HSTrans: Homogeneous substructures transformer for predicting frequencies of drug-side effects.

Autor: Xu K; School of Computer Science, China University of Geosciences, Wuhan 430074, China., Wang M; Department of Pharmacy, Lianshui People's Hospital Affiliated to Kangda College of Nanjing Medical University, Huai'an 223300, China., Zou X; School of Computer Science, China University of Geosciences, Wuhan 430074, China., Liu J; Department of Cardiac Surgery, Tianjin Chest Hospital, Tianjin 300222, China., Wei A; Department of Cardiology, Tianjin Chest Hospital, Tianjin 300222, China., Chen J; Department of Pharmacy, The Affiliated Huai'an Hospital of Xuzhou Medical University and The Second People's Hospital of Huai'an, Huai'an 223002, China. Electronic address: jjiachen@outlook.com., Tang C; School of Computer Science, China University of Geosciences, Wuhan 430074, China. Electronic address: tangchang@cug.edu.cn.
Jazyk: angličtina
Zdroj: Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2025 Jan; Vol. 181, pp. 106779. Date of Electronic Publication: 2024 Oct 23.
DOI: 10.1016/j.neunet.2024.106779
Abstrakt: Identifying the frequencies of drug-side effects is crucial for assessing drug risk-benefit. However, accurately determining these frequencies remains challenging due to the limitations of time and scale in clinical randomized controlled trials. As a result, several computational methods have been proposed to address these issues. Nonetheless, two primary problems still persist. Firstly, most of these methods face challenges in generating accurate predictions for novel drugs, as they heavily depend on the interaction graph between drugs and side effects (SEs) within their modeling framework. Secondly, some previous methods often simply concatenate the features of drugs and SEs, which fails to effectively capture their underlying association. In this work, we present HSTrans, a novel approach that treats drugs and SEs as sets of substructures, leveraging a transformer encoder for unified substructure embedding and incorporating an interaction module for association capture. Specifically, HSTrans extracts drug substructures through a specialized algorithm and identifies effective substructures for each SE by employing an indicator that measures the importance of each substructure and SE. Additionally, HSTrans applies convolutional neural network (CNN) in the interaction module to capture complex relationships between drugs and SEs. Experimental results on datasets from Galeano et al.'s study demonstrate that the proposed method outperforms other state-of-the-art approaches. The demo codes for HSTrans are available at https://github.com/Dtdtxuky/HSTrans/tree/master.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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