Paper: Training-free Multi-objective Diffusion Model for 3D Molecule Generation (ICLR-2024)

Reviewer comment (Reproducibility):
Can the authors provide more information about the property checker and explain how the boundary is determined.

Author response:
Thanks for your constructive suggestions! We do believe that the border decision of chaotic and semantic stages is important. To do this, we visualize generated molecules in the range of $t \in [1000,0]$ at 50-step intervals using the pre-trained diffusion model, without defining an objective. We empirically found most generated molecules in $t=400$ are already widely distributed and have certain shapes. Thus, we established 400 as the boundary. For other noise schedules, we can also first visualize the generated process, and decide an appropriate range. 

The property checker is to evaluate whether the property function of the input (i.e., the molecules generated at $t$) is out of range. If the output value falls outside the property function's range, this value will not be used to guide the direction to prevent instability of the whole process. We provided more details about the property checker on page 5 of the revised manuscript.

The main reason that we distinguish chaotic and semantic stages, is that the judgment of the property checker always returns 'not guide' during the chaotic stage. To improve efficiency, we explicitly provide guidance only during the semantic stage. An ablation study was conducted in A.4.1  on two conditioned properties HOMO/Gap. The results indicated that there is no significant difference in guidance, whether the chaotic stage is included or not.  

| Guide from | MAE 1 | MAE 2 |
|------------|-------|-------|
| 400        | 262   | 489   |
| 1000       | 271   | 465   |

Reviewer comment (Evaluation):
In ablation studies, the reviewer agrees that the MAE curve for HOMO demonstrate the existence of the two stage in generation process. However, for the LUMO curve, no significant increase is observed, what are the possible explanations?

Author response:
Thanks for your question! The value range of LUMO in the training dataset is indeed larger than that of HOMO. In fact, the range of HOMO is small [-8,-4] compared to the LUMO range [-4,4].  This discrepancy makes the neural predictor for HOMO less sensitive to input molecules. Even when the input deviates significantly from valid molecules, the predictor doesn't produce extreme values, which results in a seemingly small MAE. However, it's worth noting that the MAE of HOMO decreases rapidly after 400 steps.

Reviewer comment (Theory):
In proof of proposition 4, why A(Rx, h) equals to RA(x, h) while the property predictor A is invariant? Besides, there is a typo on the numerator after the second equals sign.

Author response:
Thanks for your questions! We have revised the error and provided new proof demonstrating our SDE is also equivariant. Please find it in Appendix A.2.2.

Reviewer comment (Writing):
In equation 8, n indicates the number of samples, not m.

Author response:
We really appreciate your careful reading! We have revised these typos in the manuscript.

Reviewer comment (Writing):
A typo at the beginning of the last paragraph in related works.

Author response:
We really appreciate your careful reading! We have revised these typos in the manuscript.

Reviewer comment (Experiments):
I think the question about chaotic and semantic stages is very important. Authors briefly talk about it in Section 4.1 and experimentally illustrate the importance of this issue in Figure 7. Besides, authors mention a certain "property checker" twice in Section 4.1, however never expand on it. I find it very critical to have a clear understanding of how the sampling stage is taken into account. I would be also interested in seeing the corresponding ablation study (e.g. if to take / not to take into account the chaotic stage).

Author response:
Thanks for your constructive suggestions! We do believe that the border decision of chaotic and semantic stages is important. To do this, we visualize generated molecules in the range of $t \in [1000,0]$ at 50-step intervals using the pre-trained diffusion model, without defining an objective. We empirically found most generated molecules in $t=400$ are already widely distributed and have certain shapes. Thus, we established 400 as the boundary. For other noise schedules, we can also first visualize the generated process, and decide an appropriate range. 


The property checker is to evaluate whether the property function of the input (i.e., the molecules generated at $t$) is out of range. If the output value falls outside the property function's range, this value will not be used to guide the direction to prevent instability of the whole process. We provided more details about the property checker on page 5 of the revised manuscript.


The main reason that we distinguish chaotic and semantic stages, is that the judgment of the property checker always returns 'not guide' during the chaotic stage. To improve efficiency, we explicitly provide guidance only during the semantic stage. An ablation study was conducted in A.4.1  on two conditioned properties HOMO/Gap. The results indicated that there is no significant difference in guidance, whether the chaotic stage is included or not.  

| Guide from | MAE 1 | MAE 2 |
|------------|-------|-------|
| 400        | 262   | 489   |
| 1000       | 271   | 465   |

Reviewer comment (Experiments):
While one of the main contributions of this work is multi-condition guidance mechanism, in my opinion a more solid experimental validation of the proposed method is required: Combinations of properties considered in Table 2 look a bit strange. Why is dipole moment used in 4 out of 5 considered combinations as Property 2?

Author response:
This is because we want to fairly compare our model with EEGSDE. They selected three combinations which all contain dipole. So we added a new combination HUMO and LUMO. And following your suggestion, we added two more combinations: LUMO and gap, HOMO and gap. Please see Table 2 in the manuscript.

Reviewer comment (Experiments):
While one of the main contributions of this work is multi-condition guidance mechanism, in my opinion a more solid experimental validation of the proposed method is required: Besides, correlation 0.22 doesn't seem too high – aren't there better combinations?

Author response:
Yes. We found LUMO/gap combination has a higher correlation 0.89. We conducted the experiment on LUMO/gap and obtained a superior performance. Please see Table 2 in the manuscript.

Reviewer comment (Experiments):
While one of the main contributions of this work is multi-condition guidance mechanism, in my opinion a more solid experimental validation of the proposed method is required: It would be interesting to see some experiments with combination from Figure 3(c). Again, $(\Delta\epsilon, \epsilon_{\text{HOMO}}, \epsilon_{\text{LUMO}})$ seems to be a good candidate for it.

Author response:
Thanks for your comments! We introduced an additional experiment for y1 = HOMO, y2=LUMO, and y3=Gap, where y1, y2, y3 satisfy the relation in Figure 3(c). We designed two scenarios, one is that y3 is conflicted with y1 and y2, and the other is y3 is consistent with y1 and y2. In the first scenario, the properties HOMO, LUMO and Gap were independently sampled, introducing the possibility of conflicting requirements. Consequently, no molecule could perfectly satisfy the targeted properties, yet MUDM still demonstrated commendable performance. In contrast, the second scenario did not present any inherent conflict among the three properties since Gap is computed from the other two properties. As expected, the performance in this conflict-free setting was superior, as indicated by the lower mean absolute errors (MAEs). These results not only underscore the robustness of MUDM in handling multi-objective tasks with complex inter-property relationships but also highlight its capability to generate feasible molecular structures even in the presence of potential property conflicts.

| Conflict | MAE 1 | MAE 2 | MAE 3 |
|----------|-------|-------|-------|
| Yes      | 479   | 545   | 678   |
| No       | 283   | 392   | 255   |

Reviewer comment (Experiments):
_"We found it is important to set reasonable guidance weights for multi-condition guidance"_ – how did authors find these weights and how much do the final results depend on these weights?

Author response:
Thanks for your questions! To solve your concern we did a new ablation study on the properties LOMO/gap in Appendix A.4.3. It shows how different weights influence the performance. We decided the hyper-parameters by grid search, i.e., search from (1,1), (1,2), (2,1), (3,1), (1,3) on a small batch of generated molecules. Different weights have impacts on the final results. But in most cases, we can simply set all weights as 1.

| Weight 1 | Weight 2 | MAE 1 | MAE 2 |
|----------|----------|-------|-------|
| 1        | 1        | 361   | 228   |
| 1        | 2        | 540   | 456   |
| 2        | 1        | 393   | 342   |
| 1        | 3        | 687   | 545   |
| 3        | 1        | 613   | 526   |

Reviewer comment (Evaluation):
How does the sampling time depend on the number of MC samples? I would be also interested to see the comparison of the sampling time with other methods.

Author response:
Thanks for your question. The sampling time for a batch of 20 molecules is 77.4 seconds when the number of MC samples is 1, And it is 288.9 seconds as the number of MC samples is equal to 10. The experiments are taken on a single GPU (A100). EEGSDE has roughly the same sampling time. However, it should be noted that EEGSDE needs ~3 days to train a new diffusion model before sampling.

Reviewer comment (Experiments):
As authors discuss in Section 4.1, point estimation used in DPS is very inaccurate, especially in case of 3D molecule generation. It would be interesting to see some experiments with DPS (e.g. for single property conditioning) and comparison with the proposed MC-based method.

Author response:
Thanks for your suggestions. We did this ablation study on the properties LUMO/gap in Appendix A.4.2 and found the performance of MC sampling is better than DPS.

| Sampling method | MAE 1 | MAE 2 |
|-----------------|-------|-------|
| DPS             | 472   | 396   |
| MC sampling     | 361   | 228   |

Reviewer comment (Experiments):
I had an impression that adding another gradient to the denoising process (i.e. equation 5) in reality would lead to the high instability of the sampling process. Did you experience any issues of this kind?

Author response:
Thanks for your concerns. We found the guidance can be unstable in the early stage during the denoising process. However, we only apply guidance after 400 steps. In this stage, the estimated molecules are more valid and the gradient is relatively stable. In addition, MC sampling method, property checker, as well as time-travel technique are helpful to mitigate this issue.

Reviewer comment (Experiments):
How did you select the target property values when sampling? Did you randomly sample these values from the distribution taken from the training set?

Author response:
Yes, you are absolutely right. It is also the experimental setting used in the baseline papers.

Reviewer comment (Experiments):
Following question 1 discussed in "Weaknesses", how does the border between chaotic and semantic stage depend on the type of noise schedule? I.e. linear, cosine, polynomial, etc.

Author response:
Thanks for your constructive suggestions! We do believe that the border decision of chaotic and semantic stages is important. To do this, we visualize generated molecules in the range of $t \in [1000,0]$ at 50-step intervals using the pre-trained diffusion model, without defining an objective. We empirically found most generated molecules in $t=400$ are already widely distributed and have certain shapes. Thus, we established 400 as the boundary. For other noise schedules, we can also first visualize the generated process, and decide an appropriate range.

Reviewer comment (Experiments):
To make their results more convincing, they should compare their method with more latest state-of-the-art methods.

Author response:
Thanks for your suggestions! Our proposed MUDM is designed for training-free multi-objective 3D molecule generation based on off-the-shelf pre-trained diffusion model and time-independent property prediction functions. So MUDM relies on minimal prerequisites. However, our baselines are very strong, where they can conduct additional trainings to enhance the alignment of generated molecules with objectives. One retrained the diffusion model for multiple objectives, and the other trained time-dependent property prediction functions. Despite these alignments, MUDM can still achieve superior performance, which is sufficient to demonstrate its effectiveness.

Reviewer comment (Experiments):
It remains unclear whether the proposed method is sensitive to the hyper-parameters and how to setup the values in general cases.

Author response:
Thanks for your comments. The sensitivity of MUDM to hyper-parameters is an essential aspect of our study. To address this, we have conducted comprehensive ablation studies, with detailed results included in the appendix of our revised submission. These studies investigate the influence of different hyper-parameters on the model's performance. 

**Ablation Study**: 
    
- **Influence of Guidance Step (Appendix A.4.1)**: We explored how different guidance steps during the diffusion process affect the model’s accuracy. We found that setting guidance step from 400 can achive a balance between the computation and performance.
   
 - **Influence of Condition Weights (Appendix A.4.3)**: Another critical aspect we examined is the influence of condition weights on multi-objective molecule generation. Our findings indicate that varying these weights allows for nuanced control over the trade-offs between different properties. This is particularly crucial when dealing with conflicting objectives, as it enables the model to prioritize certain properties over others based on the specific requirements of the generation task. In practice, we can simply set all weights as 1.

Reviewer comment (Experiments):
Lack of description of the details of the datasets.

Author response:
Thanks for this concern! We have added more details of the datasets in Appendix A.6.

Reviewer comment (Experiments):
To make their results more convincing, they should compare their method with more latest state-of-the-art methods.

Author response:
Thanks for your suggestions! Our proposed MUDM is designed for training-free multi-objective 3D molecule generation based on off-the-shelf pre-trained diffusion model and time-independent property prediction functions. So MUDM relies on minimal prerequisites. However, our baselines are very strong, where they can conduct additional trainings to enhance the alignment of generated molecules with objectives. One retrained the diffusion model for multiple objectives, and the other trained time-dependent property prediction functions. Despite these alignments, MUDM can still achieve superior performance, which is sufficient to demonstrate its effectiveness.

Reviewer comment (Experiments):
It remains unclear whether the proposed method is sensitive to the hyper-parameters and how to setup the values in general cases.

Author response:
Thanks for your comments. The sensitivity of MUDM to hyper-parameters is an essential aspect of our study. To address this, we have conducted comprehensive ablation studies, with detailed results included in the appendix of our revised submission. These studies investigate the influence of different hyper-parameters on the model's performance. 

**Ablation Study**: 
    
- **Influence of Guidance Step (Appendix A.4.1)**: We explored how different guidance steps during the diffusion process affect the model’s accuracy. We found that setting guidance step from 400 can achive a balance between the computation and performance.
   
 - **Influence of Condition Weights (Appendix A.4.3)**: Another critical aspect we examined is the influence of condition weights on multi-objective molecule generation. Our findings indicate that varying these weights allows for nuanced control over the trade-offs between different properties. This is particularly crucial when dealing with conflicting objectives, as it enables the model to prioritize certain properties over others based on the specific requirements of the generation task. In practice, we can simply set all weights as 1.

Reviewer comment (Experiments):
Lack of description of the details of the datasets.

Author response:
Thanks for this concern! We have added more details of the datasets in Appendix A.6.

Reviewer comment (Theory):
Could the authors give some insights into why the proposed method can handle the potential properties conflict?

Author response:
Thanks for the reviewer's supportive evaluation of our work. We are trying our best to address your concerns with the following answers.

> Q: Could the authors give some insights into why the proposed method can handle the potential properties conflict?

1. **Modeling Property Relationships as a Probabilistic Graph**: MUDM models the relationships between multiple properties as a probabilistic graph. This allows the method to understand and incorporate the dependencies and potential conflicts between different properties. By doing so, MUDM can adjust the generation process to account for these relationships, thereby effectively managing conflicts.

2. **Weighted Sum of Gradients for Multiple-Objective Guidance**: In cases of multi-objective tasks, MUDM computes the guidance as a weighted sum of the gradients of each property with respect to the latent variable. This approach takes into account the interdependencies between properties. By considering these dependencies in the calculation, MUDM can navigate through potential conflicts by prioritizing certain properties over others based on their relative importance or the specific requirements of the generation task.

3. **Flexibility in Handling Conflicting Objectives**: The design of MUDM inherently allows for a degree of flexibility in handling conflicting objectives. This flexibility is evident in how MUDM approaches the generation process, adapting to different scenarios where properties may have varying degrees of conflict or compatibility.

4. **Experimental Evidence**: The effectiveness of MUDM in handling property conflicts is supported by experimental results. For instance, when examining combinations of quantum properties, MUDM demonstrated superior performance compared to methods like conditional EDM and EEGSDE, even in scenarios with correlated property combinations that could potentially introduce conflicts. We added a new experiment in the revised manuscript A.5. In this experiment, when there are conflicts for the three properties, the proposed framework can still produce molecules with closed targeted properties.

5. **Advanced Sampling Techniques**: The utilization of advanced techniques like Monte Carlo sampling and the 'time-travel' mechanism during the inference stage contributes to the robustness of MUDM. These techniques ensure more accurate property estimations and help in navigating through the complexities introduced by conflicting properties.

We've made updates based on the feedback provided, and we believe that these changes substantially improve the manuscript. We're grateful for the valuable comments and thank you for your time and expertise.