Paper: GlycoNMR: A Carbohydrate-Specific NMR Chemical Shift Dataset for Machine Learning Research (ICLR-2024)

Reviewer comment (Experiments):
The paper provides statistics on the dataset but omits details for certain features like ring size, ring position, and atom type. Adding those statistics would add to the clarity of the features extracted.

Author response:
We would like to thank reviewer sFB8’s suggestions, as the visualization could further illustrate the data statistics in Table 1. We provide a detailed feature analysis on both GlycoNMR.Exp and GlycoNMR.Sim in our **Appendix A.3 and A.4**. We kindly invite reviewer sfB8 to check our expanded version on page 17. In **Appendix A.3**, we visualize the feature statistics in six pie charts for atom-level and monosaccharide-level features. 

For the atom level feature (first row), we included the dataset statistics of atom type, carbon atom position, and hydrogen atom position. According to the pie chart for atomic identity(top left), we observed that of all the 27267 atoms in GlycoNMR.Exp, 13010 are Hydrogen, 7663 are Carbon, 6257 are Oxygen, and 337 are other types of atoms, including Nitrogen, Phosphorus, and Sulfur. 

To describe carbon atom position (top middle), ‘Other’ indicates the off-ring carbons. Similarly, for describing the hydrogen atom position (top right), ‘Other’ indicates off-ring hydrogens. 

For the monosaccharide level feature, we included Amomer (bottom left, indicates the hydroxyl group), Configure (bottom middle, indicates Fischer project information), and Ring Size (bottom right, number of in-ring carbons). The ‘N/A’ of each figure indicates that the information is not contained in the PDB file.

Reviewer comment (Experiments):
A deeper validation of the introduced features would enhance the paper's value and credibility.

Author response:
In addition, we added **Appendix C** to analyze the feature contributions by calculating the Shapely values. Here we again present the Shapley value table: 

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| GlycoNMR.Exp | Ring position | Modification | Stem type | Anomer | Configuration | Ring size |
|--------------|---------------|--------------|-----------|--------|---------------|-----------|
| Hydrogen     | 0.457         | N/A          | 0.088     | 0.061  | 0.009         | 0.008     |
| Carbon       | 16.852        | N/A          | 2.640     | 0.515  | 0.257         | 0.085     |

| GlycoNMR.Sim | Ring position | Modification | Stem type | Anomer | Configuration | Ring size |
|--------------|---------------|--------------|-----------|--------|---------------|-----------|
| Hydrogen     | 0.387         | 0.014        | 0.112     | 0.051  | 0.014         | 0.003     |
| Carbon       | 13.007        | 0.321        | 3.619     | 0.465  | 0.199         | 0.055     |

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We observe that the Shapley values across all features are positive. Notably, the ring position of carbon and hydrogen atoms is a critical feature in predicting NMR shifts. Moreover, integrating the stem type of monosaccharides into the 2D Graph Neural Network can marginally reduce the prediction error. Other features, like the modification group, anomer, configuration, and ring size, exert lesser influence.

Reviewer comment (Experiments):
There seems to be missing statistics on some specific features. Could you share more detailed stats about the ring size, ring position, and atom type to give a fuller picture of the datasets?

Author response:
We would like to thank reviewer sFB8’s suggestions, as the visualization could further illustrate the data statistics in Table 1. We provide a detailed feature analysis on both GlycoNMR.Exp and GlycoNMR.Sim in our **Appendix A.3 and A.4**. We kindly invite reviewer sfB8 to check our expanded version on page 17. In **Appendix A.3**, we visualize the feature statistics in six pie charts for atom-level and monosaccharide-level features. 

For the atom level feature (first row), we included the dataset statistics of atom type, carbon atom position, and hydrogen atom position. According to the pie chart for atomic identity(top left), we observed that of all the 27267 atoms in GlycoNMR.Exp, 13010 are Hydrogen, 7663 are Carbon, 6257 are Oxygen, and 337 are other types of atoms, including Nitrogen, Phosphorus, and Sulfur. 

To describe carbon atom position (top middle), ‘Other’ indicates the off-ring carbons. Similarly, for describing the hydrogen atom position (top right), ‘Other’ indicates off-ring hydrogens. 

For the monosaccharide level feature, we included Amomer (bottom left, indicates the hydroxyl group), Configure (bottom middle, indicates Fischer project information), and Ring Size (bottom right, number of in-ring carbons). The ‘N/A’ of each figure indicates that the information is not contained in the PDB file.

Reviewer comment (Writing):
Could you provide further elaboration on the relationship between the ring position and the shift, specifically how the ring position influences the chemical shift value?

Author response:
We provide the violin plot of the Ring position versus the NMR shift for both datasets in **Appendix A.4.** In each subplot, the x-axis indicates the ring position of the atom (Carbon / Hydrogen), and the y-axis indicates the NMR shift of the corresponding atoms. 

We notice that the distribution of NMR shift values for the ring positions C1 and C6 significantly vary from those of C2, C3, C4, and C5, similar to hydrogens. In addition, the distribution of NMR shift values associated with the ring positions shows similarity when comparing the GlycoNMR.Exp and the GlycoNMR.Sim. This consistency indicates the value of using simulated NMR data to help develop ML approaches for studying carbohydrates. 

For a general explanation of the ring position, a fundamental factor that determines the NMR shift value is the atom’s electronic environment, especially bonded or non-bonded interactions within 1-3 atom distances away from the atom of interest.

In glycoscience specifically, it is known that in the monosaccharide units, the index of carbon and hydrogen in the ring, is one of several major factors that define the electronic environment, which is reflected in our results [1]. These basic position tendencies are further modulated by a set of factors that can also alter the electrochemical environment, including the other features tested in our model, like anomer, configuration, temperature, and solvent, and more complicated modification or conjugation groups attached to glycans. This domain knowledge prompts us to integrate ring positions as one of the node features in our 2D-based and 3D-based Graph Neural Network models.

Reviewer comment (Evaluation):
Are there any trends or patterns observed based on different ring positions that affect the shift?

Author response:
We provide the violin plot of the Ring position versus the NMR shift for both datasets in **Appendix A.4.** In each subplot, the x-axis indicates the ring position of the atom (Carbon / Hydrogen), and the y-axis indicates the NMR shift of the corresponding atoms. 

We notice that the distribution of NMR shift values for the ring positions C1 and C6 significantly vary from those of C2, C3, C4, and C5, similar to hydrogens. In addition, the distribution of NMR shift values associated with the ring positions shows similarity when comparing the GlycoNMR.Exp and the GlycoNMR.Sim. This consistency indicates the value of using simulated NMR data to help develop ML approaches for studying carbohydrates.

Reviewer comment (Experiments):
Including some analysis, possibly with graphs or charts that showcase the relationship between ring position and shift, could enhance understanding of the dataset.

Author response:
We provide the violin plot of the Ring position versus the NMR shift for both datasets in **Appendix A.4.** In each subplot, the x-axis indicates the ring position of the atom (Carbon / Hydrogen), and the y-axis indicates the NMR shift of the corresponding atoms. 

We notice that the distribution of NMR shift values for the ring positions C1 and C6 significantly vary from those of C2, C3, C4, and C5, similar to hydrogens. In addition, the distribution of NMR shift values associated with the ring positions shows similarity when comparing the GlycoNMR.Exp and the GlycoNMR.Sim. This consistency indicates the value of using simulated NMR data to help develop ML approaches for studying carbohydrates.