{'fraction_non_alphanumeric': 0.01820464532328939, 'fraction_numerical': 0.0025109855618330196, 'mean_word_length': 5.754237288135593, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
GES Polypharmacology Fingerprints: A Novel Approach for Drug Repositioning

Polypharmacology is now recognized as an increasingly important aspect of drug design. We previously introduced the Gaussian ensemble screening (GES) approach to predict relationships between drug classes rapidly without requiring thousands of bootstrap comparisons as in current promiscuity prediction approaches. Here we present the GES "computational polypharmacology fingerprint" (CPF), the first target fingerprint to encode drug promiscuity information. The similarity between the 3D shapes and chemical properties of ligands is calculated using PARAFIT and our HPCC programs to give a consensus shape-plus-chemistry ligand similarity score, and ligand promiscuity for a given set of targets is quantified using the GES fingerprints. To demonstrate our approach, we calculated the CPFs for a set of ligands from DrugBank that are related to some 800 targets. The performance of the approach was measured by comparing our CPF with an in-house "experimental polypharmacology fingerprint" (EPF) built using publicly available experimental data for the targets that comprise the fingerprint. Overall, the GES CPF gives very low fall-out while still giving high precision. We present examples of polypharmacology relationships predicted by our approach that have been experimentally validated. This demonstrates that our CPF approach can successfully describe drug-target relationships and can serve as a novel drug repurposing method for proposing new targets for preclinical compounds and clinical drug candidates.