RRepoGEO

REPOGEO REPORT · LITE

hyperai/awesome-ai4s

Default branch master · commit 1dc6503d · scanned 6/25/2026, 6:52:57 AM

GitHub: 3,265 stars · 510 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
28 /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
2 / 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 hyperai/awesome-ai4s, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    AI for Science, AI4S, Drug Discovery, Biopharmaceutical, Machine Learning, Deep Learning, Awesome List, Research Papers, Bioinformatics, Computational Biology, Paper Interpretations, Datasets, Tutorials
  • highreadme#2
    Add a clear introductory sentence to the README

    Why:

    CURRENT
    # Awesome AI for Science
    COPY-PASTE FIX
    # Awesome AI for Science

这是一个持续更新的精选列表，汇集了人工智能在科学研究（AI for Science, AI4S）领域的最新论文解读、数据集和教程。
  • mediumabout#3
    Refine the repository description for clarity

    Why:

    CURRENT
    AI for Science 论文解读合集（持续更新ing），论文/数据集/教程下载：hyper.ai
    COPY-PASTE FIX
    AI for Science (AI4S) 领域的精选论文解读合集，持续更新最新研究、数据集与教程。

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 hyperai/awesome-ai4s
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
REINVENT
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. REINVENT · recommended 2×
  2. AutoDock Vina · recommended 2×
  3. AlphaFold2 · recommended 1×
  4. DeepMind's GNoME · recommended 1×
  5. MolGPT · recommended 1×
  • CATEGORY QUERY
    What AI methods are effective for accelerating drug discovery and development research?
    you: not recommended
    AI recommended (in order):
    1. AlphaFold2
    2. DeepMind's GNoME
    3. REINVENT
    4. MolGPT
    5. Open Targets Platform
    6. DisGeNET
    7. STRING database
    8. AutoDock Vina
    9. Schrödinger's Glide
    10. DeepDock
    11. ADMETlab 2.0
    12. SwissADME
    13. ProTox-II
    14. Unlearn.AI
    15. Owkin
    16. Insilico Medicine's Pandomics

    AI recommended 16 alternatives but never named hyperai/awesome-ai4s. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for AI models to predict drug interactions or discover novel therapeutic compounds.
    you: not recommended
    AI recommended (in order):
    1. DeepChem (deepchem/deepchem)
    2. PyTorch Geometric (pyg-team/pytorch_geometric)
    3. DGL (dmlc/dgl)
    4. AlphaFold
    5. REINVENT
    6. MolBERT
    7. ChemBERTa (seyonechithran/chemberta)
    8. AutoDock Vina
    9. GNINA (gnina/gnina)
    10. DeepDDI

    AI recommended 10 alternatives but never named hyperai/awesome-ai4s. 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 hyperai/awesome-ai4s?
    pass
    AI did not name hyperai/awesome-ai4s — 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 hyperai/awesome-ai4s in production, what risks or prerequisites should they evaluate first?
    pass
    AI named hyperai/awesome-ai4s explicitly

    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 hyperai/awesome-ai4s solve, and who is the primary audience?
    pass
    AI named hyperai/awesome-ai4s explicitly

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

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hyperai/awesome-ai4s — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite