Computational Druggability Assessment of Mitochondrial Targets: Structure-Function Constraints and Binding Site Characterization

Background : Precision drug design for mitochondrial disease requires a systematic evaluation of whether each drug target possesses a pharmacologically tractable binding site. Druggability is determined by the intersection of structural accessibility, ligand efficiency evidence, pharmacological precedent, and mitochondrial delivery feasibility. No comprehensive druggability map exists for the curated mitochondrial target landscape defined by the MitoCorex Chapter 2A.1 compendium. Methods : We performed computational and literature-based druggability assessment for all 25 targets from the MitoCorex pathway registry (Chapter 2A.2). Assessment integrated five dimensions: binding site type and geometry (from PDB structures and homology models), pocket accessibility (solvent exposure versus transmembrane embedding), ligand efficiency evidence (ChEMBL bioactivity database), pharmacological precedent (approved drugs, clinical-stage compounds, and validated tool compounds), and mitochondrial delivery feasibility (encoding locus, subcellular compartment, and known delivery strategies). Targets were stratified into three druggability tiers. Results : Fifteen targets achieved Tier 1 (high druggability), nine achieved Tier 2 (moderate druggability), and one achieved Tier 3 (low druggability). Five highest-priority targets were identified: DNM1L (DRP1; cytoplasmic GTPase, no delivery barrier, Mdivi-1 scaffold), PINK1 (kinase domain, clinical-stage kinetin), NFE2L2/Nrf2 (PPI target, bardoxolone Phase III), NDUFV1 (FMN enzyme, IACS-010759 clinical scaffold), and SDHA (FAD enzyme, dual TCA/OXPHOS relevance). Delivery analysis identified six targets requiring no mitochondrial targeting, nine requiring standard matrix-targeting peptide conjugation, and three for which gene therapy is the primary therapeutic strategy (mtDNA-encoded structural subunits). Mechanism of action classes covered GTPase modulators, kinase modulators, serine proteases, PPI disruptors, enzyme active sites, nucleotide analogs, transporters, and upstream transcriptional regulators. Conclusions : The MitoCorex target landscape is pharmacologically tractable: 60% of targets achieve high druggability and 36% achieve moderate druggability. The five highest-priority targets share favorable structural, delivery, and precedent profiles that support immediate molecule design by DrugSynth AI. The druggability registry (druggability_registry_v1.yaml) is deposited at Zenodo and functions as a structured input for Chapter 3.1 (defect modeling) and Chapter 4.1 (DrugSynth AI design engine specification).