Discovery and development of antibiotics have significantly reduced the burdens of infectious disease in human population in the past. However, due to natural evolution of microbes and inappropriate usages of antibiotics in healthcare and agriculture, antibiotic resistance has become an urgent problem world wide. In addition, due to various reasons1, development of new antibiotics, especially those with novel structures and targets, has slowed down in the past decades. In the near future, emergence of widely spread antibiotic resistant micro-organism will cause more problems. Therefore, novel methods for antibiotics discovery are urgently needed.
In Unifying Likelihood-free Inference with Black-box Sequence Design and Beyond, we propose to unify two seemingly distinct worlds: likelihood-free inference and black-box sequence design, under one probabilistic framework.
E.g. most pharmaceutical companies have abandoned the development of new antibiotics and have instead focused on developing more profitable drugs for non-communicable diseases. ↩︎