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Bayesian-Illumination

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therapeutics/bayesian-illumination

Bayesian Illumination is an accelerated generative model for optimization of small molecules.

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

Bayesian-Illumination project image
GitHub preview card for Jonas-Verhellen/Bayesian-Illumination. Served by its origin, not stored here, and not covered by this registry’s licence.
record
Category
Therapeutics
Subcategory
unknown
License
AGPL-3.0(osi)
Status
maintained
Maturity
deployed
Organization
unknown
Country
unknown
Homepage
jonas-verhellen.github.io/Bayesian-Illumination/
Repository
github.com/Jonas-Verhellen/Bayesian-Illumination
Documentation
unknown
Tags
bayesian-optimization · cheminformatics · generative-model · genetic-algorithm · molecular-structures · quality-diversity
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.

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    moldrug (AKA mouse) is a Python package for drug-oriented optimization on the chemical space ​

  • ADMET_calculationscheminformatics · molecular-structures

    ADMET calculations from different research paper and a goal to make it memory efficent with ML implementations.

  • bofirebayesian-optimization · cheminformatics

    Experimental design and (multi-objective) bayesian optimization.

  • sensaascheminformatics · molecular-structures

    Shape-based alignment of molecules using 3D point-based representation

  • sensaas-bioisosterecheminformatics · molecular-structures

    Shape-based alignments for identifying bioisosteric replacements or scaffold-hopping using SENSAAS

sources
  1. api.github.com/repos/Jonas-Verhellen/Bayesian-Illumination
    retrieved 2026-08-25 · via github-api

    Machine-imported from GitHub search. Last push 2025-06-17, 19 stars, license reported as AGPL-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/15.json→ .entries["bayesian-illumination"]

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