AIMC Topic: Microbial Sensitivity Tests

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From AI-Driven Sequence Generation to Molecular Simulation: A Comprehensive Framework for Antimicrobial Peptide Discovery.

Journal of chemical information and modeling
Antimicrobial Peptides (AMPs) are a promising strategy to address bacterial resistance, yet only a limited number have advanced to clinical trials. Recent advances in deep learning provide new opportunities for AMP design. Here, we propose an integra...

Chemical Space Exploration and Reinforcement Learning for Discovery of Novel Benzimidazole Hybrid Antibiotics.

Journal of chemical information and modeling
Benzimidazole hybrids are promising antibacterial agents, but the growing problem of antibiotic resistance has led to the necessity of developing novel compounds with enhanced antimicrobial activity. This study utilizes AI methods to generate new ant...

Hospital acquired drug resistant pathogens infections in patients with viral respiratory tract infections: a retrospective study.

BMC infectious diseases
BACKGROUND: Viral respiratory infections (VRTIs) caused by influenza (Flu) and COVID-19 pose significant global health challenges. Clinical outcomes are further exacerbated by infections with hospital acquired drug resistant pathogens (DRPs).

Potential application of Healitide-GP1, a novel antibacterial peptide, in wound healing: in vitro studies.

Scientific reports
Wound healing is a complex process that can be compromised by bacterial infections, leading to delayed healing and an increased risk of complications. The aim of this study was to design and develop a novel antibacterial peptide, Healitide-GP1, which...

Prediction of antibiotic resistance from antibiotic susceptibility testing results from surveillance data using machine learning.

Scientific reports
Antimicrobial resistance is a growing global health threat, and artificial intelligence offers a promising avenue for developing advanced tools to address this challenge. In this study, we applied various machine learning techniques to predict bacter...

Design, characterization, and application of novel antimicrobial peptides against Bacillus cereus.

International journal of food microbiology
Foodborne pathogens such as Bacillus cereus threaten food safety, necessitating novel antimicrobial solutions. Antimicrobial peptides (AMPs) offer broad-spectrum activity and potential applications in food preservation. In this study, we designed a l...

AMPGP: Discovering Highly Effective Antimicrobial Peptides via Deep Learning.

Journal of chemical information and modeling
Antimicrobial peptides (AMPs) have emerged as vital candidates in the fight against antibiotic resistance. The traditional processes for AMP design and discovery are often time-consuming and inefficient. Here, we propose the AMPGP model, which employ...

A dual-view deep learning-driven discovery of cinnamoyl anthranilic acid derivatives against orthopoxvirus through targeting host ITGB3.

European journal of medicinal chemistry
The orthopoxvirus genus, particularly the monkeypox virus (MPXV), continues to pose a significant global public health threat. Therefore, the development of novel anti-orthopoxvirus agents remains an urgent priority. Machine learning has proven to be...

Rapid Screening of Methicillin-Resistant Using MALDI-TOF MS and Machine Learning: A Randomized, Multicenter Study.

Analytical chemistry
Methicillin-resistant (MRSA) is a major cause of healthcare-associated infections including bacteremia. The rapid detection of MRSA is essential for prompt treatment and improved outcomes. However, traditional MRSA screening and confirmatory tests b...