HD_BPMDS: a curated binary pattern multitarget dataset of Huntington's disease-targeting agents

Area of Science:

• * Pharmacology
• * Medicinal Chemistry
• * Neurodegenerative Disease Research

Background:

• * Huntington's disease (HD) is an orphan neurodegenerative disorder with poorly understood molecular mechanisms, hindering drug development.
• * Current approved HD treatments only manage symptoms, not the underlying disease progression.
• * Significant challenges in HD drug discovery include identifying novel targets and effective lead molecules.

Purpose of the Study:

• * To compile and curate a comprehensive landscape of pre-clinical small-molecule agents targeting Huntington's disease.
• * To annotate these agents with molecular patterns, physicochemical properties, and drug targets.
• * To facilitate a better understanding of the interactome involved in HD initiation and progression.

Main Methods:

• * Manual compilation and curation of pre-clinical small-molecule agents evaluated for HD.
• * Annotation of agents with substructural molecular patterns (binary code), physicochemical properties, and drug targets.
• * Linking curated data to benchmark databases like PubChem, ChEMBL, and UniProt.

Main Results:

• * A curated database of HD-targeting small-molecule agents and their properties was generated.
• * Annotation with substructural patterns enabled the creation of target-specific and -unspecific fingerprints.
• * These fingerprints can determine the (poly)pharmacological profiles of diverse molecules.

Conclusions:

• * The curated modulator landscape provides crucial data awareness for HD drug discovery.
• * The generated fingerprints offer a novel approach to understanding drug-target interactions in HD.
• * This resource can aid in the translation of pre-clinical findings into effective clinical therapies for Huntington's disease.