From Small Molecules to microRNA Networks: Rethinking Drug Discovery with AI
Speaker: Diego Galeano, Universidad Nacional de Asunción (FIUNA), Paraguay
This seminar will present my research on applying machine learning to bridge molecular mechanisms and clinical outcomes in drug discovery. I will introduce sChemNET, a neural network framework designed to predict small-molecule modulators of microRNA bioactivity, enabling network-level therapeutic discovery beyond single-protein targeting. I will also discuss the use of large language model (LLM) architectures, including Retrieval-Augmented Generation (RAG) and GraphRAG, to improve biomedical information retrieval and reasoning tasks using drug side effect data as proof of concept. Together, these approaches demonstrate how machine learning can capture complex biological relationships and enhance explainability in pharmacological data, paving the way for safer and more effective therapeutics.
Event Details
- Date: November 17, 2025
- Time: 11:30 a.m. – 12:30 p.m.
- Location: BST3 6014
About the Speaker
Dr. Diego Galeano is a Machine Learning Researcher at the Faculty of Engineering, Universidad Nacional de Asunción (FIUNA), Paraguay and co-founder of Tesabio.ai. He obtained his Ph.D. in Computer Science from Royal Holloway, University of London (RHUL). He was research fellow at Yale University and Fundação Getulio Vargas, where he worked on machine learning applications in biomedicine. His current research focuses on developing computational frameworks for drug discovery and precision medicine, including predicting drug side effects, modeling microRNA–small molecule interactions, and designing AI systems for pharmacological countermeasures against the biological effects of space radiation.