summit
importedtherapeutics/summit
Optimising chemical reactions using machine 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
- sustainable-processes
- Country
- unknown
- Repository
- github.com/sustainable-processes/summit
- Documentation
- unknown
- Tags
- bayesian-optimization · chemistry · drug-discovery · machine-learning · nelder-mead · neural-networks · optimization · self-optimization
- 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.
- SOBERbayesian-optimization · drug-discovery · optimization
Fast Bayesian optimization, quadrature, inference over arbitrary domain with GPU parallel acceleration
- chempropbayesian-optimization · chemistry · drug-discovery
Fast and scalable uncertainty quantification for neural molecular property prediction, accelerated optimization, and guided virtual screening.
- bopepbayesian-optimization · optimization
Bayesian Optimization for protein design and mining
- chempropchemistry · drug-discovery · neural-networks
Message Passing Neural Networks for Molecule Property Prediction
- pyepidemicsbayesian-optimization · optimization
Open source epidemiological modeling in Python
- sdl-frameworkbayesian-optimization · drug-discovery
A reproducible framework for closed-loop experimental optimization using self-driving laboratory principles and Bayesian optimization.
- api.github.com/repos/sustainable-processes/summitretrieved 2026-08-05 · via github-api
Machine-imported from GitHub search. Last push 2024-09-03, 151 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/2.json→ .entries["summit"]
Entries are sharded 64 ways by a stable hash of the id, so a consumer can find any record without an index.