Conflict over commercialisation of research

this model could predict protein structures using AI
built by Nobel Laurates - John jumper and Demis Hassabis
it has 2 pre models - AlphaFold and Alphafold 2
these 2 were released open source, but the latest one wasn't released open sourcely.
Responding to criticism from the academic community, senior authors of the AlphaFold 3 paper had said they would publish their code within six months and did so early in November.

Commercialisation is driven by competition and prot, so the creators and/or owners invoke property and patent laws to protect their intellectual property (IP).
hospitals, research institutes, and universities also need money to operate and hence bank on commercialisation for revenue.

publish all the code and details of any algorithm they are working on.
but hold on to a premium, ready-to-use version of the software that could be commercialised.
“If you use somebody else’s algorithm, you should at least quote them and say, ‘That’s based on that machine or on that technology or that finding’.”
Some scientists strike deals with the companies: they study and develop a product the way the company likes it. In exchange, the company gives their lab unrestricted funds to continue other avenues of research (in which the company has no say).
More government funding is a way to circumvent the conflict between patented and open science.
The government can also subsidise the costs of products made by companies such that the latter can still hold on to their IP even as the products are available for sale at a lower price.
- This is what happened with the COVID-19 vaccines made by Moderna and Pfizer.

There is always a risk in such cases where someone else could commercialise the published work.

Hemmerling and his team developed an anaesthesia robot in 2008 that they named “McSleepy”.
The robot could autonomously administer drugs to induce general anaesthesia and monitor the effects.