reviews: 
Comments: The present manuscript proposes a novel graph construction method, SCHull for molecular modeling thorugh GNNs. The authors start by introducing limitations of the currently existing graph construction  methods (e.g. radial cuttoff, and knn graphs), that cannot deal with sparsity, connectivity, and rigidity at the same time.

The method constructs graphs in two steps, first by projecting points onto the unit sphere. and secondly by constructing a convex hull for the projected points, and adding edges to the graph, based on the convex hull. According to the authors' motivation, the resulting graphs are sparse, connected, and rigid, making them suitable for learning molecular representations using GNNs.

The authors show the impact of SCHull, by integrating it into current molecular Gnns, and test it in various benchmarks, including MD17, as well as protein modeling tasks (fold, enzyme reaction classification, Protein-ligand binding).
Strengths: 1. I particularly find Theorem 3.6 interesting, and useful for the distinction of point clouds through their corresponding SCHull graphs. In general, the authors provide a well-defined set of propositions for defining connectivity, rigidity, and sparsity for the SChull graphs, making it useful for predicting their impact on GNN's performance and scalability.

2. The motivation for a hyper-parameter free approach for graph construction is very intriguing, and can be proven very useful for molecular modeling tasks, where the distance thresholds can play an important role. 

3. The empirical results show that in both small- and large-molecular modeling tasks, SChull has a positive impact in a couple of GNNs. 

4. The paper is well-written, the formulations are clear, and the experimentation setup is nicely described.

5. It's very interesting that Tables 4, and 5 show a comparable increase of the runtime cost, with respect to the methods without the SChull. I would expect a much longer runtime discrepancy, due to the point projection, and the CH computation.

5. The code is available, seemingly containing all variants of models and benchmarks appeared in the manuscript. I cannot assess its validity, as I did not run it.
Weaknesses: 1.  The paper primarily compares SCHull to radial cutoff and k-nearest neighbor graphs, as well as chemical graphs. It lacks  some compariso with more recent or complex molecular graph construction methods, such as power graphs [1] (which are by the way already mentioned by the authors but not benchmarked) or methods focusing on higher-order graphs, such as simplicial complexes [2] ( that define volumes of data points), or combinatorial complexes [3] (that particularly make use of convex hulls and their corresponding volumes in point clouds).

2. An approach on learning new geometric interactions might be useful in better interpreting the correlation of geometry and function, which could be particularly a motivation for this paper. The paper should focus on exploring or at least mention how such a convex hull approach, despite a comparable computational cost can help on finding interpretable structures.

3. Since the method is focusing on the three properties of sparsity, connectivity, and rigidity, it'd be great if the authors can show empirically the impact of SChull on each of these properties independently. 

[1] On the Expressive Power of Sparse Geometric MPNNs. Sverdlov et al 2024.
[2] E(n) Equivariant Message Passing Simplicial Networks. Eijkelboom et al 2023.
[3] E(n) Equivariant Topological Neural Networks. Battiloro et al 2024.
Recommendation: 8
Reviewer confidence: 3
Soundness/correctness: 3
Clarity: 3