Predicting single-cell transcriptional responses to genetic, chemical and cytokine perturbations is a fundamental challenge in computational biology and AI Virtual Cell (AIVC) modeling, with direct implications for drug discovery and the elucidation of gene regulatory networks. Existing approaches often rely on auxiliary cell-state encoders, hierarchical variational autoencoders, dedicated Transformer encoder-decoder modules, or gene-interaction priors to compress high-dimensional expression profiles into latent representations. While effective, these designs increase architectural complexity and may limit scalability and generalizability. This paper introduces OCOO-T, a minimalist flow-matching-based AIVC model for transcriptional perturbation response prediction. OCOO-T utilizes a vanilla Transformer stack that operates directly on continuous gene expression profiles and formulates perturbation response prediction as a continuous-time denoising process. Perturbation embeddings, dosage information, and cell-line/cell-type specificity are integrated through adaptive layer normalization and in-context tokens. Comprehensive evaluations on Tahoe100M, Replogle, and PBMC benchmarks demonstrate that OCOO-T achieves state-of-the-art performance across diverse perturbations and cell types while effectively scaling to long transcriptional profiles through patching and depatching of cellular contexts. By leveraging the simplicity of Transformer-based denoising for single-cell omics, OCOO-T provides an effective and scalable framework for in-silico cellular simulation.

翻译：预测单细胞对遗传、化学和细胞因子扰动的转录响应，是计算生物学和AI虚拟细胞（AIVC）建模中的一个基本挑战，对药物发现和基因调控网络的阐明具有直接影响。现有方法通常依赖于辅助细胞状态编码器、分层变分自编码器、专用Transformer编码器-解码器模块或基因交互先验，将高维表达谱压缩为潜在表示。尽管这些方法有效，但它们增加了架构复杂性，并可能限制可扩展性和泛化能力。本文介绍了OCOO-T，一种基于最小化流匹配的AIVC模型，用于转录扰动响应预测。OCOO-T采用一个直接作用于连续基因表达谱的普通Transformer堆栈，并将扰动响应预测表述为一个连续时间去噪过程。通过自适应层归一化和上下文内令牌，整合了扰动嵌入、剂量信息以及细胞系/细胞类型特异性。在Tahoe100M、Replogle和PBMC基准上的全面评估表明，OCOO-T在多种扰动和细胞类型上实现了最先进的性能，同时通过细胞上下文的拼接和拆解，有效扩展到长转录谱。通过利用基于Transformer去噪在单细胞组学中的简洁性，OCOO-T为硅内细胞模拟提供了一个有效且可扩展的框架。