AIMC Topic: Anti-Bacterial Agents

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Machine learning for predicting antimicrobial resistance in critical and high-priority pathogens: A systematic review considering antimicrobial susceptibility tests in real-world healthcare settings.

PloS one
BACKGROUND: Antimicrobial resistance (AMR) poses a worldwide health threat; quick and accurate identification of AMR enhances patient outcomes and reduces inappropriate antibiotic usage. The objective of this systematic review is to evaluate the effi...

A machine learning method for predicting molecular antimicrobial activity.

Scientific reports
In response to the increasing concern over antibiotic resistance and the limitations of traditional methods in antibiotic discovery, we introduce a machine learning-based method named MFAGCN. This method predicts the antimicrobial efficacy of molecul...

Predicting Antimicrobial Class Specificity of Small Molecules Using Machine Learning.

Journal of chemical information and modeling
While the useful armory of antibiotic drugs is continually depleted due to the emergence of drug-resistant pathogens, the development of novel therapeutics has also slowed down. In the era of advanced computational methods, approaches like machine le...

Machine learning detection of heteroresistance in Escherichia coli.

EBioMedicine
BACKGROUND: Heteroresistance (HR) is a significant type of antibiotic resistance observed for several bacterial species and antibiotic classes where a susceptible main population contains small subpopulations of resistant cells. Mathematical models, ...

[Validation of a decision tree for selective dry cow therapy of dairy for a digital expert system].

Tierarztliche Praxis. Ausgabe G, Grosstiere/Nutztiere
In this study, a decision tree derived from scientific literature on selective dry cow therapy (ST), which was developed as a knowledge base for a digital expert system, was evaluated. The decision tree merges algorithmic (based on cell count results...

Machine learning and cheminformatics-based Identification of lichen-derived compounds targeting mutant PBP4 in Staphylococcus aureus.

Molecular diversity
Penicillin-binding protein 4 (PBP4) is essential in imparting significant β-lactam antibiotics resistance in Staphylococcus aureus (S. aureus) and the mutation R200L in PBP4 is linked to β-lactam non-susceptibility in natural strains, complicating tr...

Artificial intelligence-driven approaches in antibiotic stewardship programs and optimizing prescription practices: A systematic review.

Artificial intelligence in medicine
Antimicrobial stewardship programs (ASPs) are essential in optimizing the use of antibiotics to address the global concern of antimicrobial resistance (AMR). Artificial intelligence (AI) and machine learning (ML) have emerged as promising tools for e...

Integration of machine learning and meta-analysis reveals the behaviors and mechanisms of antibiotic adsorption on microplastics.

Journal of hazardous materials
Microplastics (MPs) can adsorb antibiotics (ATs) to cause combined pollution in the environment. Research on this topic has been limited to specific types of MPs and ATs, resulting in inconsistent findings, particularly for the influencing factors an...

MSCMamba: Prediction of Antimicrobial Peptide Activity Values by Fusing Multiscale Convolution with Mamba Module.

The journal of physical chemistry. B
Antimicrobial peptides (AMPs) have important developmental prospects as potential candidates for novel antibiotics. Although many studies have been devoted to the identification of AMPs and the qualitative prediction of their functional activities, f...

Artificial intelligence using a latent diffusion model enables the generation of diverse and potent antimicrobial peptides.

Science advances
Artificial intelligence holds great promise for the design of antimicrobial peptides (AMPs); however, current models face limitations in generating AMPs with sufficient novelty and diversity, and they are rarely applied to the generation of antifunga...