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PIDGINv3

imported

therapeutics/pidginv3

Protein target prediction using random forests and reliability-density neighbourhood analysis

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

record
Category
Therapeutics
Subcategory
unknown
License
GPL-3.0(osi)
Status
dormant
Maturity
deployed
Organization
unknown
Country
unknown
Documentation
unknown
Tags
applicability-domain · cheminformatics · chemoinformatics · protein-ligand-interactions · rdkit · reliability-density · target-prediction
Regulatory
unknown
built by · 3

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.

  • PIDGINv2cheminformatics · chemoinformatics · target-prediction

    PIDGIN Version 2: Prediction IncluDinG INactivity: Version 2 (LEGACY VERSION)

  • pyADAapplicability-domain · cheminformatics

    A cheminformatics package to perform Applicability Domain of molecular fingerprints based in similarity calculation.

  • psikitcheminformatics · chemoinformatics · rdkit

    psi4+RDKit

  • FlexAIDprotein-ligand-interactions

    Flexible Artificial Intelligence Docking

  • kgem-in-drug-discoverytarget-prediction

    Code to accompany the "Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery" manuscript (Artificial Intelligence in the Life Sciences, 2022)

  • global-chemcheminformatics · chemoinformatics

    A Knowledge Graph of Common Chemical Names to their Molecular Definition

sources
  1. api.github.com/repos/lhm30/PIDGINv3
    retrieved 2026-08-05 · via github-api

    Machine-imported from GitHub search. Last push 2020-05-06, 40 stars, license reported as GPL-3.0. Category and schematic were assigned by keyword heuristics and are unreviewed.

Not yet verified by a human. Correct this record →

machine-readable

/v1/entries/31.json→ .entries["pidginv3"]

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