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Structural fingerprints and pharmacophore modeling are methodologies thathave been used for at least two decades in various fields of cheminformatics:from similarity searching to machine learning (ML). Advances in silicotechniques consequently led to combining both these methodologies into a newapproach known as pharmacophore fingerprint. Herein, we propose ahigh-resolution, pharmacophore fingerprint called Pharmacoprint that encodesthe presence, types, and relationships between pharmacophore features of amolecule. Pharmacoprint was evaluated in classification experiments by using MLalgorithms (logistic regression, support vector machines, linear support vectormachines, and neural networks) and outperformed other popular molecularfingerprints (i.e., Estate, MACCS, PubChem, Substructure, Klekotha-Roth, CDK,Extended, and GraphOnly) and ChemAxon Pharmacophoric Features fingerprint.Pharmacoprint consisted of 39973 bits; several methods were applied fordimensionality reduction, and the best algorithm not only reduced the length ofbit string but also improved the efficiency of ML tests. Further optimizationallowed us to define the best parameter settings for using Pharmacoprint indiscrimination tests and for maximizing statistical parameters. Finally,Pharmacoprint generated for 3D structures with defined hydrogens as input datawas applied to neural networks with a supervised autoencoder for selecting themost important bits and allowed to maximize Matthews Correlation Coefficient upto 0.962. The results show the potential of Pharmacoprint as a new, perspectivetool for computer-aided drug design.

Pharmacoprint -- a combination of pharmacophore fingerprint and artificial intelligence as a tool for computer-aided drug design