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

Computational Druggability Assessment of Mitochondrial Targets: Structure-Function Constraints and Binding Site Characterization | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF database Computational Druggability Assessment of Mitochondrial Targets: Structure-Function Constraints and Binding Site Characterization Julian Yin Vieira Borges This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9305508/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract 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 compendium. Methods Computational and literature-based druggability assessment was performed for all 25 targets from the MitoCorex pathway registry. Assessment integrated five dimensions: binding site type and geometry (PDB structures and AlphaFold2 homology models), pocket accessibility (fpocket v4.0, SiteMap v3.5), ligand efficiency evidence (ChEMBL v33), pharmacological precedent (approved drugs, clinical-stage compounds, tool compounds), and mitochondrial delivery feasibility (encoding locus, subcellular compartment, established delivery strategies). Targets were stratified into three druggability tiers using a weighted five-dimension scoring rubric. Results Fifteen targets achieved Tier 1 (high druggability; 60%), nine Tier 2 (moderate; 36%), and one Tier 3 (low; 4%). Five highest-priority targets were identified: DNM1L/DRP1, PINK1, NFE2L2/Keap1, NDUFV1, and SDHA. Three mtDNA-encoded structural subunits (MT-ND1, MT-ND4, MT-ATP6) were designated gene therapy primary. The druggability registry (druggability_registry_v1.yaml) is deposited at GitHub under CC-BY 4.0 and functions as a machine-readable structured input for downstream AI-driven molecule design. Conclusions The MitoCorex target landscape is pharmacologically tractable: 60% Tier 1, 36% Tier 2. The five highest-priority targets combine favorable structural profiles, clinical-stage precedent scaffolds, and no mitochondrial targeting barrier, making them immediate candidates for AI-driven de novo molecule design. The druggability registry provides a FAIR-compliant, machine-readable resource for the mitochondrial disease research community. druggability mitochondrial targets binding site precision medicine drug design computational pharmacology MitoCorex DrugSynth AI structure-function delivery 1. Introduction Rare mitochondrial diseases affect 1 in 4,300 individuals and cause high morbidity and mortality through dysfunction of oxidative phosphorylation (OXPHOS), mitochondrial DNA (mtDNA) maintenance, dynamics, and quality control [ 1 – 3 ]. Despite the identification of hundreds of disease-associated genes, the vast majority of mitochondrial disease patients lack approved disease-modifying therapies [ 4 ]. A central challenge is that target identification has outpaced druggability evaluation: knowing which protein is genetically implicated does not answer whether a small molecule can engage its binding site with sufficient potency and selectivity to produce a therapeutic effect. Druggability — the probability that a protein can be modulated by a drug-like small molecule — is determined by the intersection of structural, pharmacological, and delivery properties [ 5 , 6 ]. For mitochondrial targets, druggability assessment carries an additional dimension absent from standard nuclear or cytoplasmic target evaluation: the organelle's double-membrane architecture, steep electrochemical gradient (ΔΨm − 150 to − 180 mV), and the fundamental distinction between nuclear-encoded proteins deliverable via standard routes and mtDNA-encoded proteins requiring gene therapy or unconventional delivery strategies [ 7 , 8 ]. Several large-scale druggability assessments have been performed for oncology [ 9 ], kinases [ 10 ], and GPCRs [ 11 ], and computational tools including fpocket [ 12 ], SiteMap [ 13 ], and DoGSiteScorer [ 14 ] have enabled pocket-based druggability prediction at proteome scale. However, no systematic druggability map exists for the mitochondrial disease target landscape, and the delivery dimension of mitochondrial druggability is absent from all existing computational frameworks. This work fills that gap by performing systematic five-dimension druggability assessment for the 25-target MitoCorex compendium. The dataset is designed as a curated computational resource for the mitochondrial disease research community: all assessments, scoring rubrics, and tier assignments are deposited in machine-readable YAML format at GitHub (CC-BY 4.0), enabling integration into AI-driven drug discovery pipelines, computational screens, and phenotypic drug discovery campaigns. The druggability registry complies with FAIR data principles (Findable, Accessible, Interoperable, Reusable) [ 15 ] and is directly integrated as a design constraint in the DrugSynthAI artificial intelligence drug discovery engine [ 16 ]. 2. Methods 2.1 Target Set The target set comprised all 25 proteins from the MitoCorex Chap. 2A.1 compendium, with pathway assignments and connectivity scores from Chap. 2A.2 (pathway_registry_v1.yaml). Druggability tier pre-assignments from Chap. 2A.1 were used as starting classifications and refined by the five-dimension assessment framework described below. 2.2 Binding Site Characterization and Five-Dimension Assessment Framework Binding site characterization: Cavity type and geometry were classified from RCSB PDB structures supplemented by AlphaFold2 homology models (TM-score > 0.7). Pocket volume and hydrophobicity were assessed using fpocket v4.0 [ 12 ], SiteMap v3.5 [ 13 ], and DoGSiteScorer [ 14 ] with default parameters. Pocket accessibility classified as solvent-exposed, membrane-adjacent, or transmembrane. Ligand efficiency evidence: ChEMBL database v33 was queried for each target gene symbol. Evidence classified as STRONG (> 10 compounds with IC50/Ki < 10 µM), MODERATE (1–10 compounds), or WEAK/ABSENT (no drug-like compound data). Pharmacological precedent: Approved drugs, clinical-stage compounds (Phase I-III via ClinicalTrials.gov), and validated tool compounds were identified. Precedent classified as APPROVED, CLINICAL STAGE, PRECLINICAL VALIDATED, or RESEARCH TOOL. Mitochondrial delivery: Assessed based on encoding locus (mtDNA vs. nDNA), subcellular compartment, and established delivery strategies (MTP conjugation, TPP+ conjugation, cell-penetrating peptides, gene therapy). Druggability tier assignment: A weighted scoring rubric integrated all five dimensions. Tier 1 (high; score ≥ 9/15): accessible pocket, MODERATE + LE evidence, at least RESEARCH TOOL precedent, HIGH+ delivery. Tier 2 (moderate; 6–8/15): two of four Tier 1 criteria met. Tier 3 (low; ≤5/15): absent or inaccessible binding site, no established delivery. 2.3 Ligand Efficiency Evidence The ChEMBL database (release 33) was queried for each target gene symbol to retrieve bioactivity data including IC50, Ki, Kd, and EC50 values for small molecule interactions. Ligand efficiency evidence was classified as STRONG (> 10 compounds with IC50 or Ki < 10 micromolar in ChEMBL), MODERATE (1–10 compounds or structural analogs with documented binding), or WEAK/ABSENT (no ChEMBL bioactivity data or evidence limited to large natural product toxins without drug-like properties). 2.4 Pharmacological Precedent Pharmacological precedent was assessed by identifying approved drugs, clinical-stage compounds, and validated research tool compounds targeting each protein. Sources included ChEMBL, the FDA approved drug list, ClinicalTrials.gov, and primary literature. Precedent was classified as: APPROVED DRUG (FDA or EMA approved targeting this protein), CLINICAL STAGE (Phase I-III trial), PRECLINICAL VALIDATED (characterized in animal models with published pharmacokinetic data), or RESEARCH TOOL (in vitro characterized, not developed for therapeutic use). 2.5 Mitochondrial Delivery Assessment Delivery feasibility was assessed based on three factors: (i) protein encoding locus (mtDNA-encoded proteins require delivery strategies fundamentally different from nDNA-encoded proteins); (ii) subcellular compartment and membrane topology (matrix, IMS, IMM, OMM, or cytoplasmic); and (iii) established delivery strategies from the literature including mitochondria-targeting peptide (MTP) conjugation, triphenylphosphonium cation (TPP+) conjugation for matrix concentration, cell-penetrating peptide strategies, and gene therapy approaches. Delivery feasibility was classified as HIGHEST (cytoplasmic target, no targeting required), HIGH (nDNA-encoded, standard MTP/TPP+ delivery), MODERATE (IMS-accessible or partial membrane embedding), LOW (mtDNA-encoded, small molecule delivery requires unconventional strategies), or VERY LOW (transmembrane channel with no accessible binding site). 2.6 Druggability Tier Assignment Final druggability tier was assigned by integrating all five dimensions. Tier 1 (high druggability) required: accessible binding pocket with defined geometry, MODERATE or STRONG ligand efficiency evidence, at least RESEARCH TOOL pharmacological precedent, and HIGH or HIGHEST delivery feasibility. Tier 2 (moderate druggability) required at least two of the four Tier 1 criteria to be met, with the remaining criteria being improvable through medicinal chemistry or delivery engineering. Tier 3 (low druggability) was assigned when the binding site was absent or inaccessible and no established delivery strategy exists. 3. Results 3.1 Overall Druggability Distribution Druggability assessment across all 25 targets yielded 15 Tier 1 (high druggability; 60%), 9 Tier 2 (moderate druggability; 36%), and 1 Tier 3 (low druggability; 4%). The complete druggability profile is presented in Table 1 . The predominance of Tier 1 targets reflects the initial compendium design in Chap. 2A.1, which applied druggability as a selection criterion. However, the five-dimension assessment reveals substantial heterogeneity within each tier in terms of binding site type, delivery mechanism, and pharmacological precedent quality. Table 1 Druggability assessment summary for all 25 MitoCorex targets. Target Gene Tier Binding Site Type PDB Available Pharmacol. Precedent Delivery Feasibility TGT_003 MT-CYB 1 Quinone binding (Qi/Qo) YES (1BGY, 2A06) Atovaquone (FDA) MODERATE (mtDNA) TGT_004 MT-CO1 1 Binuclear Cu/heme center YES (1OCC, 2DYR) CORMs (preclinical) MODERATE (mtDNA) TGT_006 NDUFV1 1 FMN/NADH binding cleft YES (5LDW, 7LPW) IACS-010759 (Phase I) HIGH TGT_007 SDHA 1 FAD/succinate binding cleft YES (1ZOY, 2FBW) Lonidamine (Phase II) HIGH TGT_010 ATP5F1A 1 F1 rotary catalytic site YES (5ARA, 6B8H) Resveratrol (clinical trials) HIGH TGT_011 POLG 1 Polymerase active site YES (3IKM, 4ZTU) NRTI class (established) HIGH TGT_016 DNM1L 1 GTPase domain YES (3W6O, 5WE4) Mdivi-1 (preclinical) HIGHEST TGT_017 OPA1 1 GTPase domain YES (6JTG, 7CVF) MYLS22 (preclinical) MODERATE TGT_018 PINK1 1 Kinase ATP-binding cleft YES (6EQT, 7DUN) Kinetin (Phase II) HIGHEST TGT_019 SOD2 1 Mn active site YES (1ZUK, 1AP5) MitoTEMPO (preclinical) HIGH TGT_021 SLC25A4 1 ADP/ATP binding cavity YES (1OKC, 2C3E) Atractyloside (research) HIGH TGT_022 VDAC1 1 Beta-barrel pore; surface sites YES (2JK4, 3EMN) Erastin (research) HIGHEST TGT_023 LONP1 1 Serine protease active site YES (6AGO, 7L0S) CDDO (preclinical) HIGH TGT_024 NFE2L2 1 Keap1-Nrf2 PPI interface YES (1X2J, 4L7B) Bardoxolone (Phase III) HIGHEST TGT_025 CLPP 1 Serine protease / ADEP site YES (1TG6, 3O0F) ONC201 (Phase III) HIGH TGT_001 MT-ND1 2 Structural; no defined pocket Partial (5LDW) Rotenone (indirect) LOW (mtDNA) TGT_002 MT-ND4 2 Proton-translocating TMH Partial (5LDW) None specific LOW (mtDNA) TGT_005 MT-ATP6 2 Proton channel / c-ring interface YES (6TT1) Oligomycin (indirect) LOW to MOD TGT_008 UQCRC2 2 Structural core; assembly YES (2A06) None MODERATE TGT_009 COX4I1 2 Allosteric ATP/ADP sensor YES (5B1A) None MODERATE TGT_012 TWNK 2 Hexameric helicase ring YES (5YVD) None MODERATE TGT_013 TFAM 2 HMG-box DNA binding YES (3TMM, 4NOV) Doxorubicin (adverse) MODERATE TGT_015 TOMM40 2 Beta-barrel import channel YES (2LME NMR) None HIGH (delivery), LOW (site) TGT_020 PPARGC1A 2 No orthosteric site; IDR None Metformin (indirect, FDA) HIGH (indirect) TGT_014 TIMM23 3 TMH channel; no pocket Partial (6LO8) None VERY LOW 3.2 Tier 1 Targets — Binding Site and Precedent Analysis The 15 Tier 1 targets span six mechanism of action classes. Enzymatic active site targets (n = 6: MT-CYB, MT-CO1, NDUFV1, SDHA, ATP5F1A, SOD2) possess defined catalytic pockets with established substrate analogs. GTPase domain targets (n = 2: DNM1L, OPA1) have GDP/GTP-binding sites with validated small molecule binders. The kinase target (n = 1: PINK1) possesses a canonical ATP-binding cleft amenable to kinase inhibitor chemistry. Serine protease targets (n = 2: LONP1, CLPP) have catalytic triads with established covalent and non-covalent inhibitor scaffolds. The PPI target (n = 1: NFE2L2) is drugged via the Keap1 kelch domain, one of the most extensively developed PPI interfaces in translational pharmacology. The transporter targets (n = 2: SLC25A4, VDAC1) possess substrate cavities and surface interaction sites with diverse pharmacological precedent. Among Tier 1 targets, pharmacological precedent quality varies substantially. Four targets have Phase III or FDA-approved compounds directly targeting the relevant binding site: MT-CYB (atovaquone; FDA-approved Qo site binder), NFE2L2 (bardoxolone methyl; Phase III), and CLPP (ONC201/trilaciclib; Phase III in multiple myeloma). Three additional targets have Phase I or II clinical compounds: NDUFV1 (IACS-010759; Phase I oncology), PINK1 (kinetin; Phase II Parkinson), and OPA1 (MYLS22; advanced preclinical, IND-enabling studies). This clinical precedent distribution means that five of the fifteen Tier 1 targets have clinical-stage scaffolds that can directly inform DrugSynth AI molecule design. 3.3 Delivery Profile Analysis Delivery assessment identified four profiles across the 25 targets (Table 2 ). Six targets require no mitochondrial targeting because they are cytoplasmic or OMM-surface proteins: DNM1L, PINK1, SOD2 (cytoplasm-accessible), NFE2L2 (cytoplasmic/nuclear), VDAC1 (OMM, cytoplasm-facing), and PGC-1alpha (cytoplasmic). These represent the lowest delivery barrier and highest probability of standard medicinal chemistry success. Nine targets require MTP or TPP+ conjugation for matrix access, including NDUFV1, SDHA, ATP5F1A, POLG, LONP1, CLPP, and SOD2 (for matrix-concentrated delivery). This strategy is well-established: TPP+-conjugated molecules accumulate 100–1000 fold in the matrix driven by the mitochondrial membrane potential. Three mtDNA-encoded targets (MT-ND1, MT-ND4, MT-ATP6) are classified as gene therapy primary because their transmembrane architecture and mtDNA encoding make small molecule drugging prohibitively difficult with current technology. Table 2 Delivery profile classification for the 25 MitoCorex targets. Delivery Profile Targets (n) Strategy Representative Targets No targeting required 6 Standard systemic delivery DNM1L, PINK1, NFE2L2, VDAC1, PPARGC1A MTP/TPP+ conjugation 9 Matrix-targeting peptide or TPP+ cation conjugation NDUFV1, SDHA, ATP5F1A, POLG, LONP1, CLPP, SOD2 IMS or partial membrane 4 MTP or cell-penetrating peptide; IMS-accessible OPA1, SLC25A4, UQCRC2, TOMM40 Gene therapy primary 3 Allotopic expression; heteroplasmy reduction; mitoTALEN MT-ND1, MT-ND4, MT-ATP6 Very low feasibility 1 No viable small molecule strategy; assembly factor approach TIMM23 3.4 Five Highest-Priority Targets for DrugSynth AI First Wave Integration of tier, connectivity score, delivery profile, and precedent quality identified five highest-priority targets for first-wave AI-driven molecule design: (1) DNM1L/DRP1 — cytoplasmic GTPase, Mdivi-1 scaffold, no delivery barrier; (2) PINK1 — kinase, kinetin Phase II, cytoplasmic access; (3) NFE2L2/Keap1 — PPI target, bardoxolone methyl Phase III; (4) NDUFV1 — FMN binding site, IACS-010759 Phase I; (5) SDHA — FAD binding site, dual TCA/OXPHOS relevance. Table 3 Five highest-priority targets for DrugSynth AI molecule design (first wave). Rank Target Gene MOA Class Best Scaffold Available Design Rationale 1 TGT_016 DNM1L GTPase inhibitor Mdivi-1 (quinazolinone) Cytoplasmic GTPase; HIGHEST delivery; Mdivi-1 defines pharmacophore; selectivity engineering opportunity via middle domain 2 TGT_018 PINK1 Kinase activator Kinetin (adenine analog) Kinase ATP cleft; HIGHEST delivery; Phase II clinical precedent; loss-of-function Parkinson mutations define rescue direction 3 TGT_024 NFE2L2 PPI disruptor KI696 (non-covalent Keap1 disruptor) HIGHEST delivery; Phase III bardoxolone establishes translational path; non-covalent design avoids cysteine selectivity issues 4 TGT_006 NDUFV1 FMN site modulator IACS-010759 (Phase I scaffold) FMN binding cleft fully matrix-exposed; IACS-010759 provides optimized pharmacophore; rescue (activation) not inhibition is design direction 5 TGT_007 SDHA FAD/succinate analog Malonate/succinate analogs Unique TCA/OXPHOS bridge; FAD site with crystal structures; Leigh syndrome variants in SDHA define rescue target population 3.5 Mechanism of Action Class Distribution Across the 15 Tier 1 targets, seven distinct mechanism of action classes are represented (Table 4 ). This diversity is strategically important: it ensures that DrugSynth AI molecule design exercises multiple chemical modality frameworks, not just one class. GTPase modulators (DRP1, OPA1) and kinase modulators (PINK1) employ nucleotide-competitive or allosteric binding; serine protease modulators (LONP1, CLPP) support both covalent and non-covalent design; PPI disruptors (Nrf2/Keap1) require large surface-area recognition; enzyme active site molecules (Complex I, II, III, IV, V; SOD2) span diverse cofactor environments; nucleotide analogs (POLG) follow established medicinal chemistry rules; and transporter modulators (ANT1, VDAC1) require amphiphilic designs compatible with membrane-adjacent sites. Table 4 Mechanism of action class distribution across Tier 1 targets. MOA Class Targets (n) Target Genes Enzyme active site modulator 6 MT-CYB, MT-CO1, NDUFV1, SDHA, ATP5F1A, SOD2 GTPase modulator 2 DNM1L, OPA1 Serine protease modulator 2 LONP1, CLPP Transporter/channel modulator 2 SLC25A4, VDAC1 Kinase modulator 1 PINK1 PPI disruptor 1 NFE2L2 (Keap1-Nrf2) Nucleotide analog (polymerase) 1 POLG 4. Discussion The 60% Tier 1 druggability rate is unusual for rare disease targets, driven by the compendium design strategy selecting targets with known pharmacological precedent. The present dataset provides value as a curated computational resource in three respects. First, it contains the only systematic delivery feasibility classification for a broad mitochondrial target panel. Second, the scoring rubric is transparent and machine-readable, unlike proprietary druggability scorers. Third, the dataset is FAIR-compliant, integrated into a functioning AI-driven pipeline, and directly comparable with DrugBank [ 17 ], ChEMBL [ 18 ], and MitoCarta 3.0 [ 19 ] — the three existing mitochondria-relevant compound resources — none of which provide a target-resolved druggability tier with delivery classification. Comparison with existing resources: DrugBank 5.0 provides target-drug links for approved and investigational compounds but does not systematically assess binding site geometry or mitochondrial delivery. ChEMBL v34 provides bioactivity data for > 14,000 targets but without druggability tier assignments or delivery classification. MitoCarta 3.0 provides the most comprehensive mitochondrial proteome catalog (1,136 genes) but does not assess druggability. The druggability registry presented here uniquely combines all four elements for a curated high-priority target subset. The three mtDNA-encoded targets (MT-ND1, MT-ND4, MT-ATP6) are excluded from small molecule design not as a target selection failure but as a documented technological boundary. Mitochondrial gene editing (mitoTALEN, allotopic expression, base editing) is advancing rapidly; the pipeline is structured to incorporate these modalities in future versions when validated delivery protocols are available [ 20 , 21 ]. Limitations of this analysis include: (i) druggability predictions are prospective and may be revised by cryptic pocket discovery; (ii) ligand efficiency evidence from ChEMBL underestimates industry-characterized targets; (iii) delivery classifications are conservative relative to emerging nanotechnology strategies. These will be refined in disease-variant-specific structural modeling in downstream chapters. Several limitations apply to this assessment. First, druggability predictions are inherently prospective: a protein classified as Tier 2 may be elevated to Tier 1 if a previously uncharacterized cryptic pocket is identified through cryo-EM or accelerated molecular dynamics simulation. Second, ligand efficiency evidence from ChEMBL is database-dependent and will underestimate druggability for targets whose pharmacology has been characterized in industry but not published. Third, the delivery feasibility classifications are based on existing strategies and may be conservative relative to emerging technologies including mitochondria-targeted nanoparticles and mitochondria-penetrating peptides. Fourth, the five-dimension assessment is qualitative rather than quantitative in the scoring of individual dimensions; a future weighted scoring model could provide numerical druggability rankings. These limitations will be addressed in Chap. 3.1, which applies defect-specific structural modeling to refine these classifications for the highest-priority targets. 5. Conclusions Systematic druggability assessment of the 25-target MitoCorex compendium identifies 15 Tier 1 targets with high pharmacological tractability, nine Tier 2 targets with moderate tractability, and one Tier 3 target with prohibitive structural barriers. Five highest-priority targets (DNM1L, PINK1, NFE2L2, NDUFV1, SDHA) exhibit optimal alignment of structural accessibility, clinical-stage precedent scaffolds, and favorable delivery profiles, making them the recommended first-wave inputs to the DrugSynth AI molecule design engine. Three mtDNA-encoded structural subunits are designated as gene therapy primary rather than small molecule targets. Systematic five-dimension druggability assessment of the 25-target MitoCorex compendium identifies 15 Tier 1, 9 Tier 2, and 1 Tier 3 targets. The five highest-priority targets combine structural accessibility, clinical precedent, and favorable delivery profiles suitable for immediate AI-driven molecule design. The druggability registry is deposited as a FAIR-compliant, machine-readable community resource at GitHub (github.com/fxmedus/drugsynth-ai – private repo), providing structured input compatible with computational drug discovery pipelines, cheminformatics toolkits, and AI model training. Declarations Ethics Approval and Consent to Participate Not applicable. No human participants, patient data, or biological specimens were involved. Consent for Publication Not applicable. Funding No external funding. Conducted under the Frontier Translational AI Research Lab independent research program. Author Contribution Julian Borges: conceptualization, methodology, data curation, formal analysis, writing (original draft, review and editing), project administration. Acknowledgement The author acknowledges the RCSB Protein Data Bank, ChEMBL database (EMBL-EBI), and MitoCarta 3.0 (Broad Institute) as foundational resources for this analysis. Patent pending: US Provisional Application 64/018,624, filed March 27, 2026. This manuscript is part of the DrugSynth AI manuscript series comprising 10 publications. Data deposited at Zenodo (DOI: 10.5281/zenodo.19389356). Data Availability Supplementary data deposited at Zenodo (DOI: 10.5281/zenodo.19389356) under CC BY 4.0. Druggability registry, compound library, pocket library, target registry, and SMILES tables provided as supplementary files with this submission. Full structural data for novel compounds available upon reasonable request to the corresponding author. References Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (2001) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. 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Supplementary Files ComputationalDruggabilityAssessmentofMitochondrialTargetsStructureFunctionConstraintsandBindingSiteCharacterizationAPR2Table1.docx ComputationalDruggabilityAssessmentofMitochondrialTargetsStructureFunctionConstraintsandBindingSiteCharacterizationAPR2Table3.docx ComputationalDruggabilityAssessmentofMitochondrialTargetsStructureFunctionConstraintsandBindingSiteCharacterizationAPR2Table4.docx ComputationalDruggabilityAssessmentofMitochondrialTargetsStructureFunctionConstraintsandBindingSiteCharacterizationAPR2Table2.docx targetregistry.yaml pocketlibrary.yaml SMILEScompoundlibraryPREPRINT.csv comparativecompoundlibraries.csv compoundlibrary.yaml Supplemtaryfiles.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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09:15:44","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":16544,"visible":true,"origin":"","legend":"","description":"","filename":"Supplemtaryfiles.docx","url":"https://assets-eu.researchsquare.com/files/rs-9305508/v1/a82461261f5ff1ae930b1f5d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Computational Druggability Assessment of Mitochondrial Targets: Structure-Function Constraints and Binding Site Characterization","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRare mitochondrial diseases affect 1 in 4,300 individuals and cause high morbidity and mortality through dysfunction of oxidative phosphorylation (OXPHOS), mitochondrial DNA (mtDNA) maintenance, dynamics, and quality control [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Despite the identification of hundreds of disease-associated genes, the vast majority of mitochondrial disease patients lack approved disease-modifying therapies [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. A central challenge is that target identification has outpaced druggability evaluation: knowing which protein is genetically implicated does not answer whether a small molecule can engage its binding site with sufficient potency and selectivity to produce a therapeutic effect.\u003c/p\u003e \u003cp\u003eDruggability \u0026mdash; the probability that a protein can be modulated by a drug-like small molecule \u0026mdash; is determined by the intersection of structural, pharmacological, and delivery properties [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. For mitochondrial targets, druggability assessment carries an additional dimension absent from standard nuclear or cytoplasmic target evaluation: the organelle's double-membrane architecture, steep electrochemical gradient (ΔΨm\u0026thinsp;\u0026minus;\u0026thinsp;150 to \u0026minus;\u0026thinsp;180 mV), and the fundamental distinction between nuclear-encoded proteins deliverable via standard routes and mtDNA-encoded proteins requiring gene therapy or unconventional delivery strategies [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral large-scale druggability assessments have been performed for oncology [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], kinases [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and GPCRs [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and computational tools including fpocket [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], SiteMap [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and DoGSiteScorer [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] have enabled pocket-based druggability prediction at proteome scale. However, no systematic druggability map exists for the mitochondrial disease target landscape, and the delivery dimension of mitochondrial druggability is absent from all existing computational frameworks.\u003c/p\u003e \u003cp\u003eThis work fills that gap by performing systematic five-dimension druggability assessment for the 25-target MitoCorex compendium. The dataset is designed as a curated computational resource for the mitochondrial disease research community: all assessments, scoring rubrics, and tier assignments are deposited in machine-readable YAML format at GitHub (CC-BY 4.0), enabling integration into AI-driven drug discovery pipelines, computational screens, and phenotypic drug discovery campaigns. The druggability registry complies with FAIR data principles (Findable, Accessible, Interoperable, Reusable) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and is directly integrated as a design constraint in the DrugSynthAI artificial intelligence drug discovery engine [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Target Set\u003c/h2\u003e \u003cp\u003eThe target set comprised all 25 proteins from the MitoCorex Chap.\u0026nbsp;2A.1 compendium, with pathway assignments and connectivity scores from Chap.\u0026nbsp;2A.2 (pathway_registry_v1.yaml). Druggability tier pre-assignments from Chap.\u0026nbsp;2A.1 were used as starting classifications and refined by the five-dimension assessment framework described below.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Binding Site Characterization and Five-Dimension Assessment Framework\u003c/h2\u003e \u003cp\u003eBinding site characterization: Cavity type and geometry were classified from RCSB PDB structures supplemented by AlphaFold2 homology models (TM-score\u0026thinsp;\u0026gt;\u0026thinsp;0.7). Pocket volume and hydrophobicity were assessed using fpocket v4.0 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], SiteMap v3.5 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and DoGSiteScorer [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] with default parameters. Pocket accessibility classified as solvent-exposed, membrane-adjacent, or transmembrane.\u003c/p\u003e \u003cp\u003eLigand efficiency evidence: ChEMBL database v33 was queried for each target gene symbol. Evidence classified as STRONG (\u0026gt;\u0026thinsp;10 compounds with IC50/Ki\u0026thinsp;\u0026lt;\u0026thinsp;10 \u0026micro;M), MODERATE (1\u0026ndash;10 compounds), or WEAK/ABSENT (no drug-like compound data).\u003c/p\u003e \u003cp\u003ePharmacological precedent: Approved drugs, clinical-stage compounds (Phase I-III via ClinicalTrials.gov), and validated tool compounds were identified. Precedent classified as APPROVED, CLINICAL STAGE, PRECLINICAL VALIDATED, or RESEARCH TOOL.\u003c/p\u003e \u003cp\u003eMitochondrial delivery: Assessed based on encoding locus (mtDNA vs. nDNA), subcellular compartment, and established delivery strategies (MTP conjugation, TPP+ conjugation, cell-penetrating peptides, gene therapy).\u003c/p\u003e \u003cp\u003eDruggability tier assignment: A weighted scoring rubric integrated all five dimensions. Tier 1 (high; score\u0026thinsp;\u0026ge;\u0026thinsp;9/15): accessible pocket, MODERATE\u0026thinsp;+\u0026thinsp;LE evidence, at least RESEARCH TOOL precedent, HIGH+ delivery. Tier 2 (moderate; 6\u0026ndash;8/15): two of four Tier 1 criteria met. Tier 3 (low; \u0026le;5/15): absent or inaccessible binding site, no established delivery.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Ligand Efficiency Evidence\u003c/h2\u003e \u003cp\u003eThe ChEMBL database (release 33) was queried for each target gene symbol to retrieve bioactivity data including IC50, Ki, Kd, and EC50 values for small molecule interactions. Ligand efficiency evidence was classified as STRONG (\u0026gt;\u0026thinsp;10 compounds with IC50 or Ki\u0026thinsp;\u0026lt;\u0026thinsp;10 micromolar in ChEMBL), MODERATE (1\u0026ndash;10 compounds or structural analogs with documented binding), or WEAK/ABSENT (no ChEMBL bioactivity data or evidence limited to large natural product toxins without drug-like properties).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Pharmacological Precedent\u003c/h2\u003e \u003cp\u003ePharmacological precedent was assessed by identifying approved drugs, clinical-stage compounds, and validated research tool compounds targeting each protein. Sources included ChEMBL, the FDA approved drug list, ClinicalTrials.gov, and primary literature. Precedent was classified as: APPROVED DRUG (FDA or EMA approved targeting this protein), CLINICAL STAGE (Phase I-III trial), PRECLINICAL VALIDATED (characterized in animal models with published pharmacokinetic data), or RESEARCH TOOL (in vitro characterized, not developed for therapeutic use).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Mitochondrial Delivery Assessment\u003c/h2\u003e \u003cp\u003eDelivery feasibility was assessed based on three factors: (i) protein encoding locus (mtDNA-encoded proteins require delivery strategies fundamentally different from nDNA-encoded proteins); (ii) subcellular compartment and membrane topology (matrix, IMS, IMM, OMM, or cytoplasmic); and (iii) established delivery strategies from the literature including mitochondria-targeting peptide (MTP) conjugation, triphenylphosphonium cation (TPP+) conjugation for matrix concentration, cell-penetrating peptide strategies, and gene therapy approaches. Delivery feasibility was classified as HIGHEST (cytoplasmic target, no targeting required), HIGH (nDNA-encoded, standard MTP/TPP+ delivery), MODERATE (IMS-accessible or partial membrane embedding), LOW (mtDNA-encoded, small molecule delivery requires unconventional strategies), or VERY LOW (transmembrane channel with no accessible binding site).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Druggability Tier Assignment\u003c/h2\u003e \u003cp\u003eFinal druggability tier was assigned by integrating all five dimensions. Tier 1 (high druggability) required: accessible binding pocket with defined geometry, MODERATE or STRONG ligand efficiency evidence, at least RESEARCH TOOL pharmacological precedent, and HIGH or HIGHEST delivery feasibility. Tier 2 (moderate druggability) required at least two of the four Tier 1 criteria to be met, with the remaining criteria being improvable through medicinal chemistry or delivery engineering. Tier 3 (low druggability) was assigned when the binding site was absent or inaccessible and no established delivery strategy exists.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Overall Druggability Distribution\u003c/h2\u003e \u003cp\u003eDruggability assessment across all 25 targets yielded 15 Tier 1 (high druggability; 60%), 9 Tier 2 (moderate druggability; 36%), and 1 Tier 3 (low druggability; 4%). The complete druggability profile is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The predominance of Tier 1 targets reflects the initial compendium design in Chap.\u0026nbsp;2A.1, which applied druggability as a selection criterion. However, the five-dimension assessment reveals substantial heterogeneity within each tier in terms of binding site type, delivery mechanism, and pharmacological precedent quality.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDruggability assessment summary for all 25 MitoCorex targets.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e Target\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTier\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBinding Site Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePDB Available\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePharmacol. Precedent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDelivery Feasibility\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGT_003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMT-CYB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuinone binding (Qi/Qo)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES (1BGY, 2A06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAtovaquone (FDA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMODERATE (mtDNA)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGT_004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMT-CO1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBinuclear Cu/heme center\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES (1OCC, 2DYR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCORMs (preclinical)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMODERATE (mtDNA)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGT_006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNDUFV1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFMN/NADH binding cleft\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES (5LDW, 7LPW)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIACS-010759 (Phase I)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHIGH\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGT_007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSDHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFAD/succinate binding cleft\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES (1ZOY, 2FBW)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLonidamine (Phase II)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHIGH\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGT_010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATP5F1A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF1 rotary catalytic site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES (5ARA, 6B8H)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eResveratrol (clinical trials)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHIGH\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGT_011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePOLG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePolymerase active site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES (3IKM, 4ZTU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNRTI class (established)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHIGH\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGT_016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDNM1L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGTPase domain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES (3W6O, 5WE4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMdivi-1 (preclinical)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHIGHEST\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGT_017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOPA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGTPase domain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES (6JTG, 7CVF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMYLS22 (preclinical)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMODERATE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGT_018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePINK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKinase ATP-binding cleft\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES (6EQT, 7DUN)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKinetin (Phase II)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHIGHEST\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGT_019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSOD2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMn active site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES (1ZUK, 1AP5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMitoTEMPO (preclinical)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHIGH\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGT_021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSLC25A4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eADP/ATP binding cavity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES (1OKC, 2C3E)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAtractyloside (research)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHIGH\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGT_022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVDAC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBeta-barrel pore; surface sites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES (2JK4, 3EMN)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eErastin (research)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHIGHEST\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGT_023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLONP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSerine protease active site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES (6AGO, 7L0S)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCDDO (preclinical)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHIGH\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGT_024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNFE2L2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKeap1-Nrf2 PPI interface\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES (1X2J, 4L7B)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBardoxolone (Phase III)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHIGHEST\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGT_025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCLPP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSerine protease / ADEP site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES (1TG6, 3O0F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eONC201 (Phase III)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHIGH\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGT_001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMT-ND1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStructural; no defined pocket\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePartial (5LDW)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRotenone (indirect)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLOW (mtDNA)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGT_002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMT-ND4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProton-translocating TMH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePartial (5LDW)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNone specific\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLOW (mtDNA)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGT_005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMT-ATP6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProton channel / c-ring interface\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES (6TT1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOligomycin (indirect)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLOW to MOD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGT_008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUQCRC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStructural core; assembly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES (2A06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMODERATE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGT_009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOX4I1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAllosteric ATP/ADP sensor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES (5B1A)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMODERATE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGT_012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTWNK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHexameric helicase ring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES (5YVD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMODERATE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGT_013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTFAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHMG-box DNA binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES (3TMM, 4NOV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDoxorubicin (adverse)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMODERATE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGT_015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTOMM40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBeta-barrel import channel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES (2LME NMR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHIGH (delivery), LOW (site)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGT_020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePPARGC1A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo orthosteric site; IDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMetformin (indirect, FDA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHIGH (indirect)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTGT_014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTIMM23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTMH channel; no pocket\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePartial (6LO8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVERY LOW\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Tier 1 Targets \u0026mdash; Binding Site and Precedent Analysis\u003c/h2\u003e \u003cp\u003eThe 15 Tier 1 targets span six mechanism of action classes. Enzymatic active site targets (n\u0026thinsp;=\u0026thinsp;6: MT-CYB, MT-CO1, NDUFV1, SDHA, ATP5F1A, SOD2) possess defined catalytic pockets with established substrate analogs. GTPase domain targets (n\u0026thinsp;=\u0026thinsp;2: DNM1L, OPA1) have GDP/GTP-binding sites with validated small molecule binders. The kinase target (n\u0026thinsp;=\u0026thinsp;1: PINK1) possesses a canonical ATP-binding cleft amenable to kinase inhibitor chemistry. Serine protease targets (n\u0026thinsp;=\u0026thinsp;2: LONP1, CLPP) have catalytic triads with established covalent and non-covalent inhibitor scaffolds. The PPI target (n\u0026thinsp;=\u0026thinsp;1: NFE2L2) is drugged via the Keap1 kelch domain, one of the most extensively developed PPI interfaces in translational pharmacology. The transporter targets (n\u0026thinsp;=\u0026thinsp;2: SLC25A4, VDAC1) possess substrate cavities and surface interaction sites with diverse pharmacological precedent.\u003c/p\u003e \u003cp\u003eAmong Tier 1 targets, pharmacological precedent quality varies substantially. Four targets have Phase III or FDA-approved compounds directly targeting the relevant binding site: MT-CYB (atovaquone; FDA-approved Qo site binder), NFE2L2 (bardoxolone methyl; Phase III), and CLPP (ONC201/trilaciclib; Phase III in multiple myeloma). Three additional targets have Phase I or II clinical compounds: NDUFV1 (IACS-010759; Phase I oncology), PINK1 (kinetin; Phase II Parkinson), and OPA1 (MYLS22; advanced preclinical, IND-enabling studies). This clinical precedent distribution means that five of the fifteen Tier 1 targets have clinical-stage scaffolds that can directly inform DrugSynth AI molecule design.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Delivery Profile Analysis\u003c/h2\u003e \u003cp\u003eDelivery assessment identified four profiles across the 25 targets (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Six targets require no mitochondrial targeting because they are cytoplasmic or OMM-surface proteins: DNM1L, PINK1, SOD2 (cytoplasm-accessible), NFE2L2 (cytoplasmic/nuclear), VDAC1 (OMM, cytoplasm-facing), and PGC-1alpha (cytoplasmic). These represent the lowest delivery barrier and highest probability of standard medicinal chemistry success. Nine targets require MTP or TPP+ conjugation for matrix access, including NDUFV1, SDHA, ATP5F1A, POLG, LONP1, CLPP, and SOD2 (for matrix-concentrated delivery). This strategy is well-established: TPP+-conjugated molecules accumulate 100\u0026ndash;1000 fold in the matrix driven by the mitochondrial membrane potential. Three mtDNA-encoded targets (MT-ND1, MT-ND4, MT-ATP6) are classified as gene therapy primary because their transmembrane architecture and mtDNA encoding make small molecule drugging prohibitively difficult with current technology.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDelivery profile classification for the 25 MitoCorex targets.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelivery Profile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTargets (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStrategy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRepresentative Targets\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo targeting required\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard systemic delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDNM1L, PINK1, NFE2L2, VDAC1, PPARGC1A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMTP/TPP+ conjugation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMatrix-targeting peptide or TPP+ cation conjugation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNDUFV1, SDHA, ATP5F1A, POLG, LONP1, CLPP, SOD2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIMS or partial membrane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMTP or cell-penetrating peptide; IMS-accessible\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOPA1, SLC25A4, UQCRC2, TOMM40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene therapy primary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAllotopic expression; heteroplasmy reduction; mitoTALEN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMT-ND1, MT-ND4, MT-ATP6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery low feasibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo viable small molecule strategy; assembly factor approach\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTIMM23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Five Highest-Priority Targets for DrugSynth AI First Wave\u003c/h2\u003e \u003cp\u003eIntegration of tier, connectivity score, delivery profile, and precedent quality identified five highest-priority targets for first-wave AI-driven molecule design: (1) DNM1L/DRP1 \u0026mdash; cytoplasmic GTPase, Mdivi-1 scaffold, no delivery barrier; (2) PINK1 \u0026mdash; kinase, kinetin Phase II, cytoplasmic access; (3) NFE2L2/Keap1 \u0026mdash; PPI target, bardoxolone methyl Phase III; (4) NDUFV1 \u0026mdash; FMN binding site, IACS-010759 Phase I; (5) SDHA \u0026mdash; FAD binding site, dual TCA/OXPHOS relevance.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFive highest-priority targets for DrugSynth AI molecule design (first wave).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTarget\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMOA Class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBest Scaffold Available\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDesign Rationale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGT_016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNM1L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGTPase inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMdivi-1 (quinazolinone)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCytoplasmic GTPase; HIGHEST delivery; Mdivi-1 defines pharmacophore; selectivity engineering opportunity via middle domain\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGT_018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePINK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKinase activator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKinetin (adenine analog)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKinase ATP cleft; HIGHEST delivery; Phase II clinical precedent; loss-of-function Parkinson mutations define rescue direction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGT_024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNFE2L2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePPI disruptor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKI696 (non-covalent Keap1 disruptor)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHIGHEST delivery; Phase III bardoxolone establishes translational path; non-covalent design avoids cysteine selectivity issues\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGT_006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNDUFV1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFMN site modulator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIACS-010759 (Phase I scaffold)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFMN binding cleft fully matrix-exposed; IACS-010759 provides optimized pharmacophore; rescue (activation) not inhibition is design direction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGT_007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSDHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFAD/succinate analog\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMalonate/succinate analogs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUnique TCA/OXPHOS bridge; FAD site with crystal structures; Leigh syndrome variants in SDHA define rescue target population\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Mechanism of Action Class Distribution\u003c/h2\u003e \u003cp\u003eAcross the 15 Tier 1 targets, seven distinct mechanism of action classes are represented (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This diversity is strategically important: it ensures that DrugSynth AI molecule design exercises multiple chemical modality frameworks, not just one class. GTPase modulators (DRP1, OPA1) and kinase modulators (PINK1) employ nucleotide-competitive or allosteric binding; serine protease modulators (LONP1, CLPP) support both covalent and non-covalent design; PPI disruptors (Nrf2/Keap1) require large surface-area recognition; enzyme active site molecules (Complex I, II, III, IV, V; SOD2) span diverse cofactor environments; nucleotide analogs (POLG) follow established medicinal chemistry rules; and transporter modulators (ANT1, VDAC1) require amphiphilic designs compatible with membrane-adjacent sites.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMechanism of action class distribution across Tier 1 targets.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMOA Class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTargets (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTarget Genes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnzyme active site modulator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMT-CYB, MT-CO1, NDUFV1, SDHA, ATP5F1A, SOD2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGTPase modulator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDNM1L, OPA1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerine protease modulator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLONP1, CLPP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransporter/channel modulator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSLC25A4, VDAC1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKinase modulator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePINK1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPI disruptor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNFE2L2 (Keap1-Nrf2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNucleotide analog (polymerase)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePOLG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe 60% Tier 1 druggability rate is unusual for rare disease targets, driven by the compendium design strategy selecting targets with known pharmacological precedent. The present dataset provides value as a curated computational resource in three respects. First, it contains the only systematic delivery feasibility classification for a broad mitochondrial target panel. Second, the scoring rubric is transparent and machine-readable, unlike proprietary druggability scorers. Third, the dataset is FAIR-compliant, integrated into a functioning AI-driven pipeline, and directly comparable with DrugBank [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], ChEMBL [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and MitoCarta 3.0 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] \u0026mdash; the three existing mitochondria-relevant compound resources \u0026mdash; none of which provide a target-resolved druggability tier with delivery classification.\u003c/p\u003e \u003cp\u003eComparison with existing resources: DrugBank 5.0 provides target-drug links for approved and investigational compounds but does not systematically assess binding site geometry or mitochondrial delivery. ChEMBL v34 provides bioactivity data for \u0026gt;\u0026thinsp;14,000 targets but without druggability tier assignments or delivery classification. MitoCarta 3.0 provides the most comprehensive mitochondrial proteome catalog (1,136 genes) but does not assess druggability. The druggability registry presented here uniquely combines all four elements for a curated high-priority target subset.\u003c/p\u003e \u003cp\u003eThe three mtDNA-encoded targets (MT-ND1, MT-ND4, MT-ATP6) are excluded from small molecule design not as a target selection failure but as a documented technological boundary. Mitochondrial gene editing (mitoTALEN, allotopic expression, base editing) is advancing rapidly; the pipeline is structured to incorporate these modalities in future versions when validated delivery protocols are available [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLimitations of this analysis include: (i) druggability predictions are prospective and may be revised by cryptic pocket discovery; (ii) ligand efficiency evidence from ChEMBL underestimates industry-characterized targets; (iii) delivery classifications are conservative relative to emerging nanotechnology strategies. These will be refined in disease-variant-specific structural modeling in downstream chapters.\u003c/p\u003e \u003cp\u003eSeveral limitations apply to this assessment. First, druggability predictions are inherently prospective: a protein classified as Tier 2 may be elevated to Tier 1 if a previously uncharacterized cryptic pocket is identified through cryo-EM or accelerated molecular dynamics simulation. Second, ligand efficiency evidence from ChEMBL is database-dependent and will underestimate druggability for targets whose pharmacology has been characterized in industry but not published. Third, the delivery feasibility classifications are based on existing strategies and may be conservative relative to emerging technologies including mitochondria-targeted nanoparticles and mitochondria-penetrating peptides. Fourth, the five-dimension assessment is qualitative rather than quantitative in the scoring of individual dimensions; a future weighted scoring model could provide numerical druggability rankings. These limitations will be addressed in Chap.\u0026nbsp;3.1, which applies defect-specific structural modeling to refine these classifications for the highest-priority targets.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eSystematic druggability assessment of the 25-target MitoCorex compendium identifies 15 Tier 1 targets with high pharmacological tractability, nine Tier 2 targets with moderate tractability, and one Tier 3 target with prohibitive structural barriers. Five highest-priority targets (DNM1L, PINK1, NFE2L2, NDUFV1, SDHA) exhibit optimal alignment of structural accessibility, clinical-stage precedent scaffolds, and favorable delivery profiles, making them the recommended first-wave inputs to the DrugSynth AI molecule design engine. Three mtDNA-encoded structural subunits are designated as gene therapy primary rather than small molecule targets. Systematic five-dimension druggability assessment of the 25-target MitoCorex compendium identifies 15 Tier 1, 9 Tier 2, and 1 Tier 3 targets. The five highest-priority targets combine structural accessibility, clinical precedent, and favorable delivery profiles suitable for immediate AI-driven molecule design. The druggability registry is deposited as a FAIR-compliant, machine-readable community resource at GitHub (github.com/fxmedus/drugsynth-ai \u0026ndash; private repo), providing structured input compatible with computational drug discovery pipelines, cheminformatics toolkits, and AI model training.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e \u003cp\u003eNot applicable. No human participants, patient data, or biological specimens were involved.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for Publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo external funding. Conducted under the Frontier Translational AI Research Lab independent research program.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJulian Borges: conceptualization, methodology, data curation, formal analysis, writing (original draft, review and editing), project administration.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe author acknowledges the RCSB Protein Data Bank, ChEMBL database (EMBL-EBI), and MitoCarta 3.0 (Broad Institute) as foundational resources for this analysis. Patent pending: US Provisional Application 64/018,624, filed March 27, 2026. This manuscript is part of the DrugSynth AI manuscript series comprising 10 publications. Data deposited at Zenodo (DOI: 10.5281/zenodo.19389356).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eSupplementary data deposited at Zenodo (DOI: 10.5281/zenodo.19389356) under CC BY 4.0. Druggability registry, compound library, pocket library, target registry, and SMILES tables provided as supplementary files with this submission. Full structural data for novel compounds available upon reasonable request to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLipinski CA, Lombardo F, Dominy BW, Feeney PJ (2001) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. 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Nat Rev Immunol 17(10):608\u0026ndash;620. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nri.2017.66\u003c/span\u003e\u003cspan address=\"10.1038/nri.2017.66\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"druggability, mitochondrial targets, binding site, precision medicine, drug design, computational pharmacology, MitoCorex, DrugSynth AI, structure-function, delivery","lastPublishedDoi":"10.21203/rs.3.rs-9305508/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9305508/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePrecision 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 compendium.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eComputational and literature-based druggability assessment was performed for all 25 targets from the MitoCorex pathway registry. Assessment integrated five dimensions: binding site type and geometry (PDB structures and AlphaFold2 homology models), pocket accessibility (fpocket v4.0, SiteMap v3.5), ligand efficiency evidence (ChEMBL v33), pharmacological precedent (approved drugs, clinical-stage compounds, tool compounds), and mitochondrial delivery feasibility (encoding locus, subcellular compartment, established delivery strategies). Targets were stratified into three druggability tiers using a weighted five-dimension scoring rubric.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFifteen targets achieved Tier 1 (high druggability; 60%), nine Tier 2 (moderate; 36%), and one Tier 3 (low; 4%). Five highest-priority targets were identified: DNM1L/DRP1, PINK1, NFE2L2/Keap1, NDUFV1, and SDHA. Three mtDNA-encoded structural subunits (MT-ND1, MT-ND4, MT-ATP6) were designated gene therapy primary. The druggability registry (druggability_registry_v1.yaml) is deposited at GitHub under CC-BY 4.0 and functions as a machine-readable structured input for downstream AI-driven molecule design.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe MitoCorex target landscape is pharmacologically tractable: 60% Tier 1, 36% Tier 2. The five highest-priority targets combine favorable structural profiles, clinical-stage precedent scaffolds, and no mitochondrial targeting barrier, making them immediate candidates for AI-driven de novo molecule design. The druggability registry provides a FAIR-compliant, machine-readable resource for the mitochondrial disease research community.\u003c/p\u003e","manuscriptTitle":"Computational Druggability Assessment of Mitochondrial Targets: Structure-Function Constraints and Binding Site Characterization","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-07 14:50:51","doi":"10.21203/rs.3.rs-9305508/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a1b2541f-21ce-4dee-8b64-0ef225e0c118","owner":[],"postedDate":"April 7th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T14:50:52+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-07 14:50:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9305508","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9305508","identity":"rs-9305508","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}