Python package for the prediction of feasible synthetic routes towards a desired compound and associated tasks related to synthesis planning. Originally developed under the DARPA Make-It program and now being developed under the MLPDS Consortium.
This package can be used on its own as a normal Python package without deploying the full ASKCOS application. To do so, make sure that the project directory is on your PYTHONPATH and that the dependencies listed in requirements.txt are satisfied. The data and models must be downloaded separately from the askcos-data repository and placed in askcos-core/askcos/data.
The askcos-core image can be built using the Dockerfile in this repository. It depends on the askcos-data Docker image, which can be built manually or pulled from Docker Hub.
$ cd askcos-core
$ docker build -t <image name> .A Makefile is also provided to simplify the build command by providing a default image name and tag:
$ cd askcos-core
$ make buildMany of the individual modules -- at least the ones that are the most interesting -- can be run "standalone". Examples of how to use them are often found in the if __name__ == '__main__' statement at the bottom of the script definitions. For example...
Using the learned synthetic complexity metric (SCScore):
askcos/prioritization/precursors/scscore.py
Obtaining a single-step retrosynthetic suggestion with consideration of chirality:
askcos/retrosynthetic/transformer.py
Finding recommended reaction conditions based on a trained neural network model:
askcos/synthetic/context/neuralnetwork.py
Using the template-free forward predictor:
askcos/synthetic/evaluation/template_free.py
Using the coarse "fast filter" (binary classifier) for evaluating reaction plausibility:
askcos/synthetic/evaluation/fast_filter.py
Predicting regio-selectivity for a given atom-mapped reaction:
askcos/synthetic/selectivity/general_selectivity.py
Predicting reactivity descriptors for a given list of molecules:
askcos/synthetic/descriptors/descriptors.py