Raúl Fernández Díaz

Raúl Fernández Díaz

Bioinformatics PhD Student

Teaching

Models that learn biochemistry

Workshop, University of Oviedo, 2025

Workshop titled “Modelos que aprenden bioquímica” - Models that learn biochemistry. This workshop answers the question of what artificial intelligence is and how can it be use din biochemistry. The course spans a wide range of techniques and use cases including the underlying models behind modern chatbots like ChatGPT, and how these technologies can be applied for the modelling of biosequences and small drug-like organic molecules for drug discovery. It also included AI approaches for molecular docking (mainly Diffdock) as well as Molecular Dynamics through Machine-learnt Force Fields. The workshop provided students with a complete experience including practical sessions with guided code examples where they could build their own toxicity predictors from Chemical Language Models, as well as docking antipsychotic drugs to a GPCR, and running code for the simulation of the folding of a peptide with 15 alanines as well as the corresponding analysis of the simulation. The workshop was attended both by undergraduate students from Biology and Biotechnology majors, as well as PhD students from different disciplines.

Deep learning in biomedicine - SECUAH IX

Workshop, University of Alcala de Henares, 2024

Workshop titled “Modelos que aprenden el lenguaje de las moléculas” - Models that learn the language of molecules. The guided practical example allowed every student to finetune MolFormer-XL to build their own small molecule toxicity predictive model. Materials can be found in this Github Repository.

Demonstrator Bioinformatics UCD (MEIN30240)

Undergraduate teaching, University College Dublin, School of Medicine, 2023

I’ve been a Demonstrator in the Bioinformatics UCD Module (MEIN30240) for two years (2023 - 2024).

Deep learning in biomedicine - SECUAH VII

Workshop, University of Alcala de Henares, 2022

I’ve taught a cohort of biosciences students (undergrad and graduate) about artificial intelligence, machine learning, and deep learning techniques and how to apply them to biomedical research with a guided practical example where every student was able to build their own deep convolutional neural network for diagnosing skin lessions as either bening or cancerous. Materials can be found in this Github Repository.