{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T16:35:00Z","timestamp":1791909300065,"version":"4.3.4"},"reference-count":144,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T00:00:00Z","timestamp":1762300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100012554","name":"Hubei Provincial Department of Education","doi-asserted-by":"crossref","award":["Q20231610"],"award-info":[{"award-number":["Q20231610"]}],"id":[{"id":"10.13039\/100012554","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Research Project of Wuhan Polytechnic University","award":["2022Y14"],"award-info":[{"award-number":["2022Y14"]}]},{"name":"Research Project of Wuhan Polytechnic University","award":["2023Y22"],"award-info":[{"award-number":["2023Y22"]}]},{"name":"Science and Technology Talent Serving Enterprises Project of Hubei Provincial Department of Science and Technology","award":["2024DJC087"],"award-info":[{"award-number":["2024DJC087"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>Breast cancer is a heterogeneous malignancy with distinct molecular subtypes that complicate the development of effective therapies. Traditional drug discovery methods are often constrained by high cost and long development timelines, underscoring the need for more efficient, subtype-aware approaches. Computer-aided drug design (CADD) has emerged as a valuable strategy to accelerate therapeutic discovery and improve lead optimization. This review synthesizes advances from a subtype-centric perspective and outlines the application of CADD techniques, including molecular docking, virtual screening (VS), pharmacophore modeling, and molecular dynamics (MD) simulations, to identify potential targets and inhibitors in receptor-positive (Luminal), HER2-positive (HER2+), and triple-negative breast cancer (TNBC). In addition to traditional pipelines, we highlight artificial intelligence (AI)-enabled methods and a hybrid workflow in which learning-based models rapidly triage chemical space while physics-based simulations provide mechanistic validation. These approaches have facilitated the discovery of subtype-specific compounds and enabled the refinement of candidate drugs to enhance efficacy and reduce toxicity. Despite these advances, critical challenges remain, particularly tumor heterogeneity, drug resistance, and the need to rigorously validate computational predictions through experimental studies. Future progress is expected to be driven by the integration of AI, machine learning (ML), multi-omics data, and digital pathology, which may enable the design of more precise, subtype-informed, and personalized therapeutic strategies for breast cancer.<\/jats:p>","DOI":"10.3390\/ijms262110744","type":"journal-article","created":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T13:19:29Z","timestamp":1762348769000},"page":"10744","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Computer-Aided Drug Design Across Breast Cancer Subtypes: Methods, Applications and Translational Outlook"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8447-1314","authenticated-orcid":false,"given":"Wei","family":"Tian","sequence":"first","affiliation":[{"name":"School of Life Science and Technology, Wuhan Polytechnic University, Wuhan 430023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Life Science and Technology, Wuhan Polytechnic University, Wuhan 430023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyu","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Life Science and Technology, Wuhan Polytechnic University, Wuhan 430023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinghui","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Life Science and Technology, Wuhan Polytechnic University, Wuhan 430023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Life Science and Technology, Wuhan Polytechnic University, Wuhan 430023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,5]]},"reference":[{"key":"ref_1","first-page":"209","article-title":"Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J. Clin."},{"key":"ref_2","first-page":"8","article-title":"Cancer Statistics, 2024","volume":"74","author":"Siegel","year":"2024","journal-title":"CA Cancer J. Clin."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1200\/JCO.2021.39.15_suppl.1043","article-title":"Updated results of tucatinib versus placebo added to trastuzumab and capecitabine for patients with pretreated HER2+ metastatic breast cancer with and without brain metastases (HER2CLIMB)","volume":"39","author":"Curigliano","year":"2021","journal-title":"J. Clin. Oncol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e2002","DOI":"10.1002\/onco.13918","article-title":"Real-World Clinical and Economic Outcomes in Selected Immune-Related Adverse Events Among Patients with Cancer Receiving Immune Checkpoint Inhibitors","volume":"26","author":"Zheng","year":"2021","journal-title":"Oncologist"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1022","DOI":"10.1200\/JCO.2023.41.16_suppl.1022","article-title":"Sequential Use of Antibody-Drug Conjugate After Antibody-Drug Conjugate for Patients with Metastatic Breast Cancer: ADC After ADC (A3) Study","volume":"41","author":"Abelman","year":"2023","journal-title":"J. Clin. Oncol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"LBA4","DOI":"10.1200\/JCO.2025.43.17_suppl.LBA4","article-title":"Camizestrant + CDK4\/6 inhibitor for emergent ESR1 mutations during first-line endocrine-based therapy in HR+\/HER2\u2212 advanced breast cancer: Phase 3, double-blind, ctDNA-guided SERENA-6 trial","volume":"43","author":"Turner","year":"2025","journal-title":"J. Clin. Oncol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Garg, P., Singhal, G., Kulkarni, P., Horne, D., Salgia, R., and Singhal, S.S. (2024). Artificial intelligence-driven computational approaches in the development of anticancer drugs. Cancers, 16.","DOI":"10.3390\/cancers16223884"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2863","DOI":"10.1021\/acs.jctc.3c00123","article-title":"Complete active space methods for NISQ devices: The importance of canonical orbital optimization for accuracy and noise resilience","volume":"19","author":"Delcey","year":"2023","journal-title":"J. Chem. Theory Comput."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1021\/acsphyschemau.4c00006","article-title":"Structure-guided antiviral peptides identification targeting the HIV-1 integrase","volume":"4","author":"Hossain","year":"2024","journal-title":"ACS Phys. Chem. Au."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1038\/s41573-023-00832-0","article-title":"Integrating QSAR Modelling and Deep Learning in Drug Discovery: The Emergence of Deep QSAR","volume":"23","author":"Tropsha","year":"2024","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Swanson, K., Walther, P., Leitz, J., Mukherjee, S., Wu, J.C., Shivnaraine, R.V., and Zou, J. (2024). ADMET-AI: A Machine Learning Platform for Fast and Accurate ADMET Property Prediction. Bioinformatics, 40.","DOI":"10.1093\/bioinformatics\/btae416"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.1002\/cpdd.1164","article-title":"Drug-Drug Interactions with the Hepatitis B Virus Capsid Assembly Modulator JNJ-56136379 (Bersacapavir)","volume":"11","author":"Vandenbossche","year":"2022","journal-title":"Clin. Pharmacol. Drug Dev."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4056","DOI":"10.1200\/JCO.2023.41.16_suppl.4056","article-title":"Predicting Role of Circulating Tumor DNA and Blood-Based Tumor Mutational Burden in Esophageal Squamous Cell Carcinoma Receiving Chemoradiotherapy Combined with Toripalimab: Exploratory Analyses from a Phase II Trial (EC-CRT-001)","volume":"41","author":"Chen","year":"2023","journal-title":"J. Clin. Oncol."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Hosseini, S.-R., and Zhou, X. (2023). CCSynergy: An integrative deep-learning framework enabling context-aware prediction of anti-cancer drug synergy. Brief. Bioinform., 24.","DOI":"10.1093\/bib\/bbac588"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1038\/s41586-022-05066-5","article-title":"Truncated FGFR2 Is a Clinically Actionable Oncogene in Multiple Cancers","volume":"608","author":"Zingg","year":"2022","journal-title":"Nature"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2674","DOI":"10.1038\/s41467-019-09799-2","article-title":"Community Assessment to Advance Computational Prediction of Cancer Drug Combinations in a Pharmacogenomic Screen","volume":"10","author":"Menden","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"ii155","DOI":"10.1093\/bioinformatics\/btac482","article-title":"Exploiting Pretrained Biochemical Language Models for Targeted Drug Design","volume":"38","author":"Ozkirimli","year":"2022","journal-title":"Bioinformatics"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Sharaf, B., Hajahjeh, A., Bani Hani, H., and Abdel-Razeq, H. (2024). Next generation selective estrogen receptor degraders in postmenopausal women with advanced-stage hormone receptors-positive, HER2-negative breast cancer. Front. Oncol., 14.","DOI":"10.3389\/fonc.2024.1385577"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e1608","DOI":"10.1002\/wcms.1608","article-title":"Generative Models for Molecular Discovery: Recent Advances and Challenges","volume":"12","author":"Bilodeau","year":"2022","journal-title":"WIREs Comput. Mol. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1038\/s41698-023-00466-w","article-title":"Patient-derived organoids identify tailored therapeutic options and determinants of plasticity in sarcomatoid urothelial bladder cancer","volume":"7","author":"Garioni","year":"2023","journal-title":"NPJ Precis. Oncol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"114775","DOI":"10.1016\/j.ejmech.2022.114775","article-title":"Discovery of potent and selective HER2 PROTAC degrader based on tucatinib with improved efficacy against HER2-positive cancers","volume":"244","author":"Hu","year":"2022","journal-title":"Eur. J. Med. Chem."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhang, C., Condon, A., and Dao Duc, K. (2025). CryoSAMU: Enhancing 3D Cryo-EM Density Maps of Protein Structures at Intermediate Resolution with Structure-Aware Multimodal U-Nets. arXiv.","DOI":"10.32388\/ADIF5A"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wu, L., Wen, Y., Leng, D., Zhang, Q., Dai, C., Wang, Z., Liu, Z., Yan, B., Zhang, Y., and Wang, J. (2022). Machine Learning Methods, Databases and Tools for Drug Combination Prediction. Brief. Bioinform., 23.","DOI":"10.1093\/bib\/bbab355"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1038\/s41586-021-03819-2","article-title":"Highly accurate protein structure prediction with AlphaFold","volume":"596","author":"Jumper","year":"2021","journal-title":"Nature"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1038\/d41586-022-02083-2","article-title":"\u2018The Entire Protein Universe\u2019: AlphaFold Predictions for 200 Million Proteins","volume":"608","author":"Callaway","year":"2022","journal-title":"Nature"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1038\/s41592-022-01488-1","article-title":"ColabFold: Making Protein Folding Accessible to All","volume":"19","author":"Mirdita","year":"2022","journal-title":"Nat. Methods"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"D439","DOI":"10.1093\/nar\/gkab1061","article-title":"AlphaFold Protein Structure Database: Massively expanding the structural coverage of protein-sequence space with high-accuracy models","volume":"50","author":"Varadi","year":"2022","journal-title":"Nucleic Acids Res."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhu, W., Shenoy, A., Kundrotas, P., and Elofsson, A. (2023). Evaluation of AlphaFold-Multimer Prediction on Multi-Chain Protein Complexes. Bioinformatics, 39.","DOI":"10.1093\/bioinformatics\/btad424"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1162","DOI":"10.1016\/j.tips.2024.10.006","article-title":"Development of PROTACs Using Computational Approaches","volume":"45","author":"Ge","year":"2024","journal-title":"Trends Pharmacol. Sci."},{"key":"ref_30","unstructured":"Hu, X., Liu, G., Chen, C., Zhao, Y., Zhang, H., and Liu, X. (2025). TransDiffSBDD: Causality-Aware Multi-Modal Structure-Based Drug Design. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1038\/s41586-024-07487-w","article-title":"Accurate structure prediction of biomolecular interactions with AlphaFold 3","volume":"630","author":"Abramson","year":"2024","journal-title":"Nature"},{"key":"ref_32","unstructured":"Corso, G., St\u00e4rk, H., Jing, B., Barzilay, R., and Jaakkola, T. (2023). DiffDock: Diffusion steps, twists, and turns for molecular docking. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"022423","DOI":"10.1103\/PhysRevA.106.022423","article-title":"Entanglement Perspective on the Quantum Approximate Optimization Algorithm","volume":"106","author":"Dupont","year":"2022","journal-title":"Phys. Rev. A"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Jafarnejad, S., Baradaran, A., Zhang, P., Kumar, A., Li, Y., Wang, J., Patel, R., Johnson, M., Chen, L., and Anderson, K. (2024). Cryo-electron microscopy-based drug design. Front. Mol. Biosci., 11.","DOI":"10.3389\/fmolb.2024.1342179"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"8515","DOI":"10.1038\/s41467-023-44208-9","article-title":"Fast Free Energy Estimates from \u03bb-Dynamics with Bias for Relative Binding Free Energies","volume":"14","author":"Robo","year":"2023","journal-title":"Nat. Commun."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1038\/s42256-024-00843-5","article-title":"Machine learning-aided generative molecular design","volume":"6","author":"Du","year":"2024","journal-title":"Nat. Mach. Intell."},{"key":"ref_37","unstructured":"St\u00e4rk, H., Ganea, O.E., Pattanaik, L., Barzilay, R., and Jaakkola, T. (2022, January 17\u201323). EquiBind: Geometric deep learning for drug binding structure prediction. Proceedings of the 39th International Conference on Machine Learning, Baltimore, MD, USA."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"822","DOI":"10.1016\/j.trecan.2019.10.007","article-title":"Metabolic Regulation of Macrophage Polarization in Cancer","volume":"5","author":"Mehla","year":"2019","journal-title":"Trends Cancer"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1056\/NEJMoa2410858","article-title":"Imlunestrant with or without Abemaciclib in Advanced Breast Cancer","volume":"392","author":"Jhaveri","year":"2025","journal-title":"N. Engl. J. Med."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1186\/s13019-019-1040-6","article-title":"Health-Related Quality of Life in Children and Adolescents Undergoing Intraoperative Device Closure of Isolated Perimembranous Ventricular Septal Defects in Southeastern China","volume":"14","author":"Huang","year":"2019","journal-title":"J. Cardiothorac. Surg."},{"key":"ref_41","first-page":"1265","article-title":"Ciltacabtagene Autoleucel, an Anti-B-Cell Maturation Antigen Chimeric Antigen Receptor T-Cell Therapy, for Relapsed\/Refractory Multiple Myeloma: CARTITUDE-1 2-Year Follow-Up.J","volume":"41","author":"Martin","year":"2023","journal-title":"Clin. Oncol."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Shao, B., Tong, Z., Ouyang, Q., Wang, Y., Xu, G., Li, S., and Li, H. (2022). Phase Ib study of camrelizumab combined with apatinib and fuzuloparib in recurrent\/metastatic triple-negative breast cancer. BMC Med., 20.","DOI":"10.1186\/s12916-022-02527-6"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1024","DOI":"10.1016\/j.molcel.2023.03.004","article-title":"Histone Chaperones: A Multinodal Highway Network Inside the Cell","volume":"83","author":"Li","year":"2023","journal-title":"Mol. Cell"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Hernando, C., Ortega-Morillo, B., Tapia, M., Morag\u00f3n, S., Mart\u00ednez, M.T., Eroles, P., Garrido-Cano, I., Adam-Artigues, A., Lluch, A., and Bermejo, B. (2021). Oral Selective Estrogen Receptor Degraders (SERDs) as a Novel Breast Cancer Therapy: Present and Future from a Clinical Perspective. Int. J. Mol. Sci., 22.","DOI":"10.3390\/ijms22157812"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"4464","DOI":"10.21037\/tcr-20-2166","article-title":"Fulvestrant as a reference antiestrogen and estrogen receptor degrader in preclinical studies: Treatment dosage, efficacy, and implications on development of new ER-targeting agents","volume":"9","author":"Wang","year":"2020","journal-title":"Transl. Cancer Res."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Pappalardo, M., Sipala, F.M., Nicolosi, M.C., Guccione, S., and Ronsisvalle, S. (2024). Recent Applications ofIn SilicoApproaches for Studying Receptor Mutations Associated with Human Pathologies. Molecules, 29.","DOI":"10.3390\/molecules29225349"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"4299","DOI":"10.1158\/1078-0432.CCR-24-1073","article-title":"Elacestrant in ER+, HER2\u2212 metastatic breast cancer with ESR1-mutated tumors: Subgroup analyses from the phase III EMERALD trial by prior duration of endocrine therapy plus CDK4\/6 inhibitor and in clinical subgroups","volume":"30","author":"Bardia","year":"2024","journal-title":"Clin. Cancer Res."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1002293","DOI":"10.37349\/etat.2025.1002293","article-title":"Elacestrant in hormone receptor-positive metastatic breast cancer: A post-hoc analysis","volume":"6","author":"Sarfraz","year":"2025","journal-title":"Explor. Target Antitumor Ther."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"101377","DOI":"10.1016\/j.esmoop.2023.101377","article-title":"188O EMERALD trial analysis of patient-reported outcomes (PROs) in patients with ER+\/HER2\u2212 advanced or metastatic breast cancer (mBC) comparing oral elacestrant vs standard of care (SoC) endocrine therapy","volume":"8","author":"Bidard","year":"2023","journal-title":"ESMO Open"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Novak, B.J., Fraser, D., and Maloney, T.H. (2020). Transforming ocean conservation: Applying the genetic rescue toolkit. Genes, 11.","DOI":"10.3390\/genes11020209"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.jhsg.2023.11.011","article-title":"Surgical Management of Hook of Hamate Fractures: A Systematic Review of Outcomes","volume":"6","author":"Donohue","year":"2023","journal-title":"J. Hand Surg. Glob. Online"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"107983","DOI":"10.1016\/j.maturitas.2024.107983","article-title":"Menopause in the Workplace: Challenges, Impact, and Next Steps","volume":"185","author":"Safwan","year":"2024","journal-title":"Maturitas"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"176245","DOI":"10.1016\/j.ejphar.2023.176245","article-title":"Cannabidiol Represses miR-143 to Promote Cardiomyocyte Proliferation and Heart Regeneration After Myocardial Infarction","volume":"963","author":"Ren","year":"2024","journal-title":"Eur. J. Pharmacol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"e0025822","DOI":"10.1128\/msystems.00258-22","article-title":"Determination of the Stage Composition of Plasmodium Infections from Bulk Gene Expression Data","volume":"7","author":"Tebben","year":"2022","journal-title":"mSystems"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.1016\/j.tibtech.2022.09.011","article-title":"Design of Biomass-Based Renewable Materials for Environmental Remediation","volume":"40","author":"Zhang","year":"2022","journal-title":"Trends Biotechnol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1388","DOI":"10.1016\/S1470-2045(24)00443-1","article-title":"The State of the Science of Oral Selective Oestrogen Receptor Degraders","volume":"25","author":"Fanucci","year":"2024","journal-title":"Lancet Oncol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1446","DOI":"10.1038\/ng.2823","article-title":"ActivatingESR1Mutations in Hormone-Resistant Metastatic Breast Cancer","volume":"45","author":"Robinson","year":"2013","journal-title":"Nat. Genet."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1186\/s13058-024-01945-z","article-title":"Targeting unique ligand binding domain structural features downregulates DKK1 in Y537S ESR1 mutant breast cancer cells","volume":"27","author":"Young","year":"2025","journal-title":"Breast Cancer Res."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"2933","DOI":"10.2147\/DDDT.S380925","article-title":"Oral selective estrogen receptor degraders (SERDs) in breast cancer: Advances, challenges, and current status","volume":"16","author":"Downton","year":"2022","journal-title":"Drug Des. Dev. Ther."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"4242","DOI":"10.1158\/1078-0432.CCR-19-3387","article-title":"A randomized, open-label, presurgical, window-of-opportunity study comparing the pharmacodynamic effects of the novel oral SERD AZD9496 with fulvestrant in patients with newly diagnosed ER+ HER2\u2212 primary breast cancer","volume":"26","author":"Robertson","year":"2020","journal-title":"Clin. Cancer Res."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Sabit, H., Abbas, S., El-Safoury, M.T., Madkour, E.M., Mahmoud, S., Abdel-Ghany, S., Albrahim, Y., Al-Dhuayan, I.S., Rashwan, S., and El-Hashash, A. (2025). Antibody-drug conjugates in breast cancer: Navigating innovations, overcoming resistance, and shaping future therapies. Biomedicines, 13.","DOI":"10.3390\/biomedicines13092227"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Hummer, A.M., Abanades, B., and Deane, C.M. (2022). Advances in Computational Structure-Based Antibody Design. Curr. Opin. Struct. Biol., 74.","DOI":"10.1016\/j.sbi.2022.102379"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1038\/s41523-023-00531-4","article-title":"Calcifications in triple-negative breast cancer: Molecular features and treatment strategies","volume":"9","author":"Lin","year":"2023","journal-title":"NPJ Breast Cancer"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Jin, R., Ye, Q., Wang, J., Cao, Z., Jiang, D., Wang, T., Kang, Y., Xu, W., Hsieh, C., and Hou, T. (2024). AttABseq: An Attention-Based Deep Learning Prediction Method for Antigen-Antibody Binding Affinity Changes Based on Protein Sequences. Brief. Bioinform., 25.","DOI":"10.1093\/bib\/bbae304"},{"key":"ref_65","first-page":"7277","article-title":"Revolutionizing Cancer Treatment: Unleashing the Power of Viral Vaccines, Monoclonal Antibodies, and Proteolysis-Targeting Chimeras in the New Era of Immunotherapy","volume":"9","author":"Mohite","year":"2024","journal-title":"ACS Omega"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Chen, J., Hao, L., Qian, X., Lin, L., Pan, Y., and Han, X. (2022). Machine Learning Models Based on Immunological Genes to Predict the Response to Neoadjuvant Therapy in Breast Cancer Patients. Front. Immunol., 13.","DOI":"10.3389\/fimmu.2022.948601"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1038\/s41698-023-00352-5","article-title":"Artificial Intelligence Reveals Features Associated with Breast Cancer Neoadjuvant Chemotherapy Responses from Multi-Stain Histopathologic Images","volume":"7","author":"Huang","year":"2023","journal-title":"NPJ Precis. Oncol."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"La, Z., Chen, J., Lu, X., Lei, C., Li, F., Zhao, L., and Yi, Y. (2025). AI Microscope Facilitates Accurate Interpretation of HER2 Immunohistochemical Scores 0 and 1+ in Invasive Breast Cancer. Sci. Rep., 15.","DOI":"10.1038\/s41598-025-13820-8"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Wang, Z.-H., Li, Y., Zhang, J.-Y., Liu, Y., Wang, X., Wang, Y., Chen, J., Yang, Y.-H., Li, H.-Y., and Zhang, S.-Y. (2022). Trastuzumab Resistance in HER2-Positive Breast Cancer: Mechanisms, Emerging Biomarkers and Targeting Agents. Front. Oncol., 12.","DOI":"10.3389\/fonc.2022.1006429"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1001\/jamaoncol.2022.5610","article-title":"Tucatinib versus Placebo, Both in Combination With Trastuzumab and Capecitabine, for Previously Treated ERBB2 (HER2)-Positive Metastatic Breast Cancer in Patients With Brain Metastases: Updated Exploratory Analysis of the HER2CLIMB Randomized Clinical Trial","volume":"9","author":"Lin","year":"2023","journal-title":"JAMA Oncol."},{"key":"ref_71","first-page":"22","article-title":"Mechanisms of action and resistance to anti-HER2 antibody-drug conjugates in breast cancer","volume":"7","author":"Saleh","year":"2024","journal-title":"Cancer Drug Resist."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1007\/s12195-024-00823-0","article-title":"Rational Design of HER2-Targeted Combination Therapies to Reverse Drug Resistance in Fibroblast-Protected HER2+ Breast Cancer Cells","volume":"17","author":"Poskus","year":"2024","journal-title":"Cell. Mol. Bioeng."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1007\/s10549-021-06143-5","article-title":"Final results of the double-blind placebo-controlled randomized phase 2 LOTUS trial of first-line ipatasertib plus paclitaxel for inoperable locally advanced\/metastatic triple-negative breast cancer","volume":"189","author":"Dent","year":"2021","journal-title":"Breast Cancer Res. Treat."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1016\/S1470-2045(23)00242-5","article-title":"Zanidatamab for HER2-amplified, unresectable, locally advanced or metastatic biliary tract cancer (HERIZON-BTC-01): A multicentre, single-arm, phase 2b study","volume":"24","author":"Harding","year":"2023","journal-title":"Lancet Oncol."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"4329","DOI":"10.1158\/1078-0432.CCR-24-0465","article-title":"Ipatasertib plus Paclitaxel for Patients with PIK3CA\/AKT1\/PTEN-Altered Locally Advanced Unresectable or Metastatic Triple-Negative Breast Cancer in the IPATunity130 Phase III Trial","volume":"30","author":"Dent","year":"2024","journal-title":"Clin. Cancer Res."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Jamal, M.S., Song, K.S., Lee, K.W., Kim, J.J., and Park, Y.M. (2023). Revolutionizing drug targeting strategies: Integrating artificial intelligence and structure-based methods in PROTAC development. Pharmaceuticals, 16.","DOI":"10.3390\/ph16121649"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Lee, E.K., and Matulonis, U.A. (2020). PARP Inhibitor Resistance Mechanisms and Implications for Post-Progression Combination Therapies. Cancers, 12.","DOI":"10.3390\/cancers12082054"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1111\/cas.16033","article-title":"BRCA1\/2Reversion Mutations in a Pan-Cancer Cohort","volume":"115","author":"Nakamura","year":"2024","journal-title":"Cancer Sci."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"12630","DOI":"10.1021\/acs.jmedchem.1c00567","article-title":"Discovery of First-in-Class Dual PARP and EZH2 Inhibitors for Triple-Negative Breast Cancer with Wild-Type BRCA","volume":"64","author":"Wang","year":"2021","journal-title":"J. Med. Chem."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"e13730","DOI":"10.1111\/cpr.13730","article-title":"BET Inhibitors (BETi) Influence Oxidative Phosphorylation Metabolism by Affecting Mitochondrial Dynamics Leading to Alterations in Apoptotic Pathways in Triple-Negative Breast Cancer (TNBC) Cells","volume":"57","author":"Rossi","year":"2024","journal-title":"Cell Prolif."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"e202302268","DOI":"10.26508\/lsa.202302268","article-title":"Pathway-Driven Analysis of Synthetic Lethal Interactions in Cancer Using Perturbation Screens","volume":"7","author":"Karimpour","year":"2024","journal-title":"Life Sci. Alliance"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"107813","DOI":"10.1016\/j.phrs.2025.107813","article-title":"The Roles of Histone Acetylation Key Enzymes HAT, HDAC and BET Proteins in Neuropathic Pain: Selection of Drug Targets","volume":"217","author":"Wang","year":"2025","journal-title":"Pharmacol. Res."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Luque, M., Sanz-\u00c1lvarez, M., Santamar\u00eda, A., Zazo, S., Crist\u00f3bal, I., de la Fuente, L., M\u00ednguez, P., Eroles, P., Rovira, A., and Albanell, J. (2021). Targeted therapy modulates the secretome of cancer-associated fibroblasts to induce resistance in HER2-positive breast cancer. Int. J. Mol. Sci., 22.","DOI":"10.3390\/ijms222413297"},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Guo, Y., Zou, Y., Chen, Y., Deng, D., Zhang, Z., Liu, K., Tang, M., Yang, T., Fu, S., and Zhang, C. (2023). Design, Synthesis and Biological Evaluation of Purine-Based Derivatives as Novel JAK2\/BRD4(BD2) Dual Target Inhibitors. Bioorg. Chem., 132.","DOI":"10.1016\/j.bioorg.2023.106386"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1002\/cbin.11139","article-title":"JQ1, a BET-Bromodomain Inhibitor, Inhibits Human Cancer Growth and Suppresses PD-L1 Expression","volume":"43","author":"Liu","year":"2019","journal-title":"Cell Biol. Int."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"21186","DOI":"10.1021\/acs.jmedchem.4c01976","article-title":"Discovery of Dual CDK6\/BRD4 Inhibitor Inducing Apoptosis and Increasing the Sensitivity of Ferroptosis in Triple-Negative Breast Cancer","volume":"67","author":"Zhang","year":"2024","journal-title":"J. Med. Chem."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"A17","DOI":"10.1158\/2326-6074.TUMIMM17-A17","article-title":"Bromodomain and Extraterminal Proteins Regulate PD-L1\/PD-1 Signaling in Breast Cancer","volume":"6","author":"Andrieu","year":"2018","journal-title":"Cancer Immunol. Res."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"2917","DOI":"10.1016\/j.molcel.2023.06.032","article-title":"Distinct Layers of BRD4-PTEFb Reveal Bromodomain-Independent Function in Transcriptional Regulation","volume":"83","author":"Zheng","year":"2023","journal-title":"Mol. Cell"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"1262","DOI":"10.1021\/acs.jmedchem.3c01781","article-title":"PROTACs Targeting BRM (SMARCA2) Afford SelectiveIn VivoDegradation over BRG1 (SMARCA4) and Are Active in BRG1 Mutant Xenograft Tumor Models","volume":"67","author":"Berlin","year":"2024","journal-title":"J. Med. Chem."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Li, R., Yan, L., Zhang, L., Ma, H., Wang, H., Bu, P., Xi, Y., and Lian, J. (2024). Genomic Characterization Reveals Distinct Mutational Landscapes and Therapeutic Implications Between Different Molecular Subtypes of Triple-Negative Breast Cancer. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-62991-3"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"e2000368","DOI":"10.1200\/PO.20.00368","article-title":"Changes in Triple-Negative Breast Cancer Molecular Subtypes in Patients Without Pathologic Complete Response After Neoadjuvant Systemic Chemotherapy","volume":"6","author":"Masuda","year":"2022","journal-title":"JCO Precis. Oncol."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Marqu\u00e9s, M., Sorolla, M.A., Urdanibia, I., Parisi, E., Hidalgo, I., Morales, S., Salud, A., and Sorolla, A. (2022). Are transcription factors plausible oncotargets for triple negative breast cancers?. Cancers, 14.","DOI":"10.3390\/cancers14051101"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"e20210693","DOI":"10.1084\/jem.20210693","article-title":"IL-17-Induced HIF1\u03b1 Drives Resistance to Anti-PD-L1 via Fibroblast-Mediated Immune Exclusion","volume":"219","author":"Chen","year":"2022","journal-title":"J. Exp. Med."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"e1010","DOI":"10.1016\/j.crad.2024.04.021","article-title":"Role of Oedema and Shrinkage Patterns for Prediction of Response to Neoadjuvant Chemotherapy and Survival Outcomes in Luminal Breast Cancer","volume":"79","author":"Sun","year":"2024","journal-title":"Clin. Radiol."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1186\/s12967-022-03452-1","article-title":"Integration of Radiogenomic Features for Early Prediction of Pathological Complete Response in Patients with Triple-Negative Breast Cancer and Identification of Potential Therapeutic Targets","volume":"20","author":"Zhang","year":"2022","journal-title":"J. Transl. Med."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1002\/jmri.28273","article-title":"Four-Dimensional Machine Learning Radiomics for the Pretreatment Assessment of Breast Cancer Pathologic Complete Response to Neoadjuvant Chemotherapy in Dynamic Contrast-Enhanced MRI","volume":"57","author":"Caballo","year":"2023","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"1213","DOI":"10.1007\/s11912-024-01580-z","article-title":"Radiogenomics as an Integrated Approach to Glioblastoma Precision Medicine","volume":"26","author":"Sanchez","year":"2024","journal-title":"Curr. Oncol. Rep."},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Bhattarai, S., Saini, G., Li, H., Seth, G., Fisher, T.B., Janssen, E.A.M., Kiraz, U., Kong, J., and Aneja, R. (2024). Predicting Neoadjuvant Treatment Response in Triple-Negative Breast Cancer Using Machine Learning. Diagnostics, 14.","DOI":"10.1101\/2023.04.17.536459"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Li, W., Zhao, X., Ren, C., Gao, S., Han, Q., Lu, M., and Li, X. (2024). The Therapeutic Role of \u03b3\u03b4T Cells in TNBC. Front. Immunol., 15.","DOI":"10.3389\/fimmu.2024.1420107"},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Jung, H., and Lee, Y. (2025). Targeting the undruggable: Recent progress in PROTAC-induced transcription factor degradation. Cancers, 17.","DOI":"10.3390\/cancers17111871"},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Kurokawa, K., Mitsuishi, Y., Shimada, N., Kawakami, Y., Miura, K., Miyawaki, T., Asao, T., Ko, R., Shukuya, T., and Shibayama, R. (2022). Association Between the Efficacy and Immune-Related Adverse Events of Pembrolizumab and Chemotherapy in Non-Small Cell Lung Cancer Patients: A Retrospective Study. BMC Cancer, 22.","DOI":"10.1186\/s12885-022-10133-1"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1038\/s41523-024-00679-7","article-title":"Results from the Randomized KEYNOTE-355 Study of Pembrolizumab Plus Chemotherapy for Asian Patients with Advanced TNBC","volume":"10","author":"Im","year":"2024","journal-title":"NPJ Breast Cancer"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1016\/j.annonc.2024.02.002","article-title":"Event-Free Survival by Residual Cancer Burden with Pembrolizumab in Early-Stage TNBC: Exploratory Analysis from KEYNOTE-522","volume":"35","author":"Pusztai","year":"2024","journal-title":"Ann. Oncol."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/s10549-023-07233-2","article-title":"High Tumor Infiltrating Lymphocytes Are Significantly Associated with Pathological Complete Response in Triple Negative Breast Cancer Treated with Neoadjuvant KEYNOTE-522 Chemoimmunotherapy","volume":"205","author":"Wood","year":"2024","journal-title":"Breast Cancer Res. Treat."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.annonc.2023.09.3114","article-title":"The Double Antibody Drug Conjugate (DAD) Phase I Trial: Sacituzumab Govitecan Plus Enfortumab Vedotin for Metastatic Urothelial Carcinoma","volume":"35","author":"McGregor","year":"2024","journal-title":"Ann. Oncol."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"2879","DOI":"10.1080\/14796694.2024.2342224","article-title":"Real-World Comparative Effectiveness and Safety of Pembrolizumab for PD-L1 \u2265 50% Metastatic Non-Small-Cell Lung Cancer","volume":"20","author":"Parmar","year":"2024","journal-title":"Future Oncol."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1002\/cncr.33885","article-title":"JASPER: Phase 2 Trial of First-Line Niraparib Plus Pembrolizumab in Patients with Advanced Non-Small Cell Lung Cancer","volume":"128","author":"Ramalingam","year":"2022","journal-title":"Cancer"},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Donisi, C., Pretta, A., Pusceddu, V., Ziranu, P., Lai, E., Puzzoni, M., Mariani, S., Massa, E., Madeddu, C., and Scartozzi, M. (2024). Immunotherapy and cancer: The multi-omics perspective. Int. J. Mol. Sci., 25.","DOI":"10.3390\/ijms25063563"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"107216","DOI":"10.1016\/j.lungcan.2023.107216","article-title":"Olaparib and Durvalumab in Patients with Relapsed Small Cell Lung Cancer (MEDIOLA): An Open-Label, Multicenter, Phase 1\/2, Basket Study","volume":"180","author":"Krebs","year":"2023","journal-title":"Lung Cancer"},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Wilton, J., Abdulmenan, J., and Janjua, N.Z. (2024). Cohort Profile: The British Columbia COVID-19 Cohort (BCC19C)-A Dynamic, Linked Population-Based Cohort. Front. Public Health, 12.","DOI":"10.3389\/fpubh.2024.1248905"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1186\/s13058-024-01799-5","article-title":"Overcoming Doxorubicin Resistance in Triple-Negative Breast Cancer Using the Class I-Targeting HDAC Inhibitor Bocodepsin\/OKI-179 to Promote Apoptosis","volume":"26","author":"Smoots","year":"2024","journal-title":"Breast Cancer Res."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"2799","DOI":"10.1007\/s11030-024-10979-6","article-title":"Ligand and Structure-Based Virtual Screening Approaches in Drug Discovery: Minireview","volume":"29","author":"Marinho","year":"2025","journal-title":"Mol. Divers."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"115419","DOI":"10.1016\/j.ejmech.2023.115419","article-title":"Structure-Based Virtual Screening Discovers Potent and Selective Adenosine A1 Receptor Antagonists","volume":"257","author":"Matricon","year":"2023","journal-title":"Eur. J. Med. Chem."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"2268","DOI":"10.1002\/prot.23053","article-title":"Fast Approximations of the Rotational Diffusion Tensor and Their Application to Structural Assembly of Molecular Complexes","volume":"79","author":"Berlin","year":"2011","journal-title":"Proteins"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"4492","DOI":"10.1039\/D3RA08650J","article-title":"Exploring Protein-Ligand Binding Affinity Prediction with Electron Density-Based Geometric Deep Learning","volume":"14","author":"Isett","year":"2024","journal-title":"RSC Adv."},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Ahmed, M., Maldonado, A.M., and Durrant, J.D. (2023). From Byte to Bench to Bedside: Molecular Dynamics Simulations and Drug Discovery. BMC Biol., 21.","DOI":"10.1186\/s12915-023-01791-z"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"5308","DOI":"10.1021\/acs.jcim.0c00547","article-title":"Evolution of Alchemical Free Energy Methods in Drug Discovery","volume":"60","author":"Song","year":"2020","journal-title":"J. Chem. Inf. Model."},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Cai, H., Zhang, H., Zhao, D., Wu, J., and Wang, L. (2022). FP-GNN: A Versatile Deep Learning Architecture for Enhanced Molecular Property Prediction. Brief. Bioinform., 23.","DOI":"10.1093\/bib\/bbac408"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1186\/s13321-023-00769-x","article-title":"DeepDelta: Predicting ADMET Improvements of Molecular Derivatives with Deep Learning","volume":"15","author":"Fralish","year":"2023","journal-title":"J. Cheminform."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"156083","DOI":"10.1016\/j.prp.2025.156083","article-title":"Integration of Machine Learning in Biomarker Discovery for Esophageal Squamous Cell Carcinoma: Applications and Future Directions","volume":"272","author":"Yang","year":"2025","journal-title":"Pathol. Res. Pract."},{"key":"ref_121","doi-asserted-by":"crossref","unstructured":"Wang, Z., Wang, S., Li, Y., Guo, J., Wei, Y., Mu, Y., Zheng, L., and Li, W. (2024). A New Paradigm for Applying Deep Learning to Protein-Ligand Interaction Prediction. Brief. Bioinform., 25.","DOI":"10.1093\/bib\/bbae145"},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Dhakal, A., McKay, C., Tanner, J.J., and Cheng, J. (2022). Artificial Intelligence in the Prediction of Protein-Ligand Interactions: Recent Advances and Future Directions. Brief. Bioinform., 23.","DOI":"10.1093\/bib\/bbab476"},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"1817","DOI":"10.1021\/acs.molpharmaceut.3c01124","article-title":"Adapting Deep Learning QSPR Models to Specific Drug Discovery Projects","volume":"21","author":"Fluetsch","year":"2024","journal-title":"Mol. Pharm."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/jb\/mvae029","article-title":"Cryo-EM Advances in GPCR Structure Determination","volume":"176","author":"Shihoya","year":"2024","journal-title":"J. Biochem."},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Bryant, P., and No\u00e9, F. (2024). Improved Protein Complex Prediction with AlphaFold-Multimer by Denoising the MSA Profile. PLoS Comput. Biol., 20.","DOI":"10.1371\/journal.pcbi.1012253"},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"108872","DOI":"10.1016\/j.jmgm.2024.108872","article-title":"Evaluation of AlphaFold 3 for the Fatty Acids Docking to Human Fatty Acid-Binding Proteins","volume":"131","author":"Nam","year":"2024","journal-title":"J. Mol. Graph. Model."},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Zhang, R., Wen, H., and Lin, Z. (2025). Artificial Intelligence-Driven Drug Toxicity Prediction: Advances, Challenges, and Future Directions. Toxics, 13.","DOI":"10.3390\/toxics13070525"},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1021\/acsphyschemau.3c00033","article-title":"Modern alchemical free energy methods for drug discovery explained","volume":"3","author":"York","year":"2023","journal-title":"ACS Phys. Chem. Au."},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"100462","DOI":"10.1016\/j.softx.2020.100462","article-title":"iScore: An MPI Supported Software for Ranking Protein-Protein Docking Models Based on a Random Walk Graph Kernel and Support Vector Machines","volume":"11","author":"Renaud","year":"2020","journal-title":"SoftwareX"},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Federico, A., Fratello, M., Scala, G., M\u00f6bus, L., Pavel, A., Del Giudice, G., Ceccarelli, M., Costa, V., Ciccodicola, A., and Fortino, V. (2022). Integrated Network Pharmacology Approach for Drug Combination Discovery: A Multi-Cancer Case Study. Cancers, 14.","DOI":"10.3390\/cancers14082043"},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"2141","DOI":"10.1016\/j.csbj.2024.05.024","article-title":"Addressing Docking Pose Selection with Structure-Based Deep Learning: Recent Advances, Challenges and Opportunities","volume":"23","author":"Vittorio","year":"2024","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"1394","DOI":"10.1038\/s41467-023-37029-3","article-title":"An Anti-HER2 Biparatopic Antibody That Induces Unique HER2 Clustering and Complement-Dependent Cytotoxicity","volume":"14","author":"Weisser","year":"2023","journal-title":"Nat. Commun."},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"Hu, Y., Li, Z., Zhao, W., Wang, H., Zhou, J., Li, X., Zhang, L., Zhao, Y., and Chen, Y. (2025). Machine Learning-Developed LKB1-AMPK Signaling-Related Signature for Prognosis and Drug Sensitivity in Hepatocellular Carcinoma. Sci. Rep., 15.","DOI":"10.1038\/s41598-025-08583-1"},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"221","DOI":"10.32604\/or.2023.028525","article-title":"Targeting triple-negative breast cancer: A clinical perspective","volume":"31","author":"Popovic","year":"2023","journal-title":"Oncol. Res."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"3003","DOI":"10.1158\/0008-5472.CAN-15-1313","article-title":"Enfortumab Vedotin Antibody-Drug Conjugate Targeting Nectin-4 Is a Highly Potent Therapeutic Agent in Multiple Preclinical Cancer Models","volume":"76","author":"Satpayev","year":"2016","journal-title":"Cancer Res."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1038\/s41596-021-00659-2","article-title":"Artificial intelligence-enabled virtual screening of ultra-large chemical libraries with deep docking","volume":"17","author":"Gentile","year":"2022","journal-title":"Nat. Protoc."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"3370","DOI":"10.1021\/acs.jcim.9b00237","article-title":"Analyzing Learned Molecular Representations for Property Prediction","volume":"59","author":"Yang","year":"2019","journal-title":"J. Chem. Inf. Model."},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1186\/s12967-022-03320-y","article-title":"Integrative Analysis of Metabolomics and Proteomics Reveals Amino Acid Metabolism Disorder in Sepsis","volume":"20","author":"Chen","year":"2022","journal-title":"J. Transl. Med."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1007\/s12553-023-00738-2","article-title":"Artificial Intelligence Applied to Clinical Trials: Opportunities and Challenges","volume":"13","author":"Askin","year":"2023","journal-title":"Health Technol."},{"key":"ref_140","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, X., Wang, Z., and Chen, H. (2025). A Comprehensive Review of Deep Learning Applications with Multi-Omics. Genes, 16.","DOI":"10.3390\/genes16060648"},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"250646","DOI":"10.1098\/rsos.250646","article-title":"Autonomous \u2018Self-Driving\u2019 Laboratories: A Review of Technology and Policy Implications","volume":"12","author":"Tobias","year":"2025","journal-title":"R. Soc. Open Sci."},{"key":"ref_142","doi-asserted-by":"crossref","unstructured":"Butakova, M.A., Chernov, A.V., Kartashov, O.O., and Soldatov, A.V. (2022). Data-Centric Architecture for Self-Driving Laboratories with Autonomous Discovery of New Nanomaterials. Nanomaterials, 12.","DOI":"10.3390\/nano12010012"},{"key":"ref_143","doi-asserted-by":"crossref","unstructured":"Li, Z., Li, J., Xu, Y., Wang, J., Zhang, H., Chen, Y., Liu, M., Zhao, X., Sun, L., and Zhou, Q. (2024). The role of artificial intelligence in drug screening, drug design, and clinical trials. Front. Pharmacol., 15.","DOI":"10.3389\/fphar.2024.1459954"},{"key":"ref_144","first-page":"276","article-title":"Advances in spatial transcriptomics and related data analysis strategies","volume":"21","author":"Li","year":"2023","journal-title":"J. Transl. Med."}],"container-title":["International Journal of Molecular Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1422-0067\/26\/21\/10744\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T09:18:46Z","timestamp":1762507126000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1422-0067\/26\/21\/10744"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,5]]},"references-count":144,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["ijms262110744"],"URL":"https:\/\/doi.org\/10.3390\/ijms262110744","relation":{},"ISSN":["1422-0067"],"issn-type":[{"value":"1422-0067","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,5]]}}}