openmedical/registry
← registry

AAVGen

imported

therapeutics/aavgen

Protein Language model for AAV capsid generation.

Machine-generated from the listed sources and not yet reviewed by a human.

AAVGen project image
GitHub preview card for mohammad-gh009/AAVGen. Served by its origin, not stored here, and not covered by this registry’s licence.
record
Category
Therapeutics
Subcategory
unknown
License
Apache-2.0(osi)
Status
active
Maturity
deployed
Organization
unknown
Country
unknown
Homepage
unknown
Documentation
unknown
Tags
artificial-intelligence · capsid-engineering · deep-learning · drug-discovery · machine-learning · protein-engineering · protien-language-model · reinforcement-learning
Regulatory
unknown
similar by tags

Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.

  • blatant-whydrug-discovery · protein-engineering

    AI-powered biologics design campaign agent — multi-agent orchestration with BoltzGen, PXDesign, Protenix, and 200+ cloud tools. Antibodies, nanobodies, de novo binders, and beyond.

  • structure-factorydrug-discovery · protein-engineering

    AI-agent toolkit for structural biology: design binders, map structures, screen candidates, rank results, and prepare cloud-scale runs.

  • drug-gymdrug-discovery · reinforcement-learning

    Reinforcement learning environments for drug discovery

  • DrugExdrug-discovery · reinforcement-learning

    De Novo Drug Design with RNNs and Transformers

  • DrugReasonerdrug-discovery · reinforcement-learning

    Predicting drug approval with reasoning.

  • ReLeaSEdrug-discovery · reinforcement-learning

    Deep Reinforcement Learning for de-novo Drug Design

sources
  1. api.github.com/repos/mohammad-gh009/AAVGen
    retrieved 2026-08-25 · via github-api

    Machine-imported from GitHub search. Last push 2026-05-19, 6 stars, license reported as Apache-2.0. Category and schematic were assigned by keyword heuristics and are unreviewed.

Not yet verified by a human. Correct this record →

machine-readable

/v1/entries/15.json→ .entries["aavgen"]

Entries are sharded 64 ways by a stable hash of the id, so a consumer can find any record without an index.