The chemical design of antimicrobial ionic liquids guided by machine learning: a review on balancing efficacy and toxicity

Zheng Liu; Qiuyu Chen; Congfei Yao; Yimin Wang; Siqi Zhu; Jinyi Guan; Xin Hu

doi:10.1039/d5tb02581h

January 1, 2026Journal of Materials Chemistry B

The chemical design of antimicrobial ionic liquids guided by machine learning: a review on balancing efficacy and toxicity

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Key Points

This review aims to explore the development of antimicrobial ionic liquids while addressing efficacy and toxicity concerns.
Reviewed existing literature on ionic liquids and their antimicrobial properties
Analyzed machine learning approaches for optimizing chemical design
Evaluated strategies to balance efficacy and toxicity in antimicrobial agents
Identified key chemical properties that influence antimicrobial efficacy
Discussed various machine learning algorithms applicable to ionic liquid design
Highlighted the importance of balancing antimicrobial efficacy with potential toxicity

Implication

Review uncovers strategies to balance effectiveness and safety in antimicrobial ionic liquids, suggesting new design approaches.

Abstract

The design of new antimicrobial materials is a critical challenge in chemistry and materials science, driven by the global threat of multidrug-resistant pathogens. Ionic Liquids (ILs) represent a highly versatile...

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Cite This Study

Liu et al. (2026) studied this question.

synapsesocial.com/papers/6980fc55c1c9540dea80e290 https://doi.org/10.1039/d5tb02581h

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The chemical design of antimicrobial ionic liquids guided by machine learning: a review on balancing efficacy and toxicity

Key Points

Implication

Abstract

Discussion

Cite This Study

The chemical design of antimicrobial ionic liquids guided by machine learning: a review on balancing efficacy and toxicity

Key Points

Implication

Abstract

Discussion

Cite This Study

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