State of the Art Iterative Docking with Logistic Regression and Morgan Fingerprints

02 April 2021, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


There is renewed interest in docking campaigns for ligand-discovery since the advent of ultra-large scale virtual libraries. Using brute-force search, the scale of the libraries suggests highly parallelized compute should be used to avoid years-long computations. This paper reports a re-analysis of docking data from an ultra-large docking campaign at the D4 receptor and AmpC beta lactamase, and demonstrates large reductions in computation time to identify the top-ranked ligands. A search of ‘baseline’ featurizations shows that logistic regression on Morgan fingerprints with pharmacophoric atom invariants can match the reported performance on the same task using message-passing networks. With this approach, an ultra-large docking campaign could be performed in a matter of weeks using consumer-grade CPUs with RDKit and scikit-learn. All code and figures are available at


Logistic regression
RDKit toolkit
Morgan Fingerprints
Molecular docking analysis

Supplementary weblinks


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