Prediction of Synergistic Antibiotic Combinations by Graph Learning

Lv, Ji; Liu, Guixia; Ju, Yuan; Sun, Ying; Guo, Weiying

doi:10.3389/fphar.2022.849006

Cited by 11 publications

(13 citation statements)

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“…We attributed this to their different activity against penicillin-binding proteins . In the previous studies, we found that drugs combinations binding to different targets (e.g., penicillin-binding protein 1a, penicillin-binding protein 1b) of the same biological process (e.g., cell wall) facilitated the bypassing of redundant biological mechanisms, resulting in synergistic effects. Interestingly, we found that ciprofloxacin is classified into Cluster 3 (cell wall inhibitors dominate).…”

Section: Resultsmentioning

confidence: 90%

“…However, the method is time-consuming and labor-intensive. In recent years, with the development of high-throughput screening and artificial intelligence, researchers have developed many computational models (e.g., network-based model, , machine learning-based models , ) for predicting DDI, − and more details can be found in our previous review . In this study, we focused on unsupervised learning models.…”

Section: Introductionmentioning

confidence: 99%

“…Based on the mechanism of actions (MoAs), antibiotics may interfere with cell wall synthesis, protein synthesis, folic acid biosynthesis, etc. Combinations of cell wall synthesis inhibitors with protein synthesis inhibitors are almost synergistic . According to the phenotype, antibiotics can be either bacteriostatic or bactericidal .…”

Section: Introductionmentioning

confidence: 99%

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Identification of Robust Antibiotic Subgroups by Integrating Multi-Species Drug–Drug Interactions

Liu

et al. 2023

J. Chem. Inf. Model.

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Previous studies have shown that antibiotics can be divided into groups, and drug–drug interactions (DDI) depend on their groups. However, these studies focused on a specific bacteria strain (i.e., Escherichia coli BW25113). Existing datasets often contain noise. Noisy labeled data may have a bad effect on the clustering results. To address this problem, we developed a multi-source information fusion method for integrating DDI information from multiple bacterial strains. Specifically, we calculated drug similarities based on the DDI network of each bacterial strain and then fused these drug similarity matrices to obtain a new fused similarity matrix. The fused similarity matrix was combined with the T-distributed stochastic neighbor embedding algorithm, and hierarchical clustering algorithm can effectively identify antibiotic subgroups. These antibiotic subgroups are strongly correlated with known antibiotic classifications, and group–group interactions are almost monochromatic. In summary, our method provides a promising framework for understanding the mechanism of action of antibiotics and exploring multi-species group–group interactions.

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Section: Resultsmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

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Identification of Robust Antibiotic Subgroups by Integrating Multi-Species Drug–Drug Interactions

Liu

et al. 2023

J. Chem. Inf. Model.

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“…Additionally, the employment of transformers and pre-trained models [62,63], alongside the utilization of propagative network architectures [64], is prevalent for the purpose of feature extraction. It is important to note that the size of the dataset and careful adjustment of model parameters are crucial when using these methods.…”

Section: Feature Extractionmentioning

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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“…Zou et al (Zou et al, 2012) used these topological parameters to explore the underlying mechanisms of drug combinations. Networkbased proximity (Cheng et al, 2019;Lv et al, 2022) can also be used to measure the relationship of two drugs. Based on the ACDB, comprehensive studies of antibiotic combinations at the system level can be undertaken.…”

Section: Investigate Mechanisms Of Antibiotic Combinations Based On N...mentioning

confidence: 99%