RRepoGEO

REPOGEO REPORT · LITE

junxia97/awesome-pretrain-on-molecules

Default branch main · commit 958b889d · scanned 6/8/2026, 3:02:50 PM

GitHub: 539 stars · 57 forks

AI VISIBILITY SCORE
15 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
0 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface junxia97/awesome-pretrain-on-molecules, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's opening to clearly state it's a curated list/survey

    Why:

    CURRENT
    This is a repository to help all readers who are interested in pre-training on molecules.
    COPY-PASTE FIX
    This repository is a comprehensive, curated list of resources and a systematic survey (as presented at IJCAI 2023) for anyone interested in pre-training on molecules.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected)
    COPY-PASTE FIX
    Add a LICENSE file (e.g., MIT for code or CC-BY-4.0 for content) to the repository root to clarify usage rights for the curated list and any associated code.
  • mediumhomepage#3
    Add a homepage URL to the repository settings

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    Set the repository homepage URL in the GitHub repository settings to the associated IJCAI 2023 paper (e.g., the arXiv link if available) or a dedicated project page.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface junxia97/awesome-pretrain-on-molecules
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 1×
  2. deepchem/deepchem · recommended 1×
  3. MoleculeNet · recommended 1×
  4. GitHub · recommended 1×
  5. pyg-team/pytorch_geometric · recommended 1×
  • CATEGORY QUERY
    Where can I find resources for pre-trained models in computational chemistry applications?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. DeepChem (deepchem/deepchem)
    3. MoleculeNet
    4. GitHub
    5. PyTorch Geometric (PyG) (pyg-team/pytorch_geometric)
    6. Chemprop (chemprop/chemprop)
    7. OpenChem (openchem/openchem)

    AI recommended 7 alternatives but never named junxia97/awesome-pretrain-on-molecules. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the latest self-supervised techniques for learning molecular representations in drug discovery?
    you: not recommended
    AI recommended (in order):
    1. InfoGraph
    2. DCL
    3. GraphCL
    4. ChemBERTa-2
    5. Molecule Attention Transformer
    6. JT-VAE
    7. MolGAN
    8. GraphVAE
    9. GraphRNN
    10. MolT5
    11. Uni-Mol
    12. GraphMVP
    13. Pretrain-GNN

    AI recommended 13 alternatives but never named junxia97/awesome-pretrain-on-molecules. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of junxia97/awesome-pretrain-on-molecules?
    pass
    AI did not name junxia97/awesome-pretrain-on-molecules — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts junxia97/awesome-pretrain-on-molecules in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name junxia97/awesome-pretrain-on-molecules — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo junxia97/awesome-pretrain-on-molecules solve, and who is the primary audience?
    pass
    AI did not name junxia97/awesome-pretrain-on-molecules — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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