RAG System Application for Supporting Drug Discovery and Docking Molecular Processes Contribución en una conferencia

Nombre del libro

Artificial Intelligence and Knowledge Processing - 5th International Conference, Proceedings

editorial

Springer Science and Business Media Deutschland GmbH

Thesis author

Alexander Rodriguez-Lopez
Julián David Forero

Resumen

This work presents the design and validation of a semantic retrieval and natural language generation pipeline aimed at supporting molecular docking studies through compound recommendation and scientific literature contextualization. The pipeline integrates biomedical data processing, semantic indexing with FAISS, and language modeling using BioBERT and TinyLlama-1.1B-Chat to generate concise and informative responses. A curated scientific corpus was built from sources such as PubMed, and embeddings were generated to enable semantic similarity searches. The system was tested using ellagic acid as a query compound, successfully retrieving relevant literature and presenting detailed chemical information from the COCONUT database. In parallel, this research aligns with ongoing efforts from the Semill-IAS research group at Universidad del Rosario, which focuses on inclusive health technologies, particularly the use of artificial intelligence for different health contexts. The proposed solution demonstrates the potential of combining machine learning models with biomedical informatics for accessible, data-driven decision support in health and drug discovery domains.

autores

http://vivoweb.org/ontology/core#FacultyMember
Orjuela Cañón, Alvaro David

fecha de publicación

2026-1-1

Área temática

Salud y bienestar

Identificador de objeto digital (DOI)

https://doi.org/10.1007/978-3-032-14706-6_5

Número Estándar Internacional de Libros (ISBN)

9783032147059

Número de páginas

11

Página inicial

53

Última página

63