We developed CellOT, a tool that integrates optimal transport with input convex neural networks to predict molecular responses of individual cells to various perturbations. By learning a map between the unpaired distributions of unperturbed and perturbed cells, CellOT outperforms current methods and generalizes the inference of treatment outcomes in unobserved cell types and patients.