Currently, directly transferring mature AI technologies such as Transformers, pretraining, and scaling laws from the text and image domains to the life omics field encounters widespread adaptation barriers, because omics data is characterized by high-dimensional sparsity, heterogeneity, and scarce labels—causing many AI4S projects to struggle to land in industrial drug R&D scenarios. Domestic AI for Science startup Perturbation Heart has launched the PerturbSeek™ digital life world model ecosystem, driven by a geometric world model, and released the industry's first virtual cell state inference world model, AlphaCell 1.0 Pro. Its core approach is to construct a biological latent space manifold, learn the patterns of cell state transitions, maintain strong generalization capability even with limited training samples, and adapt to different industrial scenarios through elastic parameter scaling. The team pointed out that scaling laws in the omics field have conditions and boundaries—one cannot blindly pile up data and parameters; instead, it is necessary to find the "parameter sweet spot" that balances performance, compute cost, and generalization capability. The team proposed that AI methodologies adapted to life omics need to focus on four core directions: multi-scale cross-modal integration, mining low-rank biological semantics, modeling spatiotemporal dynamic changes, and adapting to scenarios with scarce supervision signals—driving the shift from association identification to causal discovery. The team believes that the dry-wet closed loop will become an industry standard, and that companies do not need to blindly invest in goal-less large-scale data production and automated laboratory construction; instead, they should produce data in a targeted manner around specific drug R&D scenarios and maximize the marginal benefit of investment with an active learning approach. The team is exploring a new "state-centric" AIDD paradigm to fill the needs of complex disease target identification and new-modality drug R&D that the traditional target-centric model struggles to cover. At present, its related platform has completed full-stack adaptation to domestic GPUs and is advancing industrial cooperation with pharmaceutical companies to promote the real-world landing of life omics AI technology in drug R&D scenarios.