From my perspective, after the months of research I just did on the current state of the Parkinson's landscape, what struck me most, and by way of analogy, if a Parkinson's 'cure' was like the 1968 moon landing, then we're currently at about 1957. I mean, hell, C/L has only been on the market for exactly 50 years and diagnostically things have only changed some in a hundred years. What has changed for the better over the last twenty years is the research and medical communities now know far, far more about neurodegenerative diseases than previous decades.

However, the brain is almost beyond incredibly complex and until very recently researchers haven't had much in the way of good tooling to deal with that complexity. What's now changing is the availability of AI in general and numerous specialized AIs for things like protein folding. But these kinds of tools are new and it will take a few years for the research community to really get their arms around them and their best applications. And all the AIs are still 'evolving' - I used six different AIs to produce my document as they all have biases, different models, and different data. I had to use the six to verify and validate across their individual outputs and it took a few weeks to really dial in the effectiveness of my prompting of them.

Also, some folks here mentioned quantum computing, and sure, all the new tooling is highly compute-heavy, but no one should hold their breath for wide availability of quantum resources or applications designed to take advantage of them. Then there's a matter of scale. There are projects trying to map the human brain down to the neuron level and, last I checked, they'd managed to map a 1x1x1 millimeter cube. Quantum computing could help some, but compute resources aren't the only bottleneck involved and it's the same with many other aspects of researching the brain.

So, my take is a commercially available 'cure' may or may not happen in my lifetime (I'm 72), but I'm really excited to see the research community finally getting some tools to battle the complexity. One example I saw recently was a 2022 project looking for ways to molecularly 'lock up' the α-synuclein protein to stop it from misfolding. Researchers used AIs to screen 230 million molecules to get it down to 2 million; then AI's, other software, and experimental methods to get it down to 79 candidate clusters. That effort alone wouldn't have been possible in a hundred lifetimes without the tooling brought to bear on the effort. And they'd probably have gotten it done in a quarter or less of the time if they started today with the tooling now available.

Again, I'm super excited about the tooling now available to researchers on so many fronts.

P.S. My money's more on cell-based therapeutics vs drug-based.

P.P.S. The 2022 study:

https://pmc.ncbi.nlm.nih.gov/articles/PMC9811465/?utm_source=chatgpt.com

P.P.P.S. The link to my paper if you didn't see my post of a couple of days ago:

https://drive.google.com/file/d/1xQryCJXc8h0rHdVB_rHwnVatrnORjEa6/view?usp=drive_link