Informatics In-Depth Focus 2017
In this In-Depth Focus we look at using bioinformatics sequence similarities to optimise repurposing activities; informatics infrastructure in neglected disease research, and machine learning for accelerating drug discovery.
- Using bioinformatics sequence similarities to optimise repurposing activities
A significant amount of selectivity and potency data originating from screening of drug targets is generated each year and deposited in public databases. This can be exploited to accelerate drug discovery, in particular, for a variety of repurposing activities. Andrea Zaliani and Sheraz Gul explain how bioinformatics sequence similarities can be used to optimise repurposing activities.
- Informatics infrastructure for public-private collaborations in neglected disease research
Efforts to develop new medicines for diseases of the developing world (DDW) have been somewhat fragmented in the past and progress has been limited, despite considerable investment. Public-private partnership (PPP) is becoming an essential model for research in neglected disease areas. However, collaboration on this scale presents unique challenges, some of which can be well managed with the right informatics tools.
- Machine learning approaches as tools to accelerate drug discovery
Computational methods based on machine learning approaches are being introduced increasingly widely to screen the large number of molecules that have never participated in the drug discovery process, but which might have significant drug development potential. This article considers the latest advances in machine learning as applied to drug discovery.