ChemSampler is a simple Python API to sample the chemical space around a given molecule. It is developed and maintained by the Ersilia Open Source Initiative.
ChemSampler capitalizes on the generative models available in the Ersilia Model Hub. Feel free to suggest new models by opening an issue.
A legacy version of ChemSampler is available in the archived
chem-sampler-legacyrepository.
This repository is work-in-progress. We are actively working on it and hence new features and improved documentation will be updated on a weekly basis.
Create a Conda environment and activate it:
conda create -n chemsampler python=3.10
conda activate chemsamplerChem-Sampler only requires Ersilia and RdKit to run. Please make sure to install the latest version of the Ersilia Model Hub.
ChemSampler allows the user to specify a list of samplers and a list of descriptors from the Ersilia Model Hub. Providing an initial input molecule, ChemSampler will generate new candidates based on the list of samplers and select the best molecules using similarity searches with the provided descriptors. For good results, users should evaulate the type of descriptors used, for example those related to pharmacophore and 3D structure, in addition to traditional Morgan and other 2D descriptors.
The basic functioning of the Chem Sampler is:
from chemsampler.master_sampler import MasterSampler
ms = MasterSampler(sampler_ids=["eos1noy", "eos1d7r"], descriptor_ids = ["eos5axz", "eos1ut3"], unit_timeout_sec = 60)
input_smiles = "CN1CCN(C2=c3ccccc3=Nc3ccc(Cl)cc3N2)CC1"
df = ms.run(input_smiles)
print(df.head)
A keep_smiles and avoid_smiles arguments can be passed to the run function to specify core structures that you might want to maintain or to eliminate from the final list of molecules,
For ease of use, we provide a file in chemsampler/defaults/params.json where the parameters, including the output folder, can be specified. To run Chem-Sampler, simply modify the defaults in the params.json file and run the following:
python scripts/main.py
A report file is created at the end of the run, as well as images of a sample of the molecules generated in each round.
We are adding new features to improve the scaffold hopping capabilities of ChemSampler, particularly thinking about natural products. We will allow for a number of rules (i.e, maintain or not maintain certain core structure) to be specified by the user. Check GitHub Projects to follow the development of new features.
All the code in this repository is licensed under a GPL-v3 License. Please note that individual models fetched through ChemSampler might have different open source licenses.
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