DT-ML: Drug-Target Metric Learning

Domonkos Pogány, Péter Antal

2023

Abstract

The challenges of modern drug discovery motivate the use of machine learning-based methods, such as predicting drug-target interactions or novel indications for already approved drugs to speed up the early discovery or repositioning process. Publication bias has resulted in a shortage of known negative data points in large-scale repositioning datasets. However, training a good predictor requires both positive and negative samples. The problem of negative sampling has also recently been addressed in subfields of machine learning with utmost importance, namely in representation and metric learning. Although these novel negative sampling approaches proved to be efficient solutions for learning from imbalanced data sets, they have not yet been used in repositioning in such a way that the learned similarities give the predicted interactions. In this paper, we adapt representation learning-inspired methods in pairwise drug-target/drug-disease predictors and propose a modification to one of the loss functions to better manage the uncertainty of negative samples. We evaluate the methods using benchmark drug discovery and repositioning data sets. Results indicate that interaction prediction with metric learning is superior to previous approaches in highly imbalanced scenarios, such as drug repositioning.

Paper Citation

in Harvard Style

Pogány D. and Antal P. (2023). DT-ML: Drug-Target Metric Learning. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 3: BIOINFORMATICS; ISBN 978-989-758-631-6, SciTePress, pages 204-211. DOI: 10.5220/0011691100003414

in Bibtex Style

@conference{bioinformatics23,
author={Domonkos Pogány and Péter Antal},
title={DT-ML: Drug-Target Metric Learning},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 3: BIOINFORMATICS},
year={2023},
pages={204-211},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011691100003414},
isbn={978-989-758-631-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 3: BIOINFORMATICS
TI - DT-ML: Drug-Target Metric Learning
SN - 978-989-758-631-6
AU - Pogány D.
AU - Antal P.
PY - 2023
SP - 204
EP - 211
DO - 10.5220/0011691100003414
PB - SciTePress