ACDB: An Antibiotic Combination DataBase

Lv, Ji; Liu, Guixia; Wang, Dong; Ju, Yuan; Sun, Ying

doi:10.3389/fphar.2022.869983

Cited by 5 publications

(3 citation statements)

References 28 publications

(36 reference statements)

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“…In the microbiology laboratory, synergy or antagonism can be determined using the checkerboard method [3,4]. However, this approach is both time-consuming and labor-intensive, making it unsuitable for high-throughput screening.…”

Section: Prediction Of Unknown Drug-drug Interactionsmentioning

confidence: 99%

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Measuring drug similarity using drug–drug interactions

Lv,

Liu,

et al. 2024

Quant. Biol.

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Combination therapy is a promising approach to address the challenge of antimicrobial resistance, and computational models have been proposed for predicting drug–drug interactions. Most existing models rely on drug similarity measures based on characteristics such as chemical structure and the mechanism of action. In this study, we focus on the network structure itself and propose a drug similarity measure based on drug–drug interaction networks. We explore the potential applications of this measure by combining it with unsupervised learning and semi‐supervised learning approaches. In unsupervised learning, drugs can be grouped based on their interactions, leading to almost monochromatic group–group interactions. In addition, drugs within the same group tend to have similar mechanisms of action (MoA). In semi‐supervised learning, the similarity measure can be utilized to construct affinity matrices, enabling the prediction of unknown drug–drug interactions. Our method exceeds existing approaches in terms of performance. Overall, our experiments demonstrate the effectiveness and practicability of the proposed similarity measure. On the one hand, when combined with clustering algorithms, it can be used for functional annotation of compounds with unknown MoA. On the other hand, when combined with semi‐supervised graph learning, it enables the prediction of unknown drug–drug interactions.

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Section: Prediction Of Unknown Drug-drug Interactionsmentioning

confidence: 99%

“…Antimicrobial resistance is a growing public health concern [1, 2], and combination therapy (i.e., antibiotic combinations) is a promising strategy to combat antibiotic resistance [3][4][5]. To date, a great number of computational models have been developed to predict antibiotic combinations [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Measuring drug similarity using drug–drug interactions

Lv,

Liu,

et al. 2024

Quant. Biol.

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“…Antimicrobial resistance has become a serious public health concern and poses a significant threat to human health. , Combination therapy (i.e., combined two or more drugs) is a promising strategy to combat antimicrobial resistance. − Compared to monotherapy, combination therapy can improve efficacy, broaden the antibacterial spectrum, , and slow down the evolution of antimicrobial resistance. , …”

Section: Introductionmentioning

confidence: 99%

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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Integrating multi-source drug information to cluster drug-drug interaction network

Liu

et al. 2023

Computers in Biology and Medicine

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ACDB: An Antibiotic Combination DataBase

Cited by 5 publications

References 28 publications

Measuring drug similarity using drug–drug interactions

Measuring drug similarity using drug–drug interactions

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

Integrating multi-source drug information to cluster drug-drug interaction network

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