Rather than focusing on small well-characterized datasets or testing settings, it is now
possible to proceed by identifying large pools of unstructured data which can be used to
dynamically develop highly accurate predictions of technical and behavioral phenomena. In
pioneering an unstructured approach to predictive drug candidate selection that brings together a
vast array of previously disparate clinical and biophysical data, for example, Atomwise may
fundamentally reshape the “ideas production function” in drug discovery.
If advances in deep learning do represent the arrival of a general-purpose IMI, it is clear
that there are likely to be very significant long-run economic, social, and technological
consequence.
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