GRIGORIADIS, I. | Search bioRxiv

bioRxiv2026

Linobectide: a mathematical-chemistry modified black-hole algorithmic framework for ORF1p inhibitor design

Computer-aided drug design for conditional biomolecular interfaces requires evaluation across more than one receptor structure, docking pose, or scalar score. LINE-1 ORF1p is treated here as a state-family interface target whose relevant behavior is distributed across receptor microstates, assembly-compatible contact neighborhoods, ligand conformers, and perturbation snapshots. This article presents Linobectide as a mathematical-chemistry CADD workflow centered on a modified black-hole algorithm (MBHA) for persistence-weighted prioritization of putative ORF1p inhibitor candidates. Each molecule is represented as a dossier containing standardized descriptors, docking annotations, interaction-class persistence vectors, finite-action stability traces, graph-localization summaries, SPECTRAL-SAR applicability-domain records, and rank-shift diagnostics. The revised analysis emphasizes numerical reporting endpoints: fixed run parameters, baseline comparators, ablation metrics, rank stability, regeneration fractions, protected-elite fractions, and reproducibility indices. Docking is used as an annotation layer rather than as a stand-alone proof of inhibition. The framework is therefore reported as a transparent computational prioritization protocol that generates testable hypotheses for future biochemical and cellular validation, not as experimental proof of ORF1p inhibition or therapeutic activity. Author summaryDrug-design workflows can become over-dependent on the best docking pose even when an interface target remains functional through alternative contact corridors. Linobectide addresses this issue by ranking candidates only after docking annotations are aggregated across receptor-state and perturbation conditions. The MBHA search promotes a candidate when interaction persistence, finite-action stability, graph localization, SPECTRAL-SAR coherence, applicability-domain support, and reproducibility checks are concordant. The revision removes unsupported claims of performance advantage and replaces them with benchmarkable endpoints that can be compared with docking-only, consensus-docking, and ablated MBHA baselines. The SI Appendix is retained as a figure atlas for state-family construction, graph-localization diagnostics, docking provenance, consensus geometry, and comparative triage.

biophysics↗

bioRxiv2026

Teleport-Stabilized Quantum-Walk Ranking in Near-Tie Neoantigen Regimes

Personalized neoantigen vaccination is a patient-specific decision problem: given a tumors molecular signature--somatic mutations, clonality, RNA expression, and antigen-processing context--we must choose a small, manufacturable peptide set that stays therapeutically relevant under uncertainty. In late-stage pipelines, candidates often collapse into near-ties: binding/presentation estimates, immunogenicity surrogates, and structure-based refinement compress many peptides into narrow score bands, making the final top-K fragile to small shifts in calibration, scaling, sampling, or docking protocols. Similar instability arises in peptide-target discovery when multiple hypotheses remain comparably supported. We introduce a transport-stabilized ranking layer that prioritizes redundancy structure over marginal score differences. Peptides (and structural microstates) become nodes in a patient-conditioned evidence graph; edges encode evidence overlap (motifs/HLA restrictions, processing features, target neighborhoods, pocket/contact fingerprints). We apply symmetry-aware quotient reduction of a normalized graph operator, collapsing near-symmetric neighborhoods into basin units while preserving effective shortlist couplings. Discriminative basin fingerprints are then extracted using coherent quantum-walk transport, |{psi}(t)[>] = e[-]iHt|{psi}(0)[>], with visitation P(v,t) = |[<]v|{psi}(t)[>]|2. Because coherent dynamics are oscillatory and horizon-dependent, we introduce a teleport-consensus channel that mixes unitary transport with restart to yield a stationary marginal suitable for stable ranking,{rho} t+1 = (1 [-] )U{rho}tU{dagger} + {Sigma}jvj|j[>][<]j|, and{pi} i = Tr({Pi}i{rho}). Information-theoretic polygraphs--entropy, dispersion, and consensus traces--quantify stabilization and provide an interpretable tie-breaking audit trail. We demonstrate consistent stabilization across colorectal-cancer contexts spanning peptide-target mechanistic triage, microstate symmetry auditing, multimodal evidence fusion, docking-ensemble geometrization, and patient-specific neoantigen shortlist construction.

cancer biology↗