Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 8: Strong Accept: Technically strong paper, with novel ideas, excellent impact on at least one area, or high-to-excellent impact on multiple areas, with excellent evaluation, resources, and reproducibility, and no unaddressed ethical considerations.
review_content: summary: This paper proposed a novel diffusion model for molecular conformer generation. The proposed diffusion model is operated in the space of torsion angles of the conformer. An extrinsic-to-intrinsic score model is learned for the diffusion process to predict the torsional scores directly from the 3D point cloud representation of the conformer. The exact likelihoods of the generated conformers can be computed to enable energy-based training with samples from the Boltzmann distribution. The proposed diffusion model outperforms existing machine learning and cheminformatic-based solutions on the GEOM benchmark. 
strengths_and_weaknesses: This paper proposed a novel diffusion model that has a strong empirical impact on the fundamental task of molecular conformer generation. With comprehensive experiments, the authors showed that the model is able to generate more accurate conformers in less time compared with existing machine learning solutions. The model also consistently outperforms a commercial software OMEGA on the GEOM benchmark. The evaluations were conducted with high quality, with sufficient details, strong quantitative evidence, and adequate ablation analysis.

Theoretically, the intuitions behind the important design decisions are well explained, i.e., learning a diffusion model in the torsional space instead of euclidean space, using a SE(3)-invariant model to parameterize the score function, etc. The theory behind the diffusion model and training algorithm is also sound. 
questions: I am very excited to see that the proposed method outperforms commercial software for molecular conformation prediction. I am interested in the potential of such a model in the future. What are the main practical limitations (e.g., sample efficiency, training time, inference time, ...) for deploying and using such a model in an industrial scenario? Can it be considered for high-throughput applications? What is the main challenge of adapting the model to larger molecules (e.g., proteins) use cases?
limitations: The authors have adequately addressed the limitations in the appendix.
review_title: null

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: This paper propose torsional diffusion for molecular conformer generation. Different from the previous conformer generation work which mainly predict the position of each atoms in the 3D space, this work view the conformer generation in a different view by generating the torsion. To achieve this, the paper also provides the exact likelihood of the generated conformers, which lead a better training pipeline comparing with the previous work. From the experiment side, the paper reaches a very good SOTA result comparing with GEOM-DRUGS dataset, which is very convincing. 

In addition, the paper propose torsional Boltzmann generator that can be expand and adapt in generating various class of molecules.
strengths_and_weaknesses: The paper is in a good writing, very easy to follow and the idea is natural. Rather than learning the Euclidian information in the previous task, learning the torsion to generate conformer is more reasonable in a physical natural. Although learning torsion was proposed in GeoMol using MPNN, enhancing it use Diffusion model is still soundable. The experiment result is solid with many details. 
For the weakness, I know from the author's statement, they retrain the GeoDiff to adapt the larger dataset, but it still seems the GEOM-DRUGS and GEOM-QM9's performance for the baseline GeoDiff and GeoMol is much lower than their report number. Any suggestion towards this huge gap? Is it possible to use their split to have another comparison?

questions: For GeoMol and GeoDiff's performance, it seems have a huge gap between the original paper. I am not sure if it is reasonable since the author uses a different data split. It may be fair to use their split for another comparison.
limitations: Same as the Questions Section.
review_title: null

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: This paper studies molecular conformation generation. Different from previous works that focus on coordinate prediction or distance prediction, this paper proposes to focus solely on torsional angles of rotatable bonds in molecules, leaving all other degrees of freedom fixed. 

Inspired by diffusion models on Riemannian manifolds, the authors first lay the theoretical framework of torsional diffusion on hypertorus following the standard denoising diffusion models. Based on this framework, they show that such a diffusion process can also be defined on Cartesian coordinates, and design a SE(3)-invariant and parity equivariant score network operating on 3D point clouds.

They show the proposed method achieves state-of-the-art performance on GEOM-DRUGS, with much fewer denoising steps compared to previous models. The resulting method is also capable of exact likelihoods estimation, making it suitable for matching the Boltzmann distribution over torsion angles using the energy function.
strengths_and_weaknesses: ### Strengths
Torsional angles are the most important degrees of freedom that determine the conformations of a molecule. Focusing on torsional angle modeling for conformation prediction is a straightforward and novel idea. To the best of my knowledge, it is the first work that marries denoising diffusion models with torsional angle prediction, and I like this idea.

The resulting score functions for torsional angle diffusion is SE(3)-invariant and parity equivariant for every rotatable bond in a molecule, which is a nice property.

The proposed model is capable of exact likelihood estimation, which is another nice property thanks to the use of SDE.

### Weaknesses
The model relies on an external algorithm, such as RDKit ETKDG, to generate the initial local structures for the target molecule, and the only degrees of freedom are torsional angles. This means the chirality of the conformation is entirely determined by the initial structure, or equivalently, by the external algorithm that provides the initial guess structure.
questions: In line 111, the authors give the definition of the freely rotatable bond, which are those that serve as a bridge between two components. Is this definition the same as the definition provided by RDKit API? (As I remember we can get all rotatable bonds of a molecule by calling a certain function provided by RDKit).

As far as I understand, torsion angles in cycles are also the degrees of freedom that determine the conformation of a molecule. How are these rotatable bonds handled in the proposed method? Can we also do diffusion on these bonds?
limitations: The authors didn't discuss limitations and potential negative societal impact of their work.
review_title: null

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: This paper argues that instead of generating bond length, angle and torsion simultaneously, one should focus on the more difficult part, i.e. the torsion. And the authors further propose a diffusion model working on this Riemannian manifold of the torsion angles. A Bolzman generator is then introduced based on the likelihood calculated from the diffusion model. Experiments on molecules with normal size, large size and small size datasets show increased performance in terms of RMSD and convergence time.

strengths_and_weaknesses: Pros:
-The paper is well motivated to work on the torsion angles only, which reasonably leads to advantages in speed and performance, under mild assumptions about the bond length and angle. The idea is novel. 
-The idea of using the diffusion model on the torsion-composed manifold is compatible with the Bolzman generator and leads to some good properties. 
-The paper is well written. The idea, the procedure, why symmetry properties are satisfied, how to solve the sample-wise intrinsic coordinates, are well elaborated. 

Cons: 
-Ablation studies, e.g. about the depth of diffusion, conformed matching and any other hyperparameters would help to understand and are desired. 
questions: 
Please see the above weaknesses.
limitations: The authors have adequately discussed the limitations of torsional diffusion, which I think is quite fair.
review_title: null

Meta-review: metareview: This paper proposes a diffusion model for molecular conformation prediction in the space of torsion angles. The idea is new, and the experimental results are strong. All reviewers like this paper. 
confidence: Certain
Decision: Accept