AI Medical Compendium Topic

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Anti-Bacterial Agents

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Machine Learning Platform to Discover Novel Growth Inhibitors of Neisseria gonorrhoeae.

Pharmaceutical research
PURPOSE: To advance fundamental biological and translational research with the bacterium Neisseria gonorrhoeae through the prediction of novel small molecule growth inhibitors via naïve Bayesian modeling methodology.

Applications of machine-learning methods for the discovery of NDM-1 inhibitors.

Chemical biology & drug design
The emergence of New Delhi metal beta-lactamase (NDM-1)-producing bacteria and their worldwide spread pose great challenges for the treatment of drug-resistant bacterial infections. These bacteria can hydrolyze most β-lactam antibacterials. Unfortuna...

Talaromydien a and talaroisocoumarin A, new metabolites from the marine-sourced fungus sp. ZZ1616.

Natural product research
New talaromydien A and talaroisocoumarin A (), together with nine known compounds (-), were isolated from a culture of the marine-derived sp. ZZ1616 in potato dextrose broth medium. Structures of the new compounds were elucidated based on their HRE...

The application of machine learning techniques to innovative antibacterial discovery and development.

Expert opinion on drug discovery
INTRODUCTION: After the initial wave of antibiotic discovery, few novel classes of antibiotics have emerged, with the latest dating back to the 1980's. Furthermore, the pace of antibiotic drug discovery is unable to keep up with the increasing preval...

Modest Clostridiodes difficile infection prediction using machine learning models in a tertiary care hospital.

Diagnostic microbiology and infectious disease
Previous studies have shown promising results of machine learning (ML) models for predicting health outcomes. We develop and test ML models for predicting Clostridioides difficile infection (CDI) in hospitalized patients. This is a retrospective coho...

A biochemically-interpretable machine learning classifier for microbial GWAS.

Nature communications
Current machine learning classifiers have successfully been applied to whole-genome sequencing data to identify genetic determinants of antimicrobial resistance (AMR), but they lack causal interpretation. Here we present a metabolic model-based machi...

A systematic machine learning and data type comparison yields metagenomic predictors of infant age, sex, breastfeeding, antibiotic usage, country of origin, and delivery type.

PLoS computational biology
The microbiome is a new frontier for building predictors of human phenotypes. However, machine learning in the microbiome is fraught with issues of reproducibility, driven in large part by the wide range of analytic models and metagenomic data types ...

Machine Learning in Mass Spectrometry: A MALDI-TOF MS Approach to Phenotypic Antibacterial Screening.

Journal of medicinal chemistry
Machine learning techniques can be applied to MALDI-TOF mass spectral data of drug-treated cells to obtain classification models which assign the mechanism of action of drugs. Here, we present an example application of this concept to the screening o...