Unified modeling of cellular responses to diverse perturbation types

Predicting cellular responses to perturbations is essential for understanding gene regulation and advancing drug development. Most existing in silico perturbation models treat perturbation–cell interactions with simplistic fusion strategies that overlook the hierarchical nature of regulatory processes, leading to inaccurate predictions and poor generalization across perturbation types. We present X-Pert, a universal in silico perturbation model that explicitly captures both gene–perturbation interactions and gene–gene dependencies through attention mechanisms. X-Pert flexibly handles diverse perturbation inputs across different types and combinations, and quantitatively models their dosage- and efficacy-dependent effects, all within a unified representation space. This enables accurate prediction of unseen, combinatorial, and dose- or efficacy-dependent responses of various perturbation types. Across benchmarks spanning genetic and chemical perturbations, X-Pert consistently demonstrates superior performance at both gene and pathway levels. Moreover, its unified representation space enables downstream analyses such as perturbation retrieval and drug–gene association discovery. By integrating data across perturbation types, experimental platforms, and cell contexts, X-Pert establishes a versatile and generalizable foundation for in silico perturbation, enabling broad applications in biological and therapeutic discovery.