SimVec: predicting polypharmacy side effects for new drugs
Abstract Polypharmacy refers to the administration of multiple drugs on a daily basis. It has demonstrated effectiveness in treating many complex diseases , but it has a higher risk of adverse drug reactions. Hence, the prediction of polypharmacy side effects is an essential step in drug testing, es...
Main Authors: | , , , , |
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Format: | Article |
Language: | English |
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BMC
2022-07-01
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Series: | Journal of Cheminformatics |
Subjects: | |
Online Access: | https://doi.org/10.1186/s13321-022-00632-5 |
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author | Nina Lukashina Elena Kartysheva Ola Spjuth Elizaveta Virko Aleksei Shpilman |
author_facet | Nina Lukashina Elena Kartysheva Ola Spjuth Elizaveta Virko Aleksei Shpilman |
author_sort | Nina Lukashina |
collection | DOAJ |
description | Abstract Polypharmacy refers to the administration of multiple drugs on a daily basis. It has demonstrated effectiveness in treating many complex diseases , but it has a higher risk of adverse drug reactions. Hence, the prediction of polypharmacy side effects is an essential step in drug testing, especially for new drugs. This paper shows that the current knowledge graph (KG) based state-of-the-art approach to polypharmacy side effect prediction does not work well for new drugs, as they have a low number of known connections in the KG. We propose a new method , SimVec, that solves this problem by enhancing the KG structure with a structure-aware node initialization and weighted drug similarity edges. We also devise a new 3-step learning process, which iteratively updates node embeddings related to side effects edges, similarity edges, and drugs with limited knowledge. Our model significantly outperforms existing KG-based models. Additionally, we examine the problem of negative relations generation and show that the cache-based approach works best for polypharmacy tasks. |
first_indexed | 2024-12-11T16:37:16Z |
format | Article |
id | doaj.art-cadb8a5fef624a46a15229636f5c730a |
institution | Directory Open Access Journal |
issn | 1758-2946 |
language | English |
last_indexed | 2024-12-11T16:37:16Z |
publishDate | 2022-07-01 |
publisher | BMC |
record_format | Article |
series | Journal of Cheminformatics |
spelling | doaj.art-cadb8a5fef624a46a15229636f5c730a2022-12-22T00:58:26ZengBMCJournal of Cheminformatics1758-29462022-07-0114111210.1186/s13321-022-00632-5SimVec: predicting polypharmacy side effects for new drugsNina Lukashina0Elena Kartysheva1Ola Spjuth2Elizaveta Virko3Aleksei Shpilman4AI Labs, JetBrains ResearchAI Labs, JetBrains ResearchDepartment of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala UniversityData Analytics Team, JetBrainsAI Labs, JetBrains ResearchAbstract Polypharmacy refers to the administration of multiple drugs on a daily basis. It has demonstrated effectiveness in treating many complex diseases , but it has a higher risk of adverse drug reactions. Hence, the prediction of polypharmacy side effects is an essential step in drug testing, especially for new drugs. This paper shows that the current knowledge graph (KG) based state-of-the-art approach to polypharmacy side effect prediction does not work well for new drugs, as they have a low number of known connections in the KG. We propose a new method , SimVec, that solves this problem by enhancing the KG structure with a structure-aware node initialization and weighted drug similarity edges. We also devise a new 3-step learning process, which iteratively updates node embeddings related to side effects edges, similarity edges, and drugs with limited knowledge. Our model significantly outperforms existing KG-based models. Additionally, we examine the problem of negative relations generation and show that the cache-based approach works best for polypharmacy tasks.https://doi.org/10.1186/s13321-022-00632-5PolypharmacyKnowledge graph |
spellingShingle | Nina Lukashina Elena Kartysheva Ola Spjuth Elizaveta Virko Aleksei Shpilman SimVec: predicting polypharmacy side effects for new drugs Journal of Cheminformatics Polypharmacy Knowledge graph |
title | SimVec: predicting polypharmacy side effects for new drugs |
title_full | SimVec: predicting polypharmacy side effects for new drugs |
title_fullStr | SimVec: predicting polypharmacy side effects for new drugs |
title_full_unstemmed | SimVec: predicting polypharmacy side effects for new drugs |
title_short | SimVec: predicting polypharmacy side effects for new drugs |
title_sort | simvec predicting polypharmacy side effects for new drugs |
topic | Polypharmacy Knowledge graph |
url | https://doi.org/10.1186/s13321-022-00632-5 |
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