MolDQN-pytorch
importedtherapeutics/moldqn-pytorch
A PyTorch Implementation of "Optimization of Molecules via Deep Reinforcement Learning".
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/MolDQN-pytorch
- Documentation
- unknown
- Tags
- chemistry · deep-learning · dqn-pytorch · drug-discovery · inverse-design · 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.
- molecular-vaechemistry · drug-discovery · materials-informatics · materials-science
Pytorch implementation of the paper "Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules"
- chemmldrug-discovery · materials-informatics
ChemML is a machine learning and informatics program suite for the chemical and materials sciences.
- stkchemistry · 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.
- allegrodrug-discovery · materials-science
Allegro is a code for building highly scalable E(3)-equivariant interatomic potentials
- deepchemdrug-discovery · materials-science
Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology
- litmatterdrug-discovery · materials-science
Rapid experimentation and scaling of deep learning models on molecular and crystal graphs.
- api.github.com/repos/aksub99/MolDQN-pytorchretrieved 2026-08-05 · via github-api
Machine-imported from GitHub search. Last push 2023-03-24, 84 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/0.json→ .entries["moldqn-pytorch"]
Entries are sharded 64 ways by a stable hash of the id, so a consumer can find any record without an index.