VCCV: conservative transcriptomic corroboration for measurement prioritization of computational drug–target hypotheses

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ID: 329212
2026
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Abstract
MOTIVATION: Computational drug-target interaction (DTI) models nominate plausible binders but cannot determine which candidate best accounts for an observed cellular response. Perturbational transcriptomics offers orthogonal mechanistic evidence, yet pharmacology-to-genetics mismatch, incomplete reference coverage, and non-specific stress programs make simple signature matching unreliable. This motivates a principled integration layer that corroborates hypotheses conservatively, abstains under global mismatch, and prioritizes informative follow-up measurements when the evidence remains ambiguous. RESULTS: We present Virtual-to-Cellular Corroboration for Validation (VCCV), a model-agnostic posterior-triage layer for pre-trained DTI models. VCCV updates calibrated DTI working weights with context-aligned perturbational evidence using a near-identity affine map and exact covariance transport. Within the stated Gaussian class, exact transport is the unique uncertainty update that preserves posterior-odds comparisons under invertible affine changes of measurement coordinates. VCCV also introduces an empirical warning branch for abstention and converts residual ambiguity into compact follow-up gene panels, using a submodular objective for deep near-ties. Each query is assigned one of three actionable states: a target-resolved hypothesis, an abstention, or a prioritized panel. Across five DTI models, VCCV improved discrimination (paired ROC-AUC gains 0.021-0.037) and reduced negative log-likelihood in every case. Additional evaluations showed improved ranking on the same-cell-supported endpoint, warning-score discrimination of strong-response profiles (ROC-AUC 0.876), and better recovery of full-coordinate leading hypotheses by selected panels than by random panels. Across these retrospective kinase-focused evaluations, VCCV provides a principled bridge from computational nomination to conservative, measurement-directed cellular corroboration. AVAILABILITY AND IMPLEMENTATION: Source code of VCCV is publicly available at https://github.com/bio-ai-source/VCCV. SUPPLEMENTARY INFORMATION: Supplementary data are available online.
Reference Key
openalex_W7213661785 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Haihui Huang, Yanan Zhou, Dingkui Kang, Yong Liang
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Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag694
URL
https://doi.org/10.1093/bioinformatics/btag694
Keywords Keywords not found

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