LLM Agent Swarm for Hypothesis-Driven Drug Discovery

Kanehisa M, Goto S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Research . 2000;28(1):27–30

2000

Earlier work this paper cites.

Edgar R, Domrachev M, Lash AE. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Research . 2002;30(1):207–210

2002

Earlier work this paper cites.

Köster J, Rahmann S. Snakemake—a scalable bioinformatics workflow engine. Bioinformatics . 2012;28(19):2520–2522

2012

Earlier work this paper cites.

Waring MJ, Arrowsmith J, Leach AR, et al. An analysis of the attrition of drug candidates from four major pharmaceutical companies. Nature Reviews Drug Discovery . 2015;14(7):475–486

2015

Earlier work this paper cites.

Gaulton A, Hersey A, Nowotka M, et al. The ChEMBL database in 2017. Nucleic Acids Research . 2017;45(D1):D945–D954

2017

Earlier work this paper cites.

Apache Airflow. 2017. https://airflow.apache.org/

2017

Earlier work this paper cites.

Di Tommaso P, et al. Nextflow enables reproducible computational workflows. Nature Biotechnology . 2017;35(4):316–319

2017

Earlier work this paper cites.

Wishart DS, et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Research . 2018;46(D1):D1074–D1082

2018

Earlier work this paper cites.

Pushpakom S, Iorio F, Eyers PA, et al. Drug repurposing: progress, challenges and recommendations. Nature Reviews Drug Discovery . 2019;18:41–58

2019

Earlier work this paper cites.

Segler MHS, Preuss M, Waller M. Planning chemical syntheses with deep neural networks and symbolic AI. Nature . 2020;555:604–610

2020

Cited alongside, same era.

Sosa DN, Derry A, Guo M, Wei E, Brinton C, Altman RB. A literature-based knowledge graph embedding method for identifying drug repurposing opportunities in rare diseases. Pacific Symposium on Biocomputing . 2020:589–600

2020

Cited alongside, same era.

Jassal B, Matthews L, Viteri G, et al. The Reactome pathway knowledgebase. Nucleic Acids Research . 2020;48(D1):D498–D503

2020

Cited alongside, same era.

Bommasani R, Hudson D, Adeli E, et al. On the Opportunities and Risks of Foundation Models. arXiv:2108.07258. 2021

Original

2021

Cited alongside, same era.

Rives A, Meier J, Sercu T, et al. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proceedings of the National Academy of Sciences . 2021;118(15):e2016239118

Nguyen T, et al. Agent-based climate modeling with language models. Geophysical Research Letters . 2023;50(10):e2022GL100123

2023

Later among the works it cites.

Wang Y, Jiang L, Zhou M, Smith NA. Evaluating multi-agent collaboration in large language models. In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (ACL) . 2023:2334–2346

2023

Later among the works it cites.

Ochoa D, Hercules A, Carmona M, et al. The Open Targets Platform: Re-imagining drug target identification. Nucleic Acids Research . 2023;51(D1):D1305–D1316

2023

Later among the works it cites.

Prefect. 2023. https://www.prefect.io/

2023

Later among the works it cites.

Kubeflow Pipelines. 2023. https://www.kubeflow.org/

2023

Later among the works it cites.