yes, personally I think there is. I've spent a tremendous amount of my career watching computer scientists misunderstand how to work on protein folding and waste a lot of people's time. Because the concept of protein folding is so unbelievably complex, most CS and ML folks get the basic talk: "nearly all proteins fold reversibly to a global minimum energy structure which is completely defined by the sequence of the protein", which isn't remotely true (basically a weak form of Anfinsen's dogma and Levinthal's paradox). It's easy to explain, and CS and ML people get excited and go off to work on the problem. This led to a lot of publications that focused on rapidly finding heuristics that could sample enough space to find an approximation of the lowest energy structure. these methods typically failed to make good predictions although eventually methods like Rosetta did start making good predictions around 15-20 years ago (amusingly, the author of Rosetta told me: "the larger the PDB (training data set) gets, the worse the predictions we make".

But people who spend a long time getting a biological education know why this is true: most proteins don't fold to their energetic minimum, they fold to a collection of kinetically accessible states, rarely finding their true minimum (some small proteins do fold quickly, and we typically can predict their structure). And, many of the physical approximations that are used lead to inaccuracies (for example, some variables are constrained to specific values to save time, but making good predictions requires them to be unconstrained).

Some of my work made significant contributions to changing these beliefs, and I've very thankful for the CS and ML folks who contributed to that, but all of them spent a lot of time learning about proteins before they were useful contributors.

Myself I've had to "unlearn" a lot of the early things that were explained to me when I was a layperson, because when you're first learning something, if somebody gives you a simplified view, it can be really hard to move on to the more subtle and nuanced details in the field (for example, many people learn Mendelian genetics and then spend years struggling to understand why most traits don't follow mendelian statistics).

My goal here is to prevent wasted time on behalf of the experienced contributors in the field. I do appreciate good attempts at explaining the field to laypeople, but want to set the expectations on contributing accurately.