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Workflows in drug-target interaction (DTI) assessment require integrating heterogeneous data from predictive models, curated resources, and observations from experimental literature.
Selective publication of antidepressant trials and its influence on apparent efficacy
Turner, E. H., Matthews, A. M., Linardatos, E., Tell, R. A. & Rosenthal, R · 2008
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Comprehensive assay of kinase catalytic activity reveals features of kinase inhibitor selectivity
Anastassiadis, T., Deacon, S. W., Devarajan, K., Ma, H. & Peterson, J. R · 2011
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Translating embeddings for modeling multi-relational data
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J. & Yakhnenko, O · 2013
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Heterogeneous network edge prediction: a data integration approach to prioritize disease-associated genes
Himmelstein, D. S. & Baranzini, S. E · 2015
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Complex embeddings for simple link prediction
Trouillon, T., Welbl, J., Riedel, S., Gaussier, É. & Bouchard, G · 2016
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Systematic integration of biomedical knowledge prioritizes drugs for repurposing
Himmelstein, D. S. et al · 2017
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Membership inference attacks against machine learning models
Shokri, R., Stronati, M., Song, C. & Shmatikov, V · 2017
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Simboost: a read-across approach for predicting drug–target binding affinities using gradient boosting machines
He, T., Heidemeyer, M., Ban, F., Cherkasov, A. & Ester, M · 2017
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Deepdta: deep drug–target binding affinity prediction
Öztürk, H., Özgür, A. & Ozkirimli, E · 2018
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Large-scale investigation of the reasons why potentially important genes are ignored
Stoeger, T., Gerlach, M., Morimoto, R. I. & Nunes Amaral, L. A · 2018
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The us federal tox21 program: A strategic and operational plan for continued leadership
Thomas, R. S. et al · 2018
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Rotate: Knowledge graph embedding by relational rotation in complex space
Sun, Z., Deng, Z.-H., Nie, J.-Y. & Tang, J · 2019
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DeepPurpose: a deep learning library for drug–target interaction prediction
Huang, K. et al · 2020
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Lewis, P. et al · 2020
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Drkg - drug repurposing knowledge graph for covid-19
Ioannidis, V. N. et al · 2020
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Emergent abilities of large language models
Wei, J. et al · 2022
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Drug–target interaction prediction based on protein features, using wrapper feature selection
Abbasi Mesrabadi, H., Faez, K. & Pirgazi, J · 2023
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Improving factuality and reasoning in language models through multiagent debate
Du, Y., Li, S., Torralba, A., Tenenbaum, J. B. & Mordatch, I · 2023
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Toolformer: Language models can teach themselves to use tools
Schick, T. et al · 2023
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Autogen: Enabling next-gen llm applications via multi-agent conversation framework
Wu, Q. et al · 2023
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Comparative toxicogenomics database (ctd): update 2023
Davis, A. P. et al · 2023
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Peacemaker or troublemaker: How sycophancy shapes multi-agent debate
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Chatgpt-5 in education: New capabilities and opportunities for teaching and learning
Choi, W. C. & Chang, C. I · 2025
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Introducing GPT-5 (2025)
OpenAI · 2025
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The berkeley function calling leaderboard (bfcl): From tool use to agentic evaluation of large language models
Patil, S. G. et al · 2025
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No free labels: Limitations of llm-as-a-judge without human grounding
Krumdick, M., Lovering, C., Reddy, V., Ebner, S. & Tanner, C · 2025
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Judging llm-as-a-judge with mt-bench and chatbot arena
Zheng, L. et al · 2023
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Faithful chain-of-thought reasoning
Lyu, Q. et al · 2023
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Faithfulness vs. plausibility: On the (un) reliability of explanations from large language models
Agarwal, C., Tanneru, S. H. & Lakkaraju, H · 2024
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phuego: a network-based method to reconstruct active signaling pathways from phosphoproteomics datasets
Giudice, G., Chen, H., Koutsandreas, T. & Petsalaki, E · 2024
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Arena learning: Build data flywheel for llms post-training via simulated chatbot arena
Luo, H. et al · 2024
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Self-rewarding language models
Yuan, W. et al · 2024
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Do membership inference attacks work on large language models?
Duan, M. et al · 2024
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Fu, Y., Uzuner, O., Yetisgen-Yildiz, M. & Xia, F · 2025
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Zhang, T. M. & Abernethy, N. F · 2025
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Bindingdb in 2024: a fair knowledgebase of protein-small molecule binding data
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Judging the judges: A systematic study of position bias in llm-as-a-judge
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Drug discovery in the era of artificial intelligence: From target identification to clinical trials
Inoue, Y., Hao, N., Lu, Y., Fu, T. & Luna, A · 2026
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Automatic reviewers fail to detect faulty reasoning in research papers: A new counterfactual evaluation framework
Dycke, N. & Gurevych, I · 2026
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A survey on llm-as-a-judge
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Holistic ai in medicine; improved performance and explainability
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