Diffusion models have demonstrated remarkable success in various domains, including molecular generation. However, conditional molecular generation, i.e., targeting specific chemical properties while at the same time generating meaningful samples, remains a fundamental challenge due to an intrinsic trade-off between meeting desired conditions and consistency with the target data distribution. We present TACS, a novel approach to conditional generation that integrates an adaptively controlled plug-and-play "online'' guidance into a diffusion model to drive samples toward the desired properties while maintaining validity and stability. In short, TACS adresses the issue of generated samples deviating from the data distribution during the conditional generation process. To prevent this deviation, we introduce a Time Correction Sampler to control guidance and ensure that the generated molecules remain on the correct manifold at each reverse step of the diffusion process. Our proposed method achieves significant performance in conditional 3D molecular generation and offers a promising approach towards inverse molecular design, potentially facilitating advancements in drug discovery, materials science, and other related fields.