# yangkky/Machine-learning-for-proteins

Listing of papers about machine learning for proteins.

Repository: https://github.com/yangkky/Machine-learning-for-proteins
Canonical: https://ross.abutalabs.com/products/machine-learning-for-proteins
License: GPL-3.0
License Family: copyleft
Last push: 2024-05-31T14:43:27+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2672, "days_push": 824, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1714, forks 219 (observed 2026-08-28T04:05:26.131977+00:00)

## What it is
A curated, community-maintained list of research papers on machine learning applied to proteins, organized by application and model type. It serves as a living bibliography accompanying a review of ML methods in protein engineering.

## Use cases
- find papers on machine learning for protein engineering
- find papers on protein structure prediction with deep learning
- find papers on generative models for protein design
- find papers on ML-guided directed evolution
- find papers on protein representation learning
- find papers on predicting protein stability with ML
- find papers on unsupervised protein variant prediction

## When to choose
- you want a comprehensive, categorized bibliography of ML-for-proteins research
- you are starting research in computational protein engineering and need background reading
- you want to track recent papers in protein machine learning

## When to avoid
- you need runnable code or datasets rather than paper references
- you need a maintained software tool rather than a reading list

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, documentation
- domain: bioinformatics, machine-learning, artificial-intelligence, tutorials
- platform: -
- tags: awesome-list, papers, protein-engineering, protein-structure, deep-learning, curated-list

## Member repositories
- yangkky/Machine-learning-for-proteins (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:26.131977+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:34:40.140613+00:00, confidence not recorded.
  - readme: https://github.com/yangkky/Machine-learning-for-proteins (fetched 2026-08-28T04:05:26.131977+00:00, sha 403264ae85a5)
- Data as of 2026-08-30T08:39:29.467469+00:00.