Self-Attention Based Molecule Representation for Predicting Drug-Target Interaction

Bonggun Shin, Sungsoo Park, Keunsoo Kang, Joyce C. Ho
Proceedings of the 4th Machine Learning for Healthcare Conference, PMLR 106:230-248, 2019.

Abstract

Predicting drug-target interactions (DTI) is an essential part of the drug discovery process, which is an expensive process in terms of time and cost. Therefore, reducing DTI cost could lead to reduced healthcare costs for a patient. In addition, a precisely learned molecule representation in a DTI model could contribute to developing personalized medicine, which will help many patient cohorts. In this paper, we propose a new molecule representation based on the self-attention mechanism, and a new DTI model using our molecule representation. The experiments show that our DTI model outperforms the state of the art by up to 4.9% points in terms of area under the precision-recall curve. Moreover, a study using the DrugBank database proves that our model effectively lists all known drugs targeting a specific cancer biomarker in the top-30 candidate list.

Cite this Paper

BibTeX
@InProceedings{pmlr-v106-shin19a, title = {Self-Attention Based Molecule Representation for Predicting Drug-Target Interaction}, author = {Shin, Bonggun and Park, Sungsoo and Kang, Keunsoo and Ho, Joyce C.}, booktitle = {Proceedings of the 4th Machine Learning for Healthcare Conference}, pages = {230--248}, year = {2019}, editor = {Doshi-Velez, Finale and Fackler, Jim and Jung, Ken and Kale, David and Ranganath, Rajesh and Wallace, Byron and Wiens, Jenna}, volume = {106}, series = {Proceedings of Machine Learning Research}, month = {09--10 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v106/shin19a/shin19a.pdf}, url = {https://proceedings.mlr.press/v106/shin19a.html}, abstract = {Predicting drug-target interactions (DTI) is an essential part of the drug discovery process, which is an expensive process in terms of time and cost. Therefore, reducing DTI cost could lead to reduced healthcare costs for a patient. In addition, a precisely learned molecule representation in a DTI model could contribute to developing personalized medicine, which will help many patient cohorts. In this paper, we propose a new molecule representation based on the self-attention mechanism, and a new DTI model using our molecule representation. The experiments show that our DTI model outperforms the state of the art by up to 4.9% points in terms of area under the precision-recall curve. Moreover, a study using the DrugBank database proves that our model effectively lists all known drugs targeting a specific cancer biomarker in the top-30 candidate list.} }
Endnote
%0 Conference Paper %T Self-Attention Based Molecule Representation for Predicting Drug-Target Interaction %A Bonggun Shin %A Sungsoo Park %A Keunsoo Kang %A Joyce C. Ho %B Proceedings of the 4th Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2019 %E Finale Doshi-Velez %E Jim Fackler %E Ken Jung %E David Kale %E Rajesh Ranganath %E Byron Wallace %E Jenna Wiens %F pmlr-v106-shin19a %I PMLR %P 230--248 %U https://proceedings.mlr.press/v106/shin19a.html %V 106 %X Predicting drug-target interactions (DTI) is an essential part of the drug discovery process, which is an expensive process in terms of time and cost. Therefore, reducing DTI cost could lead to reduced healthcare costs for a patient. In addition, a precisely learned molecule representation in a DTI model could contribute to developing personalized medicine, which will help many patient cohorts. In this paper, we propose a new molecule representation based on the self-attention mechanism, and a new DTI model using our molecule representation. The experiments show that our DTI model outperforms the state of the art by up to 4.9% points in terms of area under the precision-recall curve. Moreover, a study using the DrugBank database proves that our model effectively lists all known drugs targeting a specific cancer biomarker in the top-30 candidate list.
APA
Shin, B., Park, S., Kang, K. & Ho, J.C.. (2019). Self-Attention Based Molecule Representation for Predicting Drug-Target Interaction. Proceedings of the 4th Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 106:230-248 Available from https://proceedings.mlr.press/v106/shin19a.html.

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