Further Responses and Clarifications to Reviewer H3jC
We sincerely thank the reviewer for the additional comments and suggestions. Please see our detailed clarifications about technical novelty below:
- 3M-Diffusion studies a novel problem about text-guided molecular graph generations. **The task of generating diverse, novel molecules that match textual descriptions is challenging due to the sparsity and discrete nature of graph structures.** While there is extensive research on using variational autoencoders (VAEs) or diffusion models (DMs) for molecular graph generation to produce diverse, novel or property-related molecules, there is no existing work that leverages the capabilities of VAEs or DMs to generate molecules that are both diverse, novel, and text-matching. **We believe that the 3M-Diffusion model makes significant technical contributions and novelty by establishing a pipeline capable of generating diverse, novel and text-matching molecular graphs.** This pipeline achieves performance comparable to transformer-based models in terms of "Similarity," which measures the model's text-matching capabilities, and excels at generating diverse and novel molecules.
- Furthermore, we are the first to explore the ability of diffusion models on the latent space of molecular graphs to generate novel, diverse and text-matching molecules. **Specifically, our technical contributions and novel approaches include designing the latent space and creating a suitable scoring networks for latent diffusion tailored to the latent space of molecular graphs, enabling our model to function effectively.**
- Also, in our 3M-Diffusion model, **we do not directly utilize HierVAE's encoder; instead, we employ our own trained encoder that aligns graph and text representations, while only incorporating HierVAE's decoder.** Unlike HierVAE's encoder, which learns hierarchical graph representations but can’t understand text, we propose using an aligned encoder from GIN. This aligned encoder is designed to understand textual descriptions by contrastive learning. **Utilizing this encoder's representation for the decoder and making it effectively generate diverse, novel, and text-matching molecules, represents a significant technical novelty and contribution of our model.**