1038\/ncpuro1296"},{"issue":"11","key":"2026020502421800000_2026.01.24.701539v1.3","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.1080\/14737140.2024.2405103","article-title":"Current uses and resistance mechanisms of enzalutamide in prostate cancer treatment","volume":"24","year":"2024","journal-title":"Expert Review of Anticancer Therapy"},{"key":"2026020502421800000_2026.01.24.701539v1.4","doi-asserted-by":"crossref","unstructured":"Vander Ark A , Cao J , Li X . Mechanisms and approaches for overcoming enzalutamide resistance in prostate cancer. Frontiers in oncology. 2018;8:180.","DOI":"10.3389\/fonc.2018.00180"},{"key":"2026020502421800000_2026.01.24.701539v1.5","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1207506"},{"key":"2026020502421800000_2026.01.24.701539v1.6","doi-asserted-by":"publisher","DOI":"10.1016\/j.ccr.2011.04.008"},{"issue":"2","key":"2026020502421800000_2026.01.24.701539v1.7","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.bbcan.2018.09.001","article-title":"PI3K pathway in prostate cancer: All resistant roads lead to PI3K","volume":"1870","year":"2018","journal-title":"Biochimica et Biophysica Acta (BBA)-Reviews on Cancer"},{"issue":"1","key":"2026020502421800000_2026.01.24.701539v1.8","doi-asserted-by":"crossref","first-page":"R31","DOI":"10.1530\/ERC-18-0289","article-title":"Enzalutamide therapy for advanced prostate cancer: efficacy, resistance and beyond","volume":"26","year":"2019","journal-title":"Endocrine-related cancer"},{"key":"2026020502421800000_2026.01.24.701539v1.9","doi-asserted-by":"crossref","unstructured":"Hiltunen J , Helminen L , Paakinaho V . Glucocorticoid receptor action in prostate cancer: the role of transcription factor crosstalk. Frontiers in Endocrinology. 2024;15:1437179.","DOI":"10.3389\/fendo.2024.1437179"},{"key":"2026020502421800000_2026.01.24.701539v1.10","doi-asserted-by":"publisher","DOI":"10.1002\/bies.20676"},{"key":"2026020502421800000_2026.01.24.701539v1.11","doi-asserted-by":"publisher","DOI":"10.1038\/nrc4016"},{"key":"2026020502421800000_2026.01.24.701539v1.12","doi-asserted-by":"publisher","DOI":"10.1530\/ERC-12-0394"},{"issue":"1","key":"2026020502421800000_2026.01.24.701539v1.13","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1038\/s41392-022-01042-7","article-title":"Targeting signaling pathways in prostate cancer: mechanisms and clinical trials","volume":"7","year":"2022","journal-title":"Signal transduction and targeted therapy"},{"key":"2026020502421800000_2026.01.24.701539v1.14","doi-asserted-by":"publisher","DOI":"10.18632\/oncotarget.4689"},{"key":"2026020502421800000_2026.01.24.701539v1.15","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btx806"},{"key":"2026020502421800000_2026.01.24.701539v1.16","doi-asserted-by":"crossref","unstructured":"Wang J , Liu X , Shen S , Deng L , Liu H . DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations. Briefings in Bioinformatics. 2022;23(1).","DOI":"10.1093\/bib\/bbab390"},{"issue":"2","key":"2026020502421800000_2026.01.24.701539v1.17","doi-asserted-by":"crossref","first-page":"e1008653","DOI":"10.1371\/journal.pcbi.1008653","article-title":"TranSynergy: Mechanism-driven interpretable deep neural network for the synergistic prediction and pathway deconvolution of drug combinations","volume":"17","year":"2021","journal-title":"PLoS computational biology"},{"key":"2026020502421800000_2026.01.24.701539v1.18","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocab162"},{"key":"2026020502421800000_2026.01.24.701539v1.19","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2105070118"},{"key":"2026020502421800000_2026.01.24.701539v1.20","doi-asserted-by":"publisher","DOI":"10.1016\/j.ccell.2020.09.014"},{"key":"2026020502421800000_2026.01.24.701539v1.21","doi-asserted-by":"crossref","unstructured":"Rafiei F , Zeraati H , Abbasi K , Ghasemi JB , Parsaeian M , Masoudi-Nejad A . DeepTraSynergy: drug combinations using multimodal deep learning with transformers. Bioinformatics. 2023;39(8):btad438.","DOI":"10.1093\/bioinformatics\/btad438"},{"key":"2026020502421800000_2026.01.24.701539v1.22","doi-asserted-by":"crossref","first-page":"1630781","DOI":"10.3389\/fimmu.2025.1630781","article-title":"Correlation does not equal causation: the imperative of causal inference in machine learning models for immunotherapy","volume":"16","year":"2025","journal-title":"Frontiers in Immunology"},{"issue":"1","key":"2026020502421800000_2026.01.24.701539v1.23","doi-asserted-by":"crossref","first-page":"e70124","DOI":"10.1111\/cts.70124","article-title":"Integrating Model-Informed Drug Development With AI: A Synergistic Approach to Accelerating Pharmaceutical Innovation","volume":"18","year":"2025","journal-title":"Clinical and Translational Science"},{"issue":"1","key":"2026020502421800000_2026.01.24.701539v1.24","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1038\/s41746-024-01024-9","article-title":"CancerGPT for few shot drug pair synergy prediction using large pretrained language models","volume":"7","year":"2024","journal-title":"NPJ Digital Medicine"},{"key":"2026020502421800000_2026.01.24.701539v1.25","doi-asserted-by":"crossref","unstructured":"Edwards C , Naik A , Khot T , Burke M , Ji H , Hope T . Synergpt: In-context learning for personalized drug synergy prediction and drug design. arXiv preprint arXiv:230711694. 2023.","DOI":"10.1101\/2023.07.06.547759"},{"key":"2026020502421800000_2026.01.24.701539v1.26","doi-asserted-by":"crossref","first-page":"1589788","DOI":"10.3389\/fphar.2025.1589788","article-title":"Improving drug-drug interaction prediction via in-context learning and judging with large language models","volume":"16","year":"2025","journal-title":"Frontiers in Pharmacology"},{"key":"2026020502421800000_2026.01.24.701539v1.27","doi-asserted-by":"crossref","unstructured":"Perez DM , Kaiser BE , Boureima I . Uncertainty quantification by large language models. Machine Learning with Applications. 2025:100773.","DOI":"10.1016\/j.mlwa.2025.100773"},{"key":"2026020502421800000_2026.01.24.701539v1.28","doi-asserted-by":"crossref","unstructured":"Shorinwa O , Mei Z , Lidard J , Ren AZ , Majumdar A . A survey on uncertainty quantification of large language models: Taxonomy, open research challenges, and future directions. ACM Computing Surveys. 2025.","DOI":"10.1145\/3744238"},{"key":"2026020502421800000_2026.01.24.701539v1.29","doi-asserted-by":"publisher","DOI":"10.1145\/3641289"},{"issue":"1","key":"2026020502421800000_2026.01.24.701539v1.30","first-page":"690","volume":"8","year":"2025","journal-title":"Uncertainty-aware large language models for explainable disease diagnosis. npj Digital Medicine"},{"key":"2026020502421800000_2026.01.24.701539v1.31","doi-asserted-by":"publisher","DOI":"10.1089\/adt.2014.573"},{"key":"2026020502421800000_2026.01.24.701539v1.32","doi-asserted-by":"publisher","DOI":"10.1038\/bjc.1986.58"},{"issue":"1","key":"2026020502421800000_2026.01.24.701539v1.33","doi-asserted-by":"crossref","first-page":"9751","DOI":"10.1038\/s41598-025-92037-1","article-title":"Influence of experimental variables on spheroid attributes","volume":"15","year":"2025","journal-title":"Scientific Reports"},{"issue":"92","key":"2026020502421800000_2026.01.24.701539v1.34","doi-asserted-by":"crossref","first-page":"20131124","DOI":"10.1098\/rsif.2013.1124","article-title":"A method for estimating the oxygen consumption rate in multicellular tumour spheroids","volume":"11","year":"2014","journal-title":"Journal of The Royal Society Interface"},{"issue":"1","key":"2026020502421800000_2026.01.24.701539v1.35","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1186\/s12935-021-01853-8","article-title":"Challenges of applying multicellular tumor spheroids in preclinical phase","volume":"21","year":"2021","journal-title":"Cancer cell international"},{"issue":"6","key":"2026020502421800000_2026.01.24.701539v1.36","doi-asserted-by":"crossref","first-page":"2200434","DOI":"10.1002\/biot.202200434","article-title":"Microwell-based flow culture increases viability and restores drug response in prostate cancer spheroids","volume":"18","year":"2023","journal-title":"Biotechnology Journal"},{"key":"2026020502421800000_2026.01.24.701539v1.37","doi-asserted-by":"publisher","DOI":"10.1002\/jcp.21392"},{"key":"2026020502421800000_2026.01.24.701539v1.38","doi-asserted-by":"crossref","unstructured":"Mansouri M , Leipzig ND . Advances in removing mass transport limitations for more physiologically relevant in vitro 3D cell constructs. Biophysics Reviews. 2021;2(2).","DOI":"10.1063\/5.0048837"},{"key":"2026020502421800000_2026.01.24.701539v1.39","doi-asserted-by":"crossref","unstructured":"Wang Y , Liu Y , Du X , Ma H , Yao J . The anti-cancer mechanisms of berberine: A review. Cancer management and research. 2020:695\u2013702.","DOI":"10.2147\/CMAR.S242329"},{"key":"2026020502421800000_2026.01.24.701539v1.40","doi-asserted-by":"crossref","first-page":"1461","DOI":"10.3389\/fphar.2019.01461","article-title":"Effects of berberine and its derivatives on cancer: A systems pharmacology review","volume":"10","year":"2020","journal-title":"Frontiers in pharmacology"},{"issue":"2","key":"2026020502421800000_2026.01.24.701539v1.41","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1038\/aps.2016.125","article-title":"Advances in the study of berberine and its derivatives: a focus on anti-inflammatory and anti-tumor effects in the digestive system","volume":"38","year":"2017","journal-title":"Acta Pharmacologica Sinica"},{"key":"2026020502421800000_2026.01.24.701539v1.42","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkae235"},{"key":"2026020502421800000_2026.01.24.701539v1.43","doi-asserted-by":"crossref","unstructured":"Reimers N , Gurevych I. Sentence-bert: Sentence embeddings using siamese bert-networks. arXiv preprint arXiv:190810084. 2019.","DOI":"10.18653\/v1\/D19-1410"},{"key":"2026020502421800000_2026.01.24.701539v1.44","doi-asserted-by":"crossref","unstructured":"Friedman JH . Greedy function approximation: a gradient boosting machine. Annals of statistics. 2001:1189\u2013232.","DOI":"10.1214\/aos\/1013203451"},{"key":"2026020502421800000_2026.01.24.701539v1.45","unstructured":"Lundberg SM , Lee SI . A unified approach to interpreting model predictions. Advances in neural information processing systems. 2017;30."},{"key":"2026020502421800000_2026.01.24.701539v1.46","doi-asserted-by":"publisher","DOI":"10.1158\/0008-5472.CAN-14-3080"},{"issue":"1","key":"2026020502421800000_2026.01.24.701539v1.47","first-page":"1","article-title":"QuPath: Open source software for digital pathology image analysis","volume":"7","year":"2017","journal-title":"Scientific reports"},{"key":"2026020502421800000_2026.01.24.701539v1.48","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkz401"},{"issue":"10","key":"2026020502421800000_2026.01.24.701539v1.49","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1038\/s41419-023-06178-0","article-title":"miR-28-based combination therapy impairs aggressive B cell lymphoma growth by rewiring DNA replication","volume":"14","year":"2023","journal-title":"Cell Death & Disease"},{"key":"2026020502421800000_2026.01.24.701539v1.50","doi-asserted-by":"publisher","DOI":"10.1093\/NAR\/GKAD347"},{"key":"2026020502421800000_2026.01.24.701539v1.51","doi-asserted-by":"publisher","DOI":"10.1111\/j.1744-7348.1939.tb06990.x"},{"key":"2026020502421800000_2026.01.24.701539v1.52","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkac382"},{"key":"2026020502421800000_2026.01.24.701539v1.53","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkz337"},{"key":"2026020502421800000_2026.01.24.701539v1.54","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1337088100"},{"key":"2026020502421800000_2026.01.24.701539v1.55","doi-asserted-by":"publisher","DOI":"10.1158\/1535-7163.MCT-15-0843"},{"issue":"6","key":"2026020502421800000_2026.01.24.701539v1.56","first-page":"1153","article-title":"In silico polypharmacology of natural products","volume":"19","year":"2018","journal-title":"Briefings in bioinformatics"},{"key":"2026020502421800000_2026.01.24.701539v1.57","doi-asserted-by":"crossref","unstructured":"Wang S , Li H , Yuan M , Fan H , Cai Z . Role of AMPK in autophagy. Frontiers in physiology. 2022;13:1015500.","DOI":"10.3389\/fphys.2022.1015500"},{"issue":"9","key":"2026020502421800000_2026.01.24.701539v1.58","doi-asserted-by":"crossref","first-page":"e0185089","DOI":"10.1371\/journal.pone.0185089","article-title":"Activation of the unfolded protein response in sarcoma cells treated with rapamycin or temsirolimus","volume":"12","year":"2017","journal-title":"Plos one"},{"key":"2026020502421800000_2026.01.24.701539v1.59","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.gde.2017.11.003","article-title":"Cross-talk between protein synthesis, energy metabolism and autophagy in cancer","volume":"48","year":"2018","journal-title":"Current opinion in genetics & development"},{"key":"2026020502421800000_2026.01.24.701539v1.60","doi-asserted-by":"publisher","DOI":"10.1074\/jbc.273.23.14424"},{"key":"2026020502421800000_2026.01.24.701539v1.61","doi-asserted-by":"crossref","unstructured":"Zheng Y , Koh HY , Ju J , Yang M , May LT , Webb GI , et al. Large language models for drug discovery and development. Patterns. 2025;6(10).","DOI":"10.1016\/j.patter.2025.101346"},{"key":"2026020502421800000_2026.01.24.701539v1.62","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-024-07421-0"},{"issue":"1","key":"2026020502421800000_2026.01.24.701539v1.63","doi-asserted-by":"crossref","first-page":"6572","DOI":"10.1038\/s41467-022-34025-x","article-title":"Uncertainty-informed deep learning models enable high-confidence predictions for digital histopathology","volume":"13","year":"2022","journal-title":"Nature communications"},{"key":"2026020502421800000_2026.01.24.701539v1.64","doi-asserted-by":"crossref","unstructured":"Chen Z , Li P , Dong X , Hong P. Uncertainty quantification for clinical outcome predictions with (large) language models. In: Findings of the Association for Computational Linguistics: NAACL 2025; 2025. p. 7512\u201323.","DOI":"10.18653\/v1\/2025.findings-naacl.419"},{"issue":"3","key":"2026020502421800000_2026.01.24.701539v1.65","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1038\/nrurol.2016.272","article-title":"A spatiotemporal hypothesis for the regulation, role, and targeting of AMPK in prostate cancer","volume":"14","year":"2017","journal-title":"Nature Reviews Urology"},{"key":"2026020502421800000_2026.01.24.701539v1.66","doi-asserted-by":"crossref","unstructured":"Cicero AF , Baggioni A . Berberine and its role in chronic disease. Anti-inflammatory nutraceuticals and chronic diseases. 2016:27\u201345.","DOI":"10.1007\/978-3-319-41334-1_2"},{"key":"2026020502421800000_2026.01.24.701539v1.67","doi-asserted-by":"crossref","unstructured":"Ahmed KT , Ansari MI , Zhang W . DTI-LM: language model powered drug\u2013target interaction prediction. Bioinformatics. 2024;40(9):btae533.","DOI":"10.1093\/bioinformatics\/btae533"},{"key":"2026020502421800000_2026.01.24.701539v1.68","unstructured":"Carammia M , Iacus SM , Porro G. Rethinking scale: The efficacy of fine-tuned open-source llms in large-scale reproducible social science research. arXiv preprint arXiv:241100890. 2024."}],"container-title":[],"original-title":[],"link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.64898\/2026.01.24.701539","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T10:42:32Z","timestamp":1770288152000},"score":1,"resource":{"primary":{"URL":"http:\/\/biorxiv.org\/lookup\/doi\/10.64898\/2026.01.24.701539"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,26]]},"references-count":68,"URL":"https:\/\/doi.org\/10.64898\/2026.01.24.701539","relation":{},"subject":[],"published":{"date-parts":[[2026,1,26]]},"subtype":"preprint"}}