Lab Data Automation, Optimizing R&D Workflows and Lead Discovery from Biologically Complex Bioassays
Posted 23rd January 2019 by Kieran Chambers
With an increasing amount of data being generated today, pharma and biotech companies need to develop new technologies and strategies in order to structure, analyse and integrate this big data. If you weren’t able to attend our Global Pharma R&D Informatics & AI Congress, we have made these slides from Ralph Haffner, Dana Caulder and Pierre Farmer available.
Posted 12th November 2018 by Kieran Chambers
Since its discovery in 2012, the use of the CRISPR/Cas9 system to edit genomes has raised the expectations for the cure of untreatable disorders. Very often, as biomedical geneticists we found ourselves talking about the potentials and drawbacks of CRIPSR/Cas9 both at scientific and layman level.
Human Genomic Evidence: Revolutionising the identification and prioritisation of targets for better medicines
Posted 17th October 2018 by Kieran Chambers
Nine out of 10 potential drugs that enter clinical trials never make it to the market. Failure often occurs because the biological target chosen is not well understood. However, it is hard to objectively select targets with a high chance of clinical success because the data required to predict efficacy and safety are complex, dispersed and incomplete. To address this challenge, Open Targets was founded in 2014 as a public-private partnership by GSK, EMBL-EBI and the Wellcome Sanger Institute. The consortium has grown since its launch, welcoming new partners Biogen in 2016, Takeda in 2017, and Celgene in 2018.
Posted 28th September 2018 by Kieran Chambers
When we last spoke, Brandon Allgood told us about The Future of AI. This week, we caught up with Brandon and spoke about drug discovery and machine learning algorithms.
Posted 17th September 2018 by Kieran Chambers
Drug discovery and development is a complex process that requires integration of multiple data points, experiments and calculated risk/benefit assumptions. It is therefore only natural that virtualization and big data analytics are a natural fit for implementations, expected to demonstrate significant cost-effective gains for the betterment of society, providing access to effective and safe medications.
Posted 5th September 2018 by Kieran Chambers
I don’t need to tell you that pharma has changed, that it is still changing at speed, or that change is the new normal. I don’t need to tell you because you − more than anyone else − already know. The passing of the blockbuster era has caused uncertainty. Without the superstar drugs of old, where does the industry go next? It’s a serious challenge, but without challenge, there is no change. The answer, of course, is the scalar shift from blockbuster generic to precision medicine specific.
Posted 3rd September 2018 by Kieran Chambers
Brandon Allgood is the Chief Technology Officer at Numerate, as well as a co-founder. In addition to being responsible for the development of Numerate’s cloud-based machine learning platforms, Brandon runs the software & data science teams, and is the technical lead on all of the companies internal drug programs, as well as external collaborations.
Here, Brandon talks about what he thinks the future holds for AI and machine learning: