Finding the right ML model for a research problem (without the GitHub graveyard)

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

1. Last commit < 6 months — research code rots fast.
2. A paper or benchmark attached — not just a README claim.
3. Someone other than the author has used it — issues, forks, citations.

Why a directory beats search here

Search optimizes for popularity; research tooling is long-tail. The model you need might have 40 GitHub stars and be exactly right. A curated, domain-organized list surfaces those; a keyword search buries them under tutorials. Pick a source maintained by someone who actually runs the tools, and revisit it each quarter.