AIMC Topic: Anti-Bacterial Agents

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Biodegradation of ciprofloxacin using machine learning tools: Kinetics and modelling.

Journal of hazardous materials
Recently, the rampant administration of antibiotics and their synthetic organic constitutes have exacerbated adverse effects on ecosystems, affecting the health of animals, plants, and humans by promoting the emergence of extreme multidrug-resistant ...

A machine learning approach to predict daptomycin exposure from two concentrations based on Monte Carlo simulations.

Antimicrobial agents and chemotherapy
Daptomycin is a concentration-dependent lipopeptide antibiotic for which exposure/effect relationships have been shown. Machine learning (ML) algorithms, developed to predict the individual exposure to drugs, have shown very good performances in comp...

Determining steady-state trough range in vancomycin drug dosing using machine learning.

Journal of critical care
BACKGROUND: Vancomycin is a renally eliminated, nephrotoxic, glycopeptide antibiotic with a narrow therapeutic window, widely used in intensive care units (ICU). We aimed to predict the risk of inappropriate vancomycin trough levels and appropriate d...

Improving the performance of machine learning penicillin adverse drug reaction classification with synthetic data and transfer learning.

Internal medicine journal
BACKGROUND: Machine learning may assist with the identification of potentially inappropriate penicillin allergy labels. Strategies to improve the performance of existing models for this task include the use of additional training data, synthetic data...

Waste to resource: Mining antimicrobial peptides in sludge from metagenomes using machine learning.

Environment international
The emergence of antibiotic-resistant bacteria poses a huge threat to the treatment of infections. Antimicrobial peptides are a class of short peptides that widely exist in organisms and are considered as potential substitutes for traditional antibio...

Chemical multiscale robotics for bacterial biofilm treatment.

Chemical Society reviews
A biofilm constitutes a bacterial community encased in a sticky matrix of extracellular polymeric substances. These intricate microbial communities adhere to various host surfaces such as hard and soft tissues as well as indwelling medical devices. T...

Machine learning and genetic algorithm-guided directed evolution for the development of antimicrobial peptides.

Journal of advanced research
INTRODUCTION: Antimicrobial peptides (AMPs) are valuable alternatives to traditional antibiotics, possess a variety of potent biological activities and exhibit immunomodulatory effects that alleviate difficult-to-treat infections. Clarifying the stru...

Antibiotic combinations prediction based on machine learning to multicentre clinical data and drug interaction correlation.

International journal of antimicrobial agents
BACKGROUND: With increasing antibiotic resistance and regulation, the issue of antibiotic combination has been emphasised. However, antibiotic combination prescribing lacks a rapid identification of feasibility, while its risk of drug interactions is...

Deep-learning image analysis for high-throughput screening of opsono-phagocytosis-promoting monoclonal antibodies against Neisseria gonorrhoeae.

Scientific reports
Antimicrobial resistance (AMR) is nowadays a global health concern as bacterial pathogens are increasingly developing resistance to antibiotics. Monoclonal antibodies (mAbs) represent a powerful tool for addressing AMR thanks to their high specificit...

EnAMP: A novel deep learning ensemble antibacterial peptide recognition algorithm based on multi-features.

Journal of bioinformatics and computational biology
Antimicrobial peptides (AMPs), as the preferred alternatives to antibiotics, have wide application with good prospects. Identifying AMPs through wet lab experiments remains expensive, time-consuming and challenging. Many machine learning methods have...