Scale up isn't an issue per se but the fact the Pharma Corp has to set the exact process somewhat early and have it in the DMF, drug master file. With the ML an

Scale up isn't an issue per se but the fact the Pharma Corp has to set the exact process somewhat early and have it in the DMF, drug master file. With the ML angle proposed here are a few not comprehensive challenges. One measurement of temperature in actual reactions wasn't too accurate until last few decades. Many reactions were not attempted under pressure. There is a whole cat, mouse and booby trap element with scientific papers and even more so w patents a patent only has to be obvious 'to some one skilled in the art of, in this case, chemistry". Then huge amounts of data sit in silos or in dusty lab notebooks. This is before the notions of what has been patented and what is now under patent until when. Trade secrets can still be an issue, but our switch to first to file from our previous first to invent patent system should reduce 'submarine patent' risk. So these IP issues and siloed data present challenges to using ML in this space. It can help target research by better selecting lead cannidates, and find novel uses to existing molecules.