CASALS, M. "Machine learning methods for drug repurposing". Ochoa, I. (dir.). Trabajo fin de grado. Univaersidad de Navarra, Pamplona, 2022
Drug Repurposing consists on using already approved drugs to treat other diseases. This
is done by identifying new targets that the drug may have. To accelerate this long and
costly process, computational methods have been developed to predict drug-target interactions
Recently, Machine Learning has had a tremendous impact on many scientific fields including
DTI prediction. In this project, two state-of-the-art methods named MolTrans and Hyper-
AttentionDTI are described and compared on 8 different datasets. Moreover, each dataset is
divided according to 3 different splits, so that the generalization of the methods with respect
to drugs and proteins is tested.
Graphs are a type of data structures that have nodes and edges connecting them. They can
model complex systems accurately such as DTI networks. In recent years, Graph Machine
Learning methods have been developed to improve on conventional models. Node embedding
techniques and Graph Neural Networks are introduced as ways to transform nodes into vector
embeddings to be able to make predictions on them.
While the two analyzed methods offer competitive results, Graph Machine Learning can take
advantage of the expressiveness that graphs have in order to make more accurate predictions.