openmedical/registry
← registry

PADME

imported

therapeutics/padme

This is the repository containing the source code for my Master's thesis research, about predicting drug-target interaction using deep learning.

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

PADME project image
GitHub preview card for simonfqy/PADME. Served by its origin, not stored here, and not covered by this registry’s licence.
record
Category
Therapeutics
Subcategory
unknown
License
MIT(osi)
Status
dormant
Maturity
deployed
Organization
unknown
Country
unknown
Homepage
unknown
Documentation
unknown
Tags
cheminformatics · convolutional-neural-networks · deep-learning
Regulatory
unknown
built by · 2

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.

  • geo-gcncheminformatics · convolutional-neural-networks

    The official implementation of the SGCN architecture.

  • gninacheminformatics · convolutional-neural-networks

    A deep learning framework for molecular docking

  • moleculegen-mlcheminformatics · convolutional-neural-networks

    Generate novel molecules using neural language models

  • bachelor-thesisconvolutional-neural-networks

    LaTeX Bachelor's Thesis

  • ADMIRE-DLconvolutional-neural-networks

    A suite of tools for the preprocessing of MRI images and the training of CNNs for the classification of Alzheimer's Disease patients.

  • airpiconvolutional-neural-networks

    AI-assisted rapid phase imaging for 4D-STEM

sources
  1. api.github.com/repos/simonfqy/PADME
    retrieved 2026-08-05 · via github-api

    Machine-imported from GitHub search. Last push 2019-06-18, 43 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["padme"]

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