Background: Addressing the Limitations of Haploid Reference Sequences and Allelic Expression Imbalance in R&D for Difficult-to-Treat Solid Tumors

Conventional haploid-based, one-dimensional reference genome analysis systems, which represent a static and linear analytical standard, fail to fully capture the allelic phase structure between homologous chromosome pairs in complex and intractable diseases. This leads to critical blind spots in the interpretation of structural variations and copy number variations. Specifically, the inherent noise associated with single-cell dissociation and the failure to computationally control for feedback fluxes in silico during in vitro experiments represent critical factors that compromise drug uptake and effective prophylactic concentrations in patients. Consequently, the inability to accurately predict target-avoidance mechanisms arising from allelic-specific imbalances during clinical development creates significant data bottlenecks and development barriers in the design of therapeutics for intractable diseases.

Discovery: Demonstrating Cell Resolution-Independent Tensor Synchronization through Diploid Assembly and Pangenome Reference-Based AI Model Operation

This Perspective (Nature Genetics, doi:10.1038/s41588-026-02645-4) introduces a near-perfect genome sequencing modality that combines complete diploid genome assembly and multidimensional pangenome graph models, disruptively surpassing conventional linear reference alignment methods. A deep neural network-based variant interpretation algorithm, driven by a diploid omics matrix, preserves the physical phase information of homologous chromosomes and computationally eliminates batch effects from sequencing. This enables precise tuning of the binding free energy between target ligands and receptors and in silico prediction of rate constants based on differential equations, thereby accurately characterizing the topological variation curves of downstream transcriptional networks and demonstrating molecular biological precision and integrity.

Establishing a Diploid Heterozygous Allele-Specific Pathway Regulation and Reversible Homeostatic Precision Layering Model

This computational systems biology framework, which combines diploid heterozygous genotype structures with multidimensional omics matrices, implements a next-generation mapping interface capable of precisely layering patient molecular phenotypes at the family level. By precisely projecting the fine genetic gradient differences between homologous alleles onto tensor data, the rate-limiting step constants of specific intracellular signaling pathways can be highly predicted. By performing precise up- and down-scaling dynamic simulations of these rate-limiting step constants, a precision genome design-based framework is established to autonomously regulate genetic structure fluxes to maintain reversible homeostasis even under abnormal tumor microenvironment and cellular stress conditions.

Prospects: Establishing a Programmable Computational Genomics Standard and Launching a Next-Generation IND Digital Governance System

This technology architecture completely resets the global new drug R&D governance from the conventional static, post-hoc symptomatic treatment system to a programmable genome precision control infrastructure based on AI-driven, multidimensional tensor modeling. By incorporating diploid reference gradient correction coefficients into the high-throughput screening stage of multinational biotech companies, a strong technological moat can be established by eliminating batch effect deviations between platform devices. Ultimately, it will function as a unique digital healthcare asset that meets the specifications of next-generation bispecific antibodies and mRNA gene therapies, while disruptively shortening the timeline for clinical trial protocols and cGMP operational approvals by global regulatory agencies.