Tversky Searches
Run a Tversky search. The Tversky similarity coefficient is a generalization of the Tanimoto coefficient that allows asymmetric weighting between query and reference fingerprints.
Using a and b Parameters
The a and b parameters in the tversky function control the weighting of the query and reference fingerprints, respectively. Adjusting these values allows you to fine-tune the similarity measure to emphasize different aspects of the fingerprints. For example, setting a to a higher value than b will give more weight to the query fingerprint.
Use the FPSim2Engine.tversky function to run a Tversky search.
from FPSim2 import FPSim2Engine
fp_filename = 'chembl_35_v0.6.0.h5'
fpe = FPSim2Engine(fp_filename)
query = 'CC(=O)Oc1ccccc1C(=O)O'
results = fpe.tversky(query, threshold=0.7, a=0.7, b=0.3, n_workers=1)
Use the FPSim2Engine.on_disk_tversky function to run Tversky searches on disk. This method is much slower but suitable when working with databases larger than available RAM. To use ONLY if the dataset doesn't fit in memory.
from FPSim2 import FPSim2Engine
fp_filename = 'chembl_35_v0.6.0.h5'
fpe = FPSim2Engine(fp_filename, in_memory_fps=False)
query = 'CC(=O)Oc1ccccc1C(=O)O'
results = fpe.on_disk_tversky(query, threshold=0.7, a=0.7, b=0.3)
Parallel Processing
The n_workers parameter can be used to split a single query into multiple threads to speed up the search.
This is especially useful when searching large datasets.