SMVSNN: An Intelligent Framework for Anticancer Drug-Drug Interaction Prediction Utilizing Spiking Multi-view Siamese Neural Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3AKDP935XD" target="_blank" >RIV/00216208:11320/26:KDP935XD - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1021/acs.jcim.4c02205" target="_blank" >http://dx.doi.org/10.1021/acs.jcim.4c02205</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1021/acs.jcim.4c02205" target="_blank" >10.1021/acs.jcim.4c02205</a>

Jazyk výsledku

angličtina

Název v původním jazyce

SMVSNN: An Intelligent Framework for Anticancer Drug-Drug Interaction Prediction Utilizing Spiking Multi-view Siamese Neural Networks

Popis výsledku v původním jazyce

The study of synergistic drug combinations is vital in cancer treatment, enhancing efficacy, reducing resistance, and minimizing side effects through complementary drug actions. Drug-drug interaction (DDI) analysis offers essential theoretical support, and with the rise of data science, intelligent algorithms are increasingly replacing traditional in vitro screening for predicting potential DDIs. Considering the limitations of previous computational methods, such as the application of a single view, overly direct concatenation of drug pair features, and existing data encoding that is difficult to handle, this paper proposes a novel DDI analysis and prediction framework, called the Spiking Multi-View Siamese Neural Network-based (SMVSNN) framework. First, the data of two drugs in each view are processed into fused features using a Siamese spiking convolutional network and a spiking neural perceptron. Second, the processed features from multiple views are integrated into a unified representation through a self-learning attention weight module. Finally, this unified representation is fed into a spiking multilayer perceptron network to obtain the prediction results. Compared to traditional intelligent algorithms, the spiking neurons and the siamese network in SMVSNN can more effectively extract and integrate latent information from drug pair data. Real anticancer drug data, including 904 drugs, 7730 DDI records, and 19 drug interactions, were extracted from authoritative public databases to assess the effectiveness of our framework. The 5-fold cross-validation indicates that SMVSNN outperforms previous models on the majority of metrics. SMVSNN is poised to be an effective method for inferring potential synergistic drug combinations in anticancer therapy. © 2025 American Chemical Society.

Název v anglickém jazyce

SMVSNN: An Intelligent Framework for Anticancer Drug-Drug Interaction Prediction Utilizing Spiking Multi-view Siamese Neural Networks

Popis výsledku anglicky

The study of synergistic drug combinations is vital in cancer treatment, enhancing efficacy, reducing resistance, and minimizing side effects through complementary drug actions. Drug-drug interaction (DDI) analysis offers essential theoretical support, and with the rise of data science, intelligent algorithms are increasingly replacing traditional in vitro screening for predicting potential DDIs. Considering the limitations of previous computational methods, such as the application of a single view, overly direct concatenation of drug pair features, and existing data encoding that is difficult to handle, this paper proposes a novel DDI analysis and prediction framework, called the Spiking Multi-View Siamese Neural Network-based (SMVSNN) framework. First, the data of two drugs in each view are processed into fused features using a Siamese spiking convolutional network and a spiking neural perceptron. Second, the processed features from multiple views are integrated into a unified representation through a self-learning attention weight module. Finally, this unified representation is fed into a spiking multilayer perceptron network to obtain the prediction results. Compared to traditional intelligent algorithms, the spiking neurons and the siamese network in SMVSNN can more effectively extract and integrate latent information from drug pair data. Real anticancer drug data, including 904 drugs, 7730 DDI records, and 19 drug interactions, were extracted from authoritative public databases to assess the effectiveness of our framework. The 5-fold cross-validation indicates that SMVSNN outperforms previous models on the majority of metrics. SMVSNN is poised to be an effective method for inferring potential synergistic drug combinations in anticancer therapy. © 2025 American Chemical Society.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

—

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Chemical Information and Modeling

ISSN

1549-9596

e-ISSN

—

Svazek periodika

65

Číslo periodika v rámci svazku

11

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

15

Strana od-do

5730-5744

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-105005791627