Paper: DiffSim: Aligning Diffusion Model and Molecular Dynamics Simulation for Accurate Blind Docking (ICLR-2024)

Reviewer comment (Novelty):
Although the technical novelty of DiffSim can be understood if we follow the DiffDock research line, from the AI for MD literature, there have been several published works on using denoising diffusion for MD simulation [1,2,3] that are not cited or compared in this work. The reviewer would like to know the authors’ feedback on this.

Author response:
Dear Reviewer HbHG,

Thank you for your feedback and for bringing the AI for MD literature to our attention. 

Our primary motivation for developing this method was to focus on the integration of symbolic software and neural networks to enhance docking accuracy and introduce a sense of trustworthiness. We recognize the importance of AI for MD, particularly in terms of its time efficiency and other advancements. And we will refine our work by incorporating both branches of research.

Thank you!

Reviewer comment (Experiments):
What if MD combined with an all-atom diffusion model such as Geodiff [1], it feels that an all-atom diffusion method is more compatible with MD.

Author response:
Combining MD with an all-atom diffusion model such as Geodiff is an interesting idea. While our paper focuses on aligning reverse diffusion and MD trajectories, exploring the compatibility and potential benefits of integrating all-atom diffusion models with MD is an avenue worth investigating.

Reviewer comment (Experiments):
Also, what is the pocket RMSD? or if you just use holo protein/pocket as initial docking?

Author response:
We use holo protein as initial conformation for protein, thus not considering protein RMSD.

Reviewer comment (Evaluation):
It might be valuable to include a discussion on the time efficiency of molecular dynamics (MD) simulations in this stage, particularly in the context of protein MD.

Author response:
Admittedly, MD simulation does take considerable time. Time efficiency is the major focus we would like to improve in future work.

Reviewer comment (Theory):
The discussion between the connection of diffusion and MD is weak, and the proposed aligning method seems not to be solid. Lemma 3.1 simply states that the reverse diffusion process has the same differential form of a Langevin dynamics. This is known since 2020. But, the per-step marginal distributions of reverse dynamics should be very much different, therefore a step-wise bounding between reverse diffusion and MD trajectories is not solid in theory. More specifically, Langevin dynamics sample from a Boltzmann distribution under the energy function, while intermediate reverse diffusion steps sample from a Gaussian, changing as $t$ evolves. This introduces a variable "energy" (log probability) in reverse diffusion models. Authors would refer to [1] [2] or [3] for deeper discussions between diffusion and MD.

Author response:
Dear Reviewer jMvQ,

Thank you for your comments on our work. We appreciate your insights and the references you provided for further discussions in terms of the connection between MD and diffusion.

The step-wise bounding approach may not be optimal. In light of your feedback, we are delving deeper into the literature and consider theoretically compatible approaches to aligning reverse diffusion and MD trajectories, taking into account the differences in sampling distributions.

Once again, we express our gratitude for your time and effort in reviewing our paper.

Reviewer comment (Experiments):
More analysis of alignment loss function forms could be useful, and also ablation study isolating active learning benefits would be informative.

Author response:
Dear Reviewer uuBj,

Thank you sincerely for your valuable feedback on our paper. 

We plan to conduct analysis on the alignment loss function forms in our future research and expand our benchmark comparisons beyond the current scope. Regarding computational efficiency, MD simulations are indeed computationally expensive, and we are exploring faster alternatives that can still provide reliable results. 

We are committed to addressing these concerns in our future endeavors. 

Thank you once again for your time and efforts in reviewing our work.

Reviewer comment (Experiments):
Testing on more diverse protein-ligand complexes beyond PDBBind. Currently, the benchmark comparison is limited. Some other papers are necessary to be compared. Such as, E3Bind, https://openreview.net/forum?id=sO1QiAftQFv, FABind, https://arxiv.org/abs/2310.06763.

Author response:
Dear Reviewer uuBj,

Thank you sincerely for your valuable feedback on our paper. 

We plan to conduct analysis on the alignment loss function forms in our future research and expand our benchmark comparisons beyond the current scope. Regarding computational efficiency, MD simulations are indeed computationally expensive, and we are exploring faster alternatives that can still provide reliable results. 

We are committed to addressing these concerns in our future endeavors. 

Thank you once again for your time and efforts in reviewing our work.

Reviewer comment (Experiments):
Computational efficiency comparison to alternatives would be helpful. It is necessary to give computational comparison since MD is usually cost, while diffusion with many steps are also cost.

Author response:
Dear Reviewer uuBj,

Thank you sincerely for your valuable feedback on our paper. 

We plan to conduct analysis on the alignment loss function forms in our future research and expand our benchmark comparisons beyond the current scope. Regarding computational efficiency, MD simulations are indeed computationally expensive, and we are exploring faster alternatives that can still provide reliable results. 

We are committed to addressing these concerns in our future endeavors. 

Thank you once again for your time and efforts in reviewing our work.