chemml
importedtherapeutics/chemml
ChemML is a machine learning and informatics program suite for the chemical and materials sciences.
Machine-generated from the listed sources and not yet reviewed by a human.
- Category
- Therapeutics
- Subcategory
- unknown
- License
- BSD-3-Clause(osi)
- Status
- active
- Maturity
- deployed
- Organization
- hachmannlab
- Country
- unknown
- Homepage
- hachmannlab.github.io/chemml
- Repository
- github.com/hachmannlab/chemml
- Documentation
- unknown
- Tags
- data-science · deep-learning · drug-discovery · machine-learning · materials-informatics · quantum-mechanics
- 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-pytorchdrug-discovery · materials-informatics
A PyTorch Implementation of "Optimization of Molecules via Deep Reinforcement Learning".
- molecular-vaedrug-discovery · materials-informatics
Pytorch implementation of the paper "Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules"
- Veronica-X-Pro-open-source-code-2.0quantum-mechanics
Advanced AI system with real quantum computing integration, sophisticated neural architectures, and production-grade infrastructure.
- aidsorbmaterials-informatics
Python package for deep learning on porous materials and beyond.
- CheMLFlowmaterials-informatics
Configuration-driven, reproducible ML workflows for molecules and materials, with DOE, auditable artifacts and agent-assisted execution.
- MaterialsBasematerials-informatics
Database for Materials
- api.github.com/repos/hachmannlab/chemmlretrieved 2026-08-05 · via github-api
Machine-imported from GitHub search. Last push 2026-08-05, 179 stars, license reported as BSD-3-Clause. Category and schematic were assigned by keyword heuristics and are unreviewed.
Not yet verified by a human. Correct this record →
/v1/entries/53.json→ .entries["chemml"]
Entries are sharded 64 ways by a stable hash of the id, so a consumer can find any record without an index.