Venue: NeurIPS 2021 Conference

Reviews:

final_score: rating: 7: Good paper, accept
review_content: summary: This paper introduces GeoMol, an end-to-end learning method for molecular conformation generation.  It is based on the construction and alignment of elements of local atomic structure, is efficient in its parameterization of geometric degrees of freedom, and is SE(3) invariant.  It achieves competitive performance on the GEOM-DRUGS and GEOM-QM9 tasks.
main_review: Originality: To the best of my knowledge, the method is novel, and represents a fast and clean way of generating molecular conformations. In particular, the generation of elements of local structure and their subsequent assembly in an end-to-end manner allows for much more computationally efficient performance than previous methods of comparable performance.

Quality: The submission is technically sounds and the claims are backed up through experimental results.  The evaluation metrics are standard and well justified.

Clarity: The paper is very well written and self-contained.

Significance: I believe the results to be significant, especially as they allow for the rapid generation of many conformers with good accuracy.  One concern I have is whether this method will maintain accuracy for much larger molecules such as proteins, as these often are driven by long-distance interactions, and any local errors can compound.  

*** Post-response update ***

Thank you for the additional information regarding your attempts to more explicitly model in long-distance interactions.  Seems like exciting future work!
limitations_and_societal_impact: The authors convincingly address the limitations of the work. 

There are no negative societal impacts to be addressed here.
review_title: null

final_score: rating: 7: Good paper, accept
review_content: summary: The authors propose the generative model that produces 3d conformations from a molecular graph based on predictions of local structures. First, for each anchor atom, the model predicts relative 1-hop positions of neighboring atoms, then predicts torsion angles. Finally, assemble the molecule in the deterministic way given predicted components. The authors comprehensively evaluate the model on the recently proposed dataset GEOM and compare it with state-of-the-art neural-based conformer generators and special software.

The paper's main contributions are: the authors propose a principal novel scheme for conformation generation that avoids expensive molecular geometry optimization used in current state-of-the-art neural conformer generators, still being translation, rotation, and reflection equivariant. The proposed model outperforms other baselines in both speeds of conformation generation and diversity of generated objects.

main_review: **Originality:** The idea proposed in the paper is quite novel and differs from the current state-of-the-art neural methods for conformation generation both in used conformation representation and algorithm of generation: model operates with bond lengths, dihedral, and torsion angles instead of distance matrices and avoids time-consuming iterative process to optimize the final coordinates of conformation.

**Quality:** The authors carefully describe their motivation, goals and provide an overview of existing methods. However, the paper lacks a broad discussion of potential applications of the proposed model in the drug-discovery area.

**Clarity:** The paper is easy to follow. The equations and essential details of the generative process are clearly stated. Still, the paper reading experience suffers from the abundance of inline equations and the absence of equations references. Also, some subsections (i.e., 'Final LS assembly for a single bond' and 'How to compute γ?') are quite technical and can be moved fully or partially to the Appendix.

**Significance:** The experimental results show the gap in the coverage and diversity metrics between the proposed model and alternatives. Still, the metrics do not provide standard deviation for reported metrics, so it's hard to determine how significant this gap is.


limitations_and_societal_impact: The main limitation of the paper is the absence of generated conformations quality evaluations - how physically plausible the generated conformations in contrast with ground-truth conformations and sampled conformations from other baselines. The model could show the high diversity and coverage metrics, still producing conformations with physically implausible fragment structures. This evaluation can be easily done by computing the potential energies of conformations.

**Update**: The authors clarified this point in their rebuttal and provided an additional experiment demonstrating potential energies of generated structures.
review_title: null

final_score: rating: 7: Good paper, accept
review_content: summary: This paper studies how to generate low-energy 3D molecular conformations based on 2D molecular graphs.  
To this end, they propose the GEOMOL, an end-to-end trainable model that models molecular conformations in a SE(3)-invariant fashion, which first predicts the 3D local structures and then assembles them into final conformations with the predicted torsional angles. 


main_review: The GEOMOL takes torsional angles as well as stereochemistry (Tetrahedral chiral) into consideration and bypasses the 2-stage distance geometry technique used in previous works.  

GEOMOL consists of several sub-modules, e.g., local Structure (1-hop) Prediction, Tetrahedral chiral corrections, Local Structure (LS) Assembly.  
I find these sub-modules are overall novel at least in the ML community. The local structure assembly algorithm is the most interesting idea of the work in my view, which seems quite general and can be applied to other 3D modeling tasks.   

The paper is generally well-written and the codes are provided.
Experimental results on GEOM datasets show that the performance of GEOMOL is quite promising.

**Questions**

 I wonder how well can each designed submodule perform on each task, especially on torsional angles prediction? (local structure prediction task and torsional angle prediction task). 

**Update**
The authors address my concerns and looking forward to see the updated version of this exciting work.

limitations_and_societal_impact: The main limitations of the work are two folds: 

* (1) It only models the local structures (covalent bonds and their angles) and overlooks the long-range non-bonded angles, which may be crucial in multi-molecular systems or large molecules.  

     Eq.4 (Line 273-274) indicates that GEOMOL predicts the torsional angle for each pair of LS independently. The benefit is that all LS can be assembled in parallel. However, it fails to take other structures into consideration (h_mol is used but it is not good enough).

* (2) The model relies on the torsional angles to assemble local structures, which means it fails to handle molecular systems with disconnected molecular graphs (e.g., molecular complexes). 
One potential way is to combine GEOMOL with the previous distance-based models, i.e., first use GEOMOL to compute the structure of each connected component, and then use distance-based method (CGCF[1] and GraphDG[2]) or gradient-based (ConfGF[3], a related work on conformation generation, maybe it's better to cite it in paper) to assembly disconnected part.

**reference**
1. Xu, M., Luo, S., Bengio, Y., Peng, J., and Tang, J. Learning neural generative dynamics for molecular conformation generation.
2. Simm, G. and Hernandez-Lobato, J. M. A generative model for molecular distance geometry
3. Shi, Chence and Luo, Shitong and Xu, Minkai and Tang, Jian. Learning Gradient Fields for Molecular Conformation Generation
review_title: null

Meta-review: This paper introduces an end-to-end learning method called GeoMol for molecular conformation generation.  The algorithm is novel and provides  a fast and clean way of generating molecular conformations. It achieves competitive performance on the GEOM-DRUGS and GEOM-QM9 tasks. All the reviewers are excited about the work. 

Decision: Accept (Spotlight)