A Drug Combination Prediction Framework Based on Graph Convolutional Network and Heterogeneous Information

Chen, Hegang; Lu, Yuyin; Yang, Yuedong; Rao, Yanghui

doi:10.1109/tcbb.2022.3224734

Cited by 8 publications

(2 citation statements)

References 40 publications

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“…Chen et al [70] proposed a novel computational pipeline called DCMGCN for predicting drug combinations. The pipeline integrates diverse drug-related information to learn low-dimensional representations of drugs from attributes and similarity networks.…”

Section: Drug Combination Prediction Based On Clinical Studiesmentioning

confidence: 99%

A Review on Graph Neural Networks for Predicting Synergistic Drug Combinations

Besharatifard,

Vafaee

2023

Preprint

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Combinational therapies with synergistic effects provide a powerful treatment strategy for tackling complex diseases, particularly malignancies. Discovering these synergistic combinations, often involving various compounds and structures, necessitates exploring a vast array of compound pairings. However, practical constraints such as cost, feasibility, and complexity hinder exhaustive in vivo and in vitro experimentation. In recent years, machine learning methods have made significant inroads in pharmacology. Among these, Graph Neural Networks (GNNs) have gained increasing attention in drug discovery due to their ability to represent complex molecular structures as networks, capture vital structural information, and seamlessly handle diverse data types. This review aims to provide a comprehensive overview of various GNN models developed for predicting effective drug combinations, examining the limitations and strengths of different models, and comparing their predictive performance. Additionally, we discuss the databases used for drug synergism prediction and the extraction of drug-related information as predictive features. By summarizing the state-of-the-art GNN-driven drug combination prediction, this review aims to offer valuable insights into the promising field of computational pharmacotherapy.

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Section: Drug Combination Prediction Based On Clinical Studiesmentioning

confidence: 99%

A Review on Graph Neural Networks for Predicting Synergistic Drug Combinations

Besharatifard,

Vafaee

2023

Preprint

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“…Using a range of machine learning techniques, several research papers have explored the field of pharmacological synergy prediction. To predict possible drug combinations, Chen et al 11 presented a unique computational framework that incorporates diverse drug-related data. Their methodology is evidence of how machine learning may be used to handle intricate interactions in the pharmaceutical industry.…”

Section: Introductionmentioning

confidence: 99%

SynSimPred: Leveraging Cell Line Similarities for Drug Combination Synergy Prediction

Zakeri,

Meybodi,

Eslahchi

2024

Preprint

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Cancer treatment faces significant challenges, necessitating innovative approaches to predict and enhance drug efficacy. Computational models, particularly advanced machine learning methods, have shown promise in customizing drug response predictions and improving patient outcomes. However, predicting synergistic drug combinations remains complex due to the vast number of potential interactions and the limitations of both data availability and traditional research methodologies. In response, our study introduces SynSimPred, a computational approach that leverages cell line similarities, gene expression, mutations, dependencies, and copy number variations to predict drug synergy scores. SynSimPred employs a suite of sophisticated regression methodologies to navigate the complex interplay between these biological characteristics and their impact on drug efficacy. A key component of our research involved a detailed case study where SynSimPred predicted the synergistic and antagonistic interactions of drug pairs such as Vinblastine and Veliparib, and Deforolimus and Dactolisib, demonstrating its practical utility and accuracy in real-world scenarios. We rigorously evaluated SynSimPred against leading methods including DeepSynergy, HypergraphSynergy, and SynPred, across two comprehensive datasets, O'NEIL and ALMANAC. Our evaluations, using metrics such as MSE, Precision, F-Measure, and Accuracy, demonstrate that SynSimPred consistently outperforms existing models, establishing it as a frontrunner in the field of predictive methodologies for cancer treatment.

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Self-pro: A Self-prompt and Tuning Framework for Graph Neural Networks

Gong,

Li,

et al. 2024

Lecture Notes in Computer Science

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A Drug Combination Prediction Framework Based on Graph Convolutional Network and Heterogeneous Information

Cited by 8 publications

References 40 publications

A Review on Graph Neural Networks for Predicting Synergistic Drug Combinations

A Review on Graph Neural Networks for Predicting Synergistic Drug Combinations

SynSimPred: Leveraging Cell Line Similarities for Drug Combination Synergy Prediction

Self-pro: A Self-prompt and Tuning Framework for Graph Neural Networks

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