AIMC Topic: Anti-Infective Agents

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Text mining for identification of biological entities related to antibiotic resistant organisms.

PeerJ
Antimicrobial resistance is a significant public health problem worldwide. In recent years, the scientific community has been intensifying efforts to combat this problem; many experiments have been developed, and many articles are published in this a...

Machine learning to design antimicrobial combination therapies: Promises and pitfalls.

Drug discovery today
Combination therapies can overcome antimicrobial resistance (AMR) and repurpose existing drugs. However, the large combinatorial space to explore presents a daunting challenge. In response, machine learning (ML) algorithms are being applied to identi...

Overcoming challenges in extracting prescribing habits from veterinary clinics using big data and deep learning.

Australian veterinary journal
Understanding antimicrobial usage patterns and encouraging appropriate antimicrobial usage is a critical component of antimicrobial stewardship. Studies using VetCompass Australia and Natural Language Processing (NLP) have demonstrated antimicrobial ...

Assessing and utilizing esterase specificity in antimicrobial prodrug development.

Methods in enzymology
As a class of enzymes, esterases have been investigated for decades and have found use in industrial processes, synthetic organic chemistry, and elsewhere. Esters are functional groups composed of an alcohol moiety and a carboxylic acid moiety. Altho...

Main-Chain Sulfonium-Containing Homopolymers with Negligible Hemolytic Toxicity for Eradication of Bacterial and Fungal Biofilms.

ACS macro letters
Antimicrobials against planktonic cells and established biofilms at low doses are in increasing demand to tackle antibiotic-resistant biofilm infections. As a promising alternative to antibiotics, cationic polymers can effectively kill planktonic mic...

Genome-Wide Mutation Scoring for Machine-Learning-Based Antimicrobial Resistance Prediction.

International journal of molecular sciences
The prediction of antimicrobial resistance (AMR) based on genomic information can improve patient outcomes. Genetic mechanisms have been shown to explain AMR with accuracies in line with standard microbiology laboratory testing. To translate genetic ...

Artificial intelligence for the discovery of novel antimicrobial agents for emerging infectious diseases.

Drug discovery today
The search for effective drugs to treat new and existing diseases is a laborious one requiring a large investment of capital, resources, and time. The coronavirus 2019 (COVID-19) pandemic has been a painful reminder of the lack of development of new ...

Machine Learning Data Augmentation as a Tool to Enhance Quantitative Composition-Activity Relationships of Complex Mixtures. A New Application to Dissect the Role of Main Chemical Components in Bioactive Essential Oils.

Molecules (Basel, Switzerland)
Scientific investigation on essential oils composition and the related biological profile are continuously growing. Nevertheless, only a few studies have been performed on the relationships between chemical composition and biological data. Herein, th...

Artificial intelligence and machine learning assisted drug delivery for effective treatment of infectious diseases.

Advanced drug delivery reviews
In the era of antimicrobial resistance, the prevalence of multidrug-resistant microorganisms that resist conventional antibiotic treatment has steadily increased. Thus, it is now unquestionable that infectious diseases are significant global burdens ...

Predicting antimicrobial mechanism-of-action from transcriptomes: A generalizable explainable artificial intelligence approach.

PLoS computational biology
To better combat the expansion of antibiotic resistance in pathogens, new compounds, particularly those with novel mechanisms-of-action [MOA], represent a major research priority in biomedical science. However, rediscovery of known antibiotics demons...