In this work, we propose a universal framework, O 2 -GEN, for designing OOMs. O 2 -GEN adopts the virtual particle method , with a 3D pretraining backbone on 3D molecular structures facilitating its rational convergence. In the validation task on two fused-ring molecule datasets, new structures were generated rapidly with nearly 100% validity and novelty. For larger molecules composed of conjugated fragments, O 2 -GEN can generate complete molecules ∼ 30 times faster than Diffusion and Flow models, with a tremendous improvement in the integrality of fragments assemblies. In particular, our framework has great flexibility, allowing designated local region modification of a known structure to empower researchers for fine-tuning molecular properties. Furthermore, O 2 -GEN incorporates an efficient and accurate property selector by fine-tuning the pre-trained 3D molecular structure backbone with a limited amount of Density Functional Theory (DFT) calculation data. This selector can be used to screen the generated molecules according to multiple downstream properties with near DFT accuracy. Through these means, the overarching objective of generating new-structured molecules with multi-objective characteristics is realized.