openmedical/registry
← registry

maxsmi

imported

therapeutics/maxsmi

maxsmi: a guide to SMILES augmentation. Find the optimal SMILES augmentation for accurate molecular prediction.

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

record
Category
Therapeutics
Subcategory
unknown
License
MIT(osi)
Status
dormant
Maturity
deployed
Organization
volkamerlab
Country
unknown
Documentation
unknown
Tags
cheminformatics · data-augmentation · deep-learning
Regulatory
unknown
built by · 4

Top contributors by commit count, from the project’s public repository. Avatars are served by their origin, not stored here. To be removed from this list, open an issue.

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.

  • AlbumentationsXdata-augmentation

    Next-generation Albumentations: dual-licensed for open-source and commercial use

  • augmentationdata-augmentation

    Adversarial Augmentation for Enhancing Classification of Mammography Images

  • candockdata-augmentation

    A time series signal analysis and classification framework

  • DeepTrack2data-augmentation

    DeepTrack2 is a modular Python library for generating, manipulating, and analyzing image data pipelines for machine learning and experimental imaging.

  • GaNDLFdata-augmentation

    A generalizable application framework for segmentation, regression, and classification using PyTorch

  • MedVisiondata-augmentation

    Medical Image Vision Operators, such as RoIAlign, DCNv1, DCNv2 and NMS for both 2/3D images.

sources
  1. api.github.com/repos/volkamerlab/maxsmi
    retrieved 2026-08-05 · via github-api

    Machine-imported from GitHub search. Last push 2024-02-22, 35 stars, license reported as MIT. Category and schematic were assigned by keyword heuristics and are unreviewed.

Not yet verified by a human. Correct this record →

machine-readable

/v1/entries/32.json→ .entries["maxsmi"]

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