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ML4SMILES

imported

therapeutics/ml4smiles

Automatic Prediction of Molecular Properties Using Substructure Vector Embeddings within a Feature Selection Workflow

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

ML4SMILES project image
GitHub preview card for Songyosk/ML4SMILES. 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
drug-discovery · machine-learning · molecular-properties · smiles · smiles-strings
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.

  • molecule-clipboardmolecular-properties · smiles

    A browser-based tool for drawing, editing, inspecting, canonicalizing, and visualizing small molecules

  • pysmilessmiles · smiles-strings

    A lightweight python-only library for reading and writing SMILES strings

  • wiswessersmiles · smiles-strings

    Wiswesser Line Notation Project

  • NistChemPymolecular-properties

    Unofficial Python tools for querying NIST Chemistry WebBook pages and extracting molecular-property records.

  • Deep-Drug-Coderdrug-discovery · smiles-strings

    A tensorflow.keras generative neural network for de novo drug design, first-authored in Nature Machine Intelligence while working at AstraZeneca.

  • kaggle_leash_belkadrug-discovery · smiles-strings

    11th place solution of NeurIPS 2024 - Predict New Medicines with BELKA competition on Kaggle: https://www.kaggle.com/competitions/leash-BELKA

sources
  1. api.github.com/repos/Songyosk/ML4SMILES
    retrieved 2026-08-25 · via github-api

    Machine-imported from GitHub search. Last push 2024-09-21, 12 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/29.json→ .entries["ml4smiles"]

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