**Question 1**: In Section 3.2.1, the authors state, “Since the atom set V can be directly inferred from the chemical formula.” However, it is unclear where the chemical formula is derived from. Could the authors clarify this, as in real-world scenarios, the input typically does not contain the chemical formula?
**Response**: Chemical formulas are derived based on the MS1 peak, which specifies the molecular weight of the ionized molecule that becomes fragmented and measured as a spectrum. From the weight of the ionized molecule, one could generate candidate formulas. Additional information such as the spectrum can be used to refine the list of candidate formulas.
For example, in the recent MIST-CF[1], a dynamic programming algorithm generates exhaustive chemical formula candidates for the MS1 peak within a small mass tolerance, often filtering implausible options using chemical rules like ring double bond equivalents (RDBE). Subsequently, peaks within the spectrum are annotated with subformulas using a Formula Transformer, a neural network, based on the candidate formulas. MIST-CF scores each candidate formula based on its alignment with the observed fragmentation spectrum, outputting a ranked list of likely formulas. SIRIUS[2] is another de noo tool that assigns a chemical formula to a spectrum. Like, MIST-CF, SIRIUS generates candidate formulas, and assigns potential subformulas to peaks. These subformula annotations are then organized into a fragmentation tree using maximum a posteriori (MAP) optimization. Finally, SIRIUS calculates the likelihood of each chemical formula based on the constructed tree. Another technique, BUDDY[3], each molecular weight associated with each peak within the spectrum is searched against a curated molecular formula database. Similarly, the neutral loss (what was lost during fragmentation and not measured) is also searched in the formula database. BUDDY prioritizes explainable candidate formulas and filters implausible formulas. Currently, SIRIUS and BUDDY are the two most common tools used by practitioners to annotate their spectra.
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**Question 2**: For the MassSpecGym dataset, what is the “best published state-of-the-art performance” referred to? Are there baseline performance metrics reported for the other two datasets as well? **& Weakness 4**: The discussion of baselines is unclear.
**Response**: The manuscript describing the MassSpecGym dataset was just accepted at NeurIPs 2024 [4]. For de novo generation with a known chemical formula, performance is reported for “random chemical generation”, “SMILES transformer”, and a “SELFIES Transformer”. The accuracy was zero for all three techniques. We also report on running additional tools, namely Spec2Mol[5] and MSNovelist[6], as summarized in Table 2.
Performance metrics include Top-1 accuracy, similarity, and MCES (Maximum Common Edge Subgraph). The results indicate that Top-1 accuracy was zero across all datasets, but similarity and MCES scores varied. For example, Spec2Mol achieved higher similarity and MCES values on MassSpecGym compared to NIST23 and Canopus, with similarity scores of 0.19 (MassSpecGym), 0.16 (NIST23), and 0.18 (Canopus). Corresponding MCES values were 45.89 (MassSpecGym), 20.88 (NIST23), and 38.97 (Canopus).
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**Weakness 3**: The scaffold retrieval performance, especially when using a predictive retriever, remains relatively low (e.g., NIST23).
**Response**: We have double-checked the implementation for NIST23 and noticed there is a numerical issue when computing the cosine similarity score. We fix the bug and re-do the experiment for NIST23. Correctness of other datasets are also double-checked, below is the updated result for NIST23:
|Retriever|SPA↑|Top1 Accuracy↑|Top1 Similarity↑|Top1 MCES↓|Top10 Accuracy↑|Top10 Similarity↑|Top10 MCES↓ |
|-|-|-|--|-|-|-|-|
|MADGEN (Predictive) (Before)| 8.7%|1.8%| 0.06| 84.24 |2.2% |0.07|82.33|
|MADGEN (Predictive) (Now)| **57.8%** |**10.3%**| **0.18**| **68.13** |**14.5%**|**0.24**|**62.65**|
We hope the updated result address your concern.
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**Reference**
#### [1] Goldman, Samuel, et al. "MIST-CF: Chemical formula inference from tandem mass spectra." Journal of Chemical Information and Modeling 64.7 (2023): 2421-2431.
#### [2] Dührkop, Kai, et al. "SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information." Nature methods 16.4 (2019): 299-302.
#### [3] Xing, Shipei, et al. "BUDDY: molecular formula discovery via bottom-up MS/MS interrogation." Nature Methods 20.6 (2023): 881-890.
#### [4] Bushuiev, Roman, et al. "MassSpecGym: A benchmark for the discovery and identification of molecules." arXiv preprint arXiv:2410.23326 (2024).
#### [5] Litsa, Eleni, et al. "Spec2Mol: An end-to-end deep learning framework for translating MS/MS Spectra to de-novo molecules." (2021).
#### [6] Stravs, Michael A., et al. "MSNovelist: de novo structure generation from mass spectra." Nature Methods 19.7 (2022): 865-870.