If you write code for research, you've felt this: there's almost certainly a model for your problem, but finding the maintained one means wading through abandoned repos, broken Colab notebooks, and demos that 404.
The existence question is solved. ML now touches structure prediction, materials screening, retrosynthesis, literature triage. The discovery question is the real bottleneck.
What I actually do
Instead of cold-searching GitHub, I start from a curated index and work backward to the repo. For the science side I lean on tools indexed under AI for Scientific Coding — it groups projects by domain (biology, chemistry, materials science) alongside papers, labs, and datasets, and it's pruned often enough that the dead links don't accumulate.
A heuristic for picking tools
