Artificial intelligence-driven virtual knockout in cancer research: Progress, challenges, and future perspectives
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PUBLIC SUMMARY
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AI-driven virtual knockout simulates gene loss before experiments.
This review covers gene regulatory network, generative, metabolic, and foundation model tools.
An L1-L4 framework is established to assess the reliability of virtual knockout findings.
Virtual knockout prioritizes cancer targets and context-specific vulnerabilities.
Virtual knockout links computational prediction with experimental validation.
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ABSTRACT
Artificial intelligence-driven virtual knockout is emerging as an intervention-oriented computational paradigm that helps move cancer research from descriptive association toward perturbation-informed hypothesis generation and model-derived counterfactual prediction. Virtual knockout simulates biologically meaningful loss-of-function perturbations in genes, pathways, or regulatory nodes. This approach facilitates target prioritization, model-based mechanistic interpretation, screening of combinatorial strategies, and optimization of downstream validation before experimental intervention. In this review, we define virtual knockout as a class of computational frameworks that model the consequences of loss-of-function perturbations and estimate model-derived post-perturbation biological states, while distinguishing strict virtual knockout models from related perturbation-inference paradigms. We summarize major modeling strategies, including gene regulatory network perturbation, trajectory and transcription factor program modeling, generative model-derived counterfactual prediction, constraint-based metabolic simulation, and communication-informed perturbation analysis, and review their applications in driver gene prioritization, context-specific vulnerability discovery, resistance-associated rewiring, tumor microenvironment analysis, metastasis-associated regulator prioritization, drug development, and patient-oriented therapeutic prioritization. We further propose a four-level evidence framework (L1-L4) and discuss major challenges, including data sparsity, tumor heterogeneity, context dependence, and cross-platform inconsistency. Overall, virtual knockout represents a promising bridge between computational inference, experimental validation, and precision oncology, although its current role is best viewed as one of prioritization and decision support rather than direct clinical replacement. -
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Wang X., Liu M., Jia M., et al. (2026). Artificial intelligence-driven virtual knockout in cancer research: Progress, challenges, and future perspectives. The Innovation Oncology 1:100027. https://doi.org/10.59717/j.xinn-oncol.2026.100027
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Wang X., Liu M., Jia M., et al. (2026). Artificial intelligence-driven virtual knockout in cancer research: Progress, challenges, and future perspectives. The Innovation Oncology 1:100027. https://doi.org/10.59717/j.xinn-oncol.2026.100027
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Figure 1.
Conceptual overview of virtual knockout applications in cancer research
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Figure 2.
Analytical landscape of virtual knockout in cancer research.
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Figure 3.
Schematic overview of a virtual knockout-based closed-loop framework for dissecting immune evasion in the tumor immune microenvironment and guiding target prioritization and validation
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Figure 4.
Schematic overview of a virtual knockout-based framework for linking in silico perturbation to metastatic phenotypes and downstream validation
