İlaç - İlaç Etkileşimi Tahmini için Konvolüsyonel Sinir Ağı Tabanlı Yeni Bir Yaklaşım

Year 2023, , 137 - 144, 25.04.2023

Ramazan Özgür Doğan , Hülya Doğan , Feride Sena Sezen

https://doi.org/10.19113/sdufenbed.1182333

Abstract

Aynı anda birden fazla ilaç kullanımında özellikle son yıllarda büyük artış görülmektedir. Bu durum ilaçlar arası reaksiyon olarak tanımlanan ilaç – ilaç etkileşimlerine yol açabilmektedir. Hastalarda oluşabilecek olumsuz durumların engellenmesi için ilaçlar arasındaki etkileşimlerin tahmin edilmesi gerekmektedir. İlaç – ilaç etkileşimlerinin tahmini genelde deneyler ile gerçekleştirmekte ve yoğun iş yükü gerektirmektedir. Klinisyenlerin daha doğru kararlar alması ve uygun tedavi programları oluşturması için literatürde otomatik ilaç – ilaç etkileşimi tahmini gerçekleştiren yaklaşımlar sıklıkla gerçekleştirilmiştir. Literatürde ilaç – ilaç etkileşimi tahmini için birçok çalışma geliştirilmesine rağmen, bu alanda hala belirgin kısıtlamalar mevcuttur. İlaç – ilaç etkileşimi tahmini alanında karşılaşılan kısıtlamaları minimize etmek amacıyla bu çalışmada ilaçların yapısal özellikleri kullanılarak literatürdeki çalışmalardan daha gelişmiş konvolüsyon sinir ağı modeli önerilmektedir. Önerilen yaklaşım, özellik çıkarma ve konvolüsyon sinir ağı modelinin tasarımı olmak üzere iki ana aşamada gerçekleştirilmektedir. Çalışmada kullanılan performans değerlendirme prosedürleri açısından, önerilen yaklaşımın başarısının ilaç – ilaç etkileşimi tahmini için tatmin edici olduğu açıkça görülmektedir.

Keywords

İlaç - ilaç etkileşimi, Konvolüsyonel sinir ağları, Derin öğrenme

A Novel Convolutional Neural Network-based Approach for Prediction of Drug - Drug Interaction

Abstract

There has been a significant increase in the use of more than one drug, especially in recent years. Concomitant use of medications by a patient can lead to drug-drug interactions, which are defined as drug-to-drug reactions. In order to prevent adverse situations, it is necessary to predict the interactions that may occur between drugs. The prediction of drug-drug interactions is usually carried out with experiments and requires an intense workload. In order for clinicians to make more accurate decisions and create appropriate treatment programs, approaches that perform automatic prediction of drug-drug interaction have been frequently used in the literature. Although many studies have been developed in the literature for prediction of drug-drug interaction, there are still significant limitations in this area. In order to minimize the limitations encountered in prediction of drug-drug interaction, this study proposes a more advanced convolution neural network model than the studies in the literature, using the properties of drugs. The proposed approach is carried out in two main stages, feature extraction and design of the convolutional neural network model. In terms of results obtained with performance evaluation procedures, it is clear that the success of the proposed approach is superior to other approaches for prediction of drug-drug interaction.

Keywords

Drug – drug interaction, Convolutional neural network, Deep learning

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