molecular-vae
importedtherapeutics/molecular-vae
Pytorch implementation of the paper "Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules"
Machine-generated from the listed sources and not yet reviewed by a human.
- Category
- Therapeutics
- Subcategory
- unknown
- License
- MIT(osi)
- Status
- dormant
- Maturity
- deployed
- Organization
- unknown
- Country
- unknown
- Homepage
- unknown
- Repository
- github.com/aksub99/molecular-vae
- Documentation
- unknown
- Tags
- cheminformatics · chemistry · deep-learning · drug-discovery · jupyter-notebook · machine-learning · materials-informatics · materials-science
- Regulatory
- unknown
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.
Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.
- MolDQN-pytorchchemistry · drug-discovery · materials-informatics · materials-science
A PyTorch Implementation of "Optimization of Molecules via Deep Reinforcement Learning".
- aidsorbcheminformatics · chemistry · materials-informatics
Python package for deep learning on porous materials and beyond.
- stkcheminformatics · chemistry · materials-science
A Python library which allows construction and manipulation of complex molecules, as well as automatic molecular design and the creation of molecular databases.
- aimscheminformatics · drug-discovery · materials-science
AIMS: Artificial Intelligence for Molecular Sciences
- ESCALATE_Capturecheminformatics · chemistry · materials-informatics
Data capture and experimental interfacing software for chemistry (part 1 of 2)
- chemmldrug-discovery · materials-informatics
ChemML is a machine learning and informatics program suite for the chemical and materials sciences.
- api.github.com/repos/aksub99/molecular-vaeretrieved 2026-08-05 · via github-api
Machine-imported from GitHub search. Last push 2021-03-31, 71 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 →
/v1/entries/16.json→ .entries["molecular-vae"]
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