Claus Bendtsen at AstraZeneca reveals how AI can be used to improve our understanding of disease, to help identify the causes of conditions and aid in drug discovery.
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Designing new drug molecules is crucial to R&D. Dr Sam Genway suggests that one way to improve and speed up this process is using AI inspired by language translation.
Charlotte Walker-Osborn, a Partner and Head of Technology Sector (International) at global law-firm Eversheds Sutherland and a legal expert in technology law, explains some of the challenges and potential future of artificial intelligence (AI) in the field of drug discovery.
A new AI project marks the first time 10 pharmaceutical companies have agreed to share data to aid drug discovery.
A new report reveals a thriving service and supply sector for the UK in addition to its research and development biotechs.
Typically, early drug discovery campaigns start with the screening of chemical libraries to identify candidate chemotypes modulating a particular target and/or phenotype. Success of the primary screening depends on multiple factors related to both biology and chemistry. These include the target’s druggability, sensitivity and specificity of assay system, composition and…
Building on previous research, scientists have made improvements to an artificial intelligence pipeline used to diagnose genetic diseases via blood samples obtained from gravely ill infants.
Data drives drug discovery, yet it continues to be among the biggest challenges faced by the industry.1 Experiments are often not repeatable and data interpretation is subject to the biases and limitations of human beings.
An online compound library consisting of the structure of over a billion molecules could be the future of drug discovery...
Researchers have developed algorithms that make it possible to predict and correct cells to identify genes...
The basic premise of drug discovery screening necessitates that the biological assays upon which it depends can be performed in a reproducible manner. In addition, the techniques employed must generate results that are biologically relevant and actionable.