Morphologically Intelligent Perturbation Prediction with FORM
Current computational models are largely restricted to two-dimensional cell morphology modeling, failing to capture realistic three-dimensional structural dynamics under perturbations—thereby limiting the predictive accuracy and functional interpretability of virtual cells. To address this, we propose the first 3D cell morphology prediction framework supporting both unconditional generation and conditional simulation. Our method employs a multi-channel VQGAN-based high-fidelity morphological encoder coupled with a diffusion model to explicitly model perturbation-induced morphological trajectories, trained on over 65,000 3D multi-fluorescence cellular volume images. The framework accurately predicts 3D morphological evolution under chemical or genetic interventions, infers signaling pathway activity, disentangles combinatorial perturbation effects, and generalizes to unseen perturbation types. We further release MorphoEval, a standardized benchmarking suite for quantitative evaluation, establishing a new paradigm and reproducible evaluation standard for 3D virtual cell modeling.