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bioRxiv · 10.64898/2026.05.06.723314

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

GRIGORIADIS, I.
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Abstract

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.

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GRIGORIADIS, I.. 2026-05-08. Linobectide: a mathematical-chemistry modified black-hole algorithmic framework for ORF1p inhibitor design. https://doi.org/10.64898/2026.05.06.723314

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