SwiftDossier: Tailored Automatic Dossier for Drug Discovery with LLMs and Agents
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arXiv
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| Format: | Preprint |
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2024
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| author | Fossi, Gabriele Boulaimen, Youssef Outemzabet, Leila Jeanray, Nathalie Gerart, Stephane Vachenc, Sebastien Giemza, Joanna Raieli, Salvatore |
| author_facet | Fossi, Gabriele Boulaimen, Youssef Outemzabet, Leila Jeanray, Nathalie Gerart, Stephane Vachenc, Sebastien Giemza, Joanna Raieli, Salvatore |
| contents | The advancement of artificial intelligence algorithms has expanded their application to several fields such as the biomedical domain. Artificial intelligence systems, including Large Language Models (LLMs), can be particularly advantageous in drug discovery, which is a very long and expensive process. However, LLMs by themselves lack in-depth knowledge about specific domains and can generate factually incorrect information. Moreover, they are not able to perform more complex actions that imply the usage of external tools. Our work is focused on these two issues. Firstly, we show how the implementation of an advanced RAG system can help the LLM to generate more accurate answers to drug-discovery-related questions. The results show that the answers generated by the LLM with the RAG system surpass in quality the answers produced by the model without RAG. Secondly, we show how to create an automatic target dossier using LLMs and incorporating them with external tools that they can use to execute more intricate tasks to gather data such as accessing databases and executing code. The result is a production-ready target dossier containing the acquired information summarized into a PDF and a PowerPoint presentation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_15817 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SwiftDossier: Tailored Automatic Dossier for Drug Discovery with LLMs and Agents Fossi, Gabriele Boulaimen, Youssef Outemzabet, Leila Jeanray, Nathalie Gerart, Stephane Vachenc, Sebastien Giemza, Joanna Raieli, Salvatore Artificial Intelligence 68T07, 92C50, 68T09 I.2.7; J.3 The advancement of artificial intelligence algorithms has expanded their application to several fields such as the biomedical domain. Artificial intelligence systems, including Large Language Models (LLMs), can be particularly advantageous in drug discovery, which is a very long and expensive process. However, LLMs by themselves lack in-depth knowledge about specific domains and can generate factually incorrect information. Moreover, they are not able to perform more complex actions that imply the usage of external tools. Our work is focused on these two issues. Firstly, we show how the implementation of an advanced RAG system can help the LLM to generate more accurate answers to drug-discovery-related questions. The results show that the answers generated by the LLM with the RAG system surpass in quality the answers produced by the model without RAG. Secondly, we show how to create an automatic target dossier using LLMs and incorporating them with external tools that they can use to execute more intricate tasks to gather data such as accessing databases and executing code. The result is a production-ready target dossier containing the acquired information summarized into a PDF and a PowerPoint presentation. |
| title | SwiftDossier: Tailored Automatic Dossier for Drug Discovery with LLMs and Agents |
| topic | Artificial Intelligence 68T07, 92C50, 68T09 I.2.7; J.3 |
| url | https://arxiv.org/abs/2409.15817 |