Deep Learning Methods for Design of RNA-targeting Small-Molecule Antibiotics

Growing antibiotic resistance is a major threat to global public health. One way to combat this problem is through the development of antibiotics that act against bacteria in new ways. Historically, antibiotics work by interacting with proteins in the bacteria, which are needed for the cells to reproduce. However, recently, it has been shown that molecules that interact with RNA rather than proteins can also inhibit bacterial growth and reproduction. We will use cutting-edge machine learning techniques to improve the capacity of industrial software to identify molecules that are good potential antibiotics that might act through interacting with RNA. The impact will be the identification of new antibiotics and new antibiotic design rules to help ameliorate antibiotic resistance in bacteria.

Faculty Supervisor:

Rachael A. Mansbach

Student:

Partner:

Molecular Forecaster

Discipline:

Physics

Sector:

Professional, scientific and technical services

University:

Concordia University

Program: