Attention-based method to predict drug-target interactions across seven protein classes

Thumbnail Image

Files

master_Schulman_Aron_2024.pdf (7.28 MB)

URL

Journal Title

Journal ISSN

Volume Title

Perustieteiden korkeakoulu | Master's thesis
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.

Authors

Date

2024-01-22

Department

Major/Subject

Bioinformatics and Digital Health

Mcode

SCI3092

Degree programme

Master’s Programme in Life Science Technologies

Language

en

Pages

64

Series

Abstract

Most approved drugs bind with proteins to modulate their activity for treating a diverse range of diseases. Unfortunately, drug development is a long and costly process. Computational methods seek to accelerate drug discovery by predicting drug-target interactions, thus facilitating compound screening and drug repurposing. This thesis presents a deep learning approach for predicting interactions between compounds and proteins categorized into seven classes. The models utilize self-attention found in transformer neural networks to learn continuous interaction values from multimodal compound-protein representations. The models were evaluated in three test settings of increasing difficulty. Most models showed competitive predictive capabilities in the two easier settings, with the most difficult test rendering them ineffective. In particular, the kinase model demonstrated state-of-the-art performance in the bioactivity imputation task when compared against other methods, while leaving room for improvement in the new compound scenario. Furthermore, conformal predictors with uncertainty estimates displayed equivalent performance to contemporary methods and provided directions for future research.

Description

Supervisor

Rousu, Juho

Thesis advisor

Tanoli, Ziaurrehman

Keywords

drug discovery, drug-target interaction prediction, deep learning, transformer neural networks, self-attention, conformal prediction

Other note

Citation

Permanent link to this item

https://urn.fi/URN:NBN:fi:aalto-202401282133

Collections

[dipl] Perustieteiden korkeakoulu / SCI