kinoml
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openkinome
Structure-informed machine learning for kinase modeling
We should add a feature to our training notebooks to check if an environment variable is set (e.g. `KINOML_NUM_THREADS`), and if so, call [`torch.set_num_threads()](https://pytorch.org/docs/stable/generated/torch.set_num_threads.html) with the appropriate number of threads....
It would be great if our CI can provide information about cell execution times when testing the example notebooks. We use the nbval plugin for pytest, which does not support...
Our current cache implementations rely on `functools.lru_cache` decorators, which admit a `size` keyword with the _number_ of items to memoize. We can customize this (now hardcoded) value if we drop...
## Data set We should include the data set which contains over 17k measurements of compounds on the CDK2 kinase. ## References: - The data set can be found in...
Still needs to include the measurement type, external indices and, if needed, provenance information for each kinase / ligand.
In `kinoml.features.core` we implement a few abstract classes. According to python best practices we should include the functionality from the built-in `abc` library. However, this may also complicate testing methods...
The recent changes in the featurization pipeline changed how the featurizers go through the different systems in a dataset. Previously, a single system would go all the way through the...
Have class-based documentation with hierarchy. In Sphinx docs. See example: https://open-forcefield-toolkit.readthedocs.io/en/0.10.0/topology.html Source: https://open-forcefield-toolkit.readthedocs.io/en/0.10.0/_sources/topology.md.txt
Update tests, ideally have 80% coverage. Note: have a check for estimate input for forward method in pytorch models, see https://github.com/openkinome/kinoml/pull/53
## Description Re-add files for HM generated by @glass-w ## Todos - [ ] Integrate and use in the kinoml pipeline ## Status - [ ] Ready to go
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Structure-informed machine learning for kinase modeling