Reading Time: 7 minutes The researching and development of medicinal innovation is particularly reliant on cross-collaboration with multiple disciplines. Today’s typical drug discovery project may entail data sharing between synthetic chemists, crystallographers, various molecular and cellular biologists, and, in the case of clinical trials, the doctors and researchers testing the drug for market. Introducing disruptive platforms to streamline this …
Google DeepMind has developed a tool to predict the structure of proteins from their genetic sequence, marking a noteworthy example of using AI in the process of scientific discovery.
Pande Lab at Stanford University in collaboration with Google released a paper earlier this week that focuses on how neural networks and deep learning technology could be crucial in improving the accuracy of determining which chemical compounds would be effective drug treatments for a variety of diseases. A Google Research
The function of molecules used in drugs in part depends on their structure, including the many chemical bonds between their atoms. These molecules can be built through several different chemical reactions, most of which are slow and inefficient because they rely on the formation of one chemical bond at a time. Ramesh Giri, Weinreb Early Career Professor of Chemistry at Penn State, has developed a reaction…
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AI relates often imperceptible observations to outcome in a fashion that’s unapologetically oblivious to mechanism, which challenges physicians and drug developers by explicitly severing utility from foundational scientific understanding.
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In just a few decades, we as an industry will be responsible for feeding an unprecedented 10 billion people – an impossible task to accomplish alone.
Even in the most prestigious of medical journals, industry influence over studies opens door to bias.
The health care industry is currently one of the fastest growing global industries on the planet, and according to economists, the growth is not expected to slow down any time soon. One reason that…
Without collaborative efforts, the industry will remain too fractured and adversarial to successfully transform itself to be patient-first and efficient.