Early in my career, my manager used the phrase in the above headline to highlight the difficulty inherent in drug discovery. Over the ensuing years, I have seen that statement repeatedly confirmed by the brutal attrition in the discovery and development of new drugs. There are so many variables that can kill a drug discovery…
5 common data management problems affecting drug discovery
Ask a pharma researcher how well they’re able to leverage their organization’s medical imaging data, and you might hear a discouraging response. While most pharma companies have massive amounts of clinical and medical imaging data, often, most of the imaging data isn’t ready for modern research processes and infrastructure. This imaging data is an untapped…
Cutting through the noise of machine learning for drug discovery
While the topic of AI in drug discovery has received considerable attention in recent years, mature deployments of techniques such as machine learning in the industry remain rare. “The chemistry domain is qualitatively different from any other problem that machine learning has exhibited real success in,” said Jason Rolfe, CTO of Variational AI (Vancouver, Canada). …
The top 5 drug discovery articles of 2021
The pandemic continued to remake the pharma and biotech industry this past year. A handful of companies fared exceptionally well at commercializing COVID-19 therapies. This fact promises to lead to sizable shifts in the rankings of pharma leaders. These trends were not yet evident in our spring roundup of the 50 largest pharma companies, which…
Realizing the potential of flow chemistry in hit to leads
Flow chemistry is a growing technology in the pharma industry, where it has proved itself invaluable, especially in the process development phase of drug discovery. However, the hit-to-lead generation phase of drug discovery has been slower to embrace these techniques. Nevertheless, the potential benefits are huge, particularly for rapid library generation and challenging chemistries that…
MIT researchers tout new machine learning technique for assessing drug molecules
MIT researchers are touting a new machine-learning technique called DeepBAR that can quickly calculate the binding affinities between drug candidates and their targets. DeepBAR produces precise calculations in a fraction of the time compared to conventional techniques, according to the researchers. They think the software could potentially accelerate drug discovery and protein engineering. “Our method…
Organ-chips could streamline drug development, but hurdles remain
While organ-on-a-chip technology has evolved tremendously over the past 15 years, adoption of the technology remains at an early stage. But as organ-chip technology advances and the R&D costs for pharma companies continue to hover near unsustainable levels, organ-on-a-chip technology has the promise to address what cell biologist and bioengineer Donald Ingber called the “broken”…
ERS Genomics and ZeClinics team up on gene-edited zebrafish disease models
The zebrafish, commonly found in aquariums, is widely tapped in drug development to understand disease mechanisms. The advent of CRISPR gene editing has given researchers more flexibility in developing disease models, thanks to its ability to create gene-edited zebrafish variants. To that end, the privately-held company ZeClinics (Barcelona) is using CRISPR-based techniques to make unique…
Turbine secures €3 million EUR seed fund to expand the potential of simulation-first drug discovery
Turbine, a simulation-based drug discovery company today announced the closing of an institutional financing round led by Delin Ventures. The seed fund will be used to redesign the failure-prone oncology drug discovery process into a series of rational steps facilitated by Turbine’s proprietary human cell model and simulation platform. The company’s first institutional financing round…