With the looming crisis of antimicrobial resistance seen worldwide, research efforts are refocusing on the development of new antibacterial agents. Identification of novel antibacterial compounds becomes an important topic for research investment. Namely, whether there is a correlation between the chemical structures and antibacterial activity on the whole cells? Second, whether there is a correlation between the chemical structure/substructure of the compound and the transport or inhibition of bacterial efflux pumps? What are the modern computational approaches to antibacterial drug design and virtual screening? Are there any novel databases of compounds that exhibit antibacterial properties? What is the role of modern machine learning methods in novel antibacterial drug design?
In summary, what are the most vulnerable targets in a bacterial cell that allow designing successful antimicrobials? We will attempt to highlight early drug design and state-of-the-art methods, as well as provide recommendations for future approaches in the design of novel antibacterial agents. Indeed, within the proposed topic, the relationship between chemical structure and the ability of compounds to kill bacteria or suppress their growth, or even alter virulence or other bacterial properties, will be studied in detail.
Also, of interest is the inactivation or suppression of efflux pumps that contribute to the general antibacterial drug resistance. We will also seek to explore new platforms that are aimed at bacterial target identification and antibacterial drug databases. We will also delve into new approaches, application and methods such as deep learning leading to the discovery of new microbial drug targets and antimicrobial agents.
Contributions, in all acceptable formats, that cover the themes listed below, are welcome:
• Novel antimicrobial drug design
• Novel antibacterial via quantitative structure-activity
relationship approach
• Target identification and under-represented targets
• Report of novel antibacterial databases/research platforms
• Novel experimental approaches to antibacterial drug design
• Novel antibacterial mode-of-action studies
• Targeting efflux-mediated bacterial resistance
• Targeting bacterial virulence
• Multi-target drug design
• Synergistic action of drugs
• Chemoinformatics and molecular modelling
• Modern in silico methods in antimicrobial drug design
• Machine learning in antimicrobial drug design
With the looming crisis of antimicrobial resistance seen worldwide, research efforts are refocusing on the development of new antibacterial agents. Identification of novel antibacterial compounds becomes an important topic for research investment. Namely, whether there is a correlation between the chemical structures and antibacterial activity on the whole cells? Second, whether there is a correlation between the chemical structure/substructure of the compound and the transport or inhibition of bacterial efflux pumps? What are the modern computational approaches to antibacterial drug design and virtual screening? Are there any novel databases of compounds that exhibit antibacterial properties? What is the role of modern machine learning methods in novel antibacterial drug design?
In summary, what are the most vulnerable targets in a bacterial cell that allow designing successful antimicrobials? We will attempt to highlight early drug design and state-of-the-art methods, as well as provide recommendations for future approaches in the design of novel antibacterial agents. Indeed, within the proposed topic, the relationship between chemical structure and the ability of compounds to kill bacteria or suppress their growth, or even alter virulence or other bacterial properties, will be studied in detail.
Also, of interest is the inactivation or suppression of efflux pumps that contribute to the general antibacterial drug resistance. We will also seek to explore new platforms that are aimed at bacterial target identification and antibacterial drug databases. We will also delve into new approaches, application and methods such as deep learning leading to the discovery of new microbial drug targets and antimicrobial agents.
Contributions, in all acceptable formats, that cover the themes listed below, are welcome:
• Novel antimicrobial drug design
• Novel antibacterial via quantitative structure-activity
relationship approach
• Target identification and under-represented targets
• Report of novel antibacterial databases/research platforms
• Novel experimental approaches to antibacterial drug design
• Novel antibacterial mode-of-action studies
• Targeting efflux-mediated bacterial resistance
• Targeting bacterial virulence
• Multi-target drug design
• Synergistic action of drugs
• Chemoinformatics and molecular modelling
• Modern in silico methods in antimicrobial drug design
• Machine learning in antimicrobial drug design