The real issue is probably that there are so many moving parts, especially when you get to the more complex drugs (e.g. antibodies). You would have to go through a million patents to find all the information required to create the actual molecule and also get it in a state to put into your body.
And even with all the information, it is unlikely anyone is sufficiently resourced (whether it's money, tools, or know-how) to actually make something viable.
(as a minor defense of the pharma companies, you're paying them not just to do a bunch of organic synthesis for you, but also to do ongoing QA that the synthesis process is working correctly. and not all processes are equally easy/amenable to industrial-scale duplication.)
What I find most dubious about the article is the lack of attention to synthetic biology. Because QA in that sense can become say, “this modified organism can produce the drug in question under most conditions and all future populations will carry the new genes with X% assurance” - ONCE -and you’re done.
This is quite possibly the real Pharma killer, IMO.
There's so much tooling and expertise that goes into front end, back end, deployment, db, QA, etc ,etc, etc just to get one app going. Why would would drug deployment be any different?
Also the same semi-synthetic organism could be applied in traditional pharmaceutical process settings as well, if for no reason other than cost reduction (and probably workforce reduction as well). So even “big pharma” has some incentive to contribute to this sort of technology.
But this is exactly like saying "Reddit is just a few SQL queries, a reasonable programmer could whip up their own version in a weekend." Just like there is only one reddit/StackOverflow/Facebook even though plenty of people have the same basic idea, even knowing the chemical formula doesn't help that much. The trick is in the execution: generating reliable quantities of reliable strength and without any contaminants.
There is a whole field, process engineers, devoted to this quite difficult problem.