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May 15, 20260 citationsOpen Access

Lo-Shu/Markov-Fiedler Framework v4: Full Size-Controlled Allosteric Benchmark (n=569), Multi-Target Vina Discrepancy (n=46), and Honest Positioning

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YKYao-Kai Kao

Key Points

  • This research aims to validate the Lo-Shu/Markov-Fiedler framework for allosteric classification and analyze discrepancies in multi-target docking.
  • Allosteric classification evaluated on n=569 proteins with size confound correction using OLS residualization.
  • Multi-target Vina discrepancy assessed across 8 targets and 46 ligands.
  • Future tests planned using PASSer/ALLO for comparison.
  • After size confound correction, results showed that CHC lost significance, while lambda_2_res (p=0.04), flex_res (p=0.003), and rig_res (p=0.001) were retained.
  • Random Forest model achieved AUC of 0.882 with a permutation test p-value of 0.005.
  • Partial support for the domain-boundary hypothesis was observed for kinases, but not for HIV-PR.

Abstract

Full validation of the Lo-Shu/Markov-Fiedler C-alpha geometric framework across three tasks. (1) Allosteric classification on n=569 proteins (486 allosteric from UniProt KW-0021+ASBench, 83 orthosteric-only). Size confound (rho (CHC, n) =0. 860) confirmed and corrected via OLS residualization. After correction: CHC loses significance; lambda₂ᵣes (p=0. 04), flexᵣes (p=0. 003), rigᵣes (p=0. 001) retained. RF AUC=0. 882 (permutation p=0. 005). (2) Multi-target Vina discrepancy: 8 targets, 46 ligands, 37/46 under-scored; domain-boundary hypothesis supported for kinases, not HIV-PR (honest negative). (3) Calibrated claim: original lightweight integration, not world-first. Next step: run PASSer/ALLO on same dataset for direct head-to-head comparison.

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Cite This Study

Yao-Kai Kao (2026) studied this question.

synapsesocial.com/papers/6a06b9e2e7dec685947ac86ahttps://doi.org/10.5281/zenodo.20153617
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