# Peldom/papers_for_protein_design_using_DL

List of papers about Proteins Design using Deep Learning

Repository: https://github.com/Peldom/papers_for_protein_design_using_DL
Canonical: https://ross.abutalabs.com/products/papers_for_protein_design_using_dl
License: GPL-3.0
License Family: copyleft
Topics: deep-learning, protein-design
Last push: 2026-08-15T05:58:26+00:00

## Health v2 (maintenance only)
Score: 76/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 97, release rhythm 35, longevity 100
- inputs: {"age_days": 1742, "days_push": 18, "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 1968, forks 220 (observed 2026-08-28T04:06:00.034178+00:00)

## What it is
A curated list of research papers on protein design using deep learning, including benchmarks, datasets, and reviews. It serves as a reading resource for researchers in computational biology and AI-driven protein engineering.

## Use cases
- find papers on deep learning for protein design
- discover datasets and benchmarks for protein structure prediction
- keep up with new research in computational protein engineering
- find reviews on generative models for enzyme design
- locate code implementations accompanying protein design papers

## When to choose
- you need a curated, regularly updated reading list for AI protein design research
- you want links to papers, code, and datasets in one place

## When to avoid
- you need runnable software rather than a paper list
- you need a comprehensive machine learning for proteins list beyond design focus

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation
- domain: deep-learning, bioinformatics, artificial-intelligence
- platform: cross-platform
- tags: awesome-list, protein-design, papers, computational-biology, protein-structure

## Member repositories
- Peldom/papers_for_protein_design_using_DL (main) score 76

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:00.034178+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 2026-08-30T03:05:10.617760+00:00, confidence not recorded.
  - readme: https://github.com/Peldom/papers_for_protein_design_using_DL (fetched 2026-08-28T04:06:00.034178+00:00, sha a04f03d7a2e0)
- Data as of 2026-08-30T08:39:29.467469+00:00.