MS-BART: Unified Modeling of Mass Spectra and Molecules for Structure Elucidation
Authors: Yang Han
Pengyu Wang
Kai Yu
xin chen
Lu Chen
Venue: NeurIPS 2025 poster ()
Keywords: AI4Science
Language Model
Metabolomics
MS/MS spectra
Primary area: machine_learning_for_sciences

Abstract: Mass spectrometry (MS) plays a critical role in molecular identification, significantly advancing scientific discovery. However, structure elucidation from MS data remains challenging due to the scarcity of annotated spectra. While large-scale pretraining has proven effective in addressing data scarcity in other domains, applying this paradigm to mass spectrometry is hindered by the complexity and heterogeneity of raw spectral signals.
To address this, we propose MS-BART, a unified modeling framework that maps mass spectra and molecular structures into a shared token vocabulary, enabling cross-modal learning through large-scale pretraining on reliably computed fingerprint–molecule datasets. Multi-task pretraining objectives further enhance MS-BART's generalization by jointly optimizing denoising and translation task. The pretrained model is subsequently transferred to experimental spectra through finetuning on fingerprint predictions generated with MIST, a pre-trained spectral inference model, thereby enhancing robustness to real-world spectral variability. While finetuning alleviates the distributional difference, MS-BART still suffers molecular hallucination and requires further alignment. We therefore introduce a chemical feedback mechanism that guides the model toward generating molecules closer to the reference structure.
Extensive evaluations demonstrate that MS-BART achieves SOTA performance across 5/12 key metrics on MassSpecGym and NPLIB1 and is faster by one order of magnitude than competing diffusion-based methods, while comprehensive ablation studies systematically validate the model's effectiveness and robustness.
We provide the data and code at [https://github.com/OpenDFM/MS-BART](https://github.com/OpenDFM/MS-BART).

MS-BART: Unified Modeling of Mass Spectra and Molecules for Structure Elucidation
Yang Han 12, Pengyu Wang! Kai Yu Xin Chen"", Lu Chen 12.344 X--LACE Lab, School of Computer Science
MoE Key Lab of Artificial Intelligence, SJTU AI Institute Shanghai Jiao Tong University, Shanghai, China
Suzhou Laboratory, Suzhou, China 3 SShngghai Innovation Institute, Shanghai, China
*Jiangsu Key Lab of Language Computing, Suzhou, China [csanghan,, chenluse usz. @sjtu jtu. edu cn, ma 1 x ncher @gma 1, com
Abstract
Mass spectrometry (MS) plays critical role in molecular identification, signif- icantly advancing scientific discovery. However, structure elucidation from MS data remains challenging due to the scarcity of annotated spectra. While large- scale pretraining has proven effective addressing data scarcity in other domains, applying this paradigm to mass spectrometry is hindered by the complexity and heterogeneity of raw spectral signals. To address this, we propose MS-BART, unified modeling framework that maps mass spectra and molecular structures into shared token vocabulary, enabling cross-modal learning through large-scale pretraining on reliably computed fingerprint-moleaul datasets. Multi-task pretrain- ing objectives further enhance ASS-BART' generalization by jointly optimizing denoising and translation task. The pretrained model is subsequently transferred to experimental spectra through finetuning on fingerprint predictions generated with MIST, pre-trained spectral inference model, thereby enhancing robustness to real-world spectral variability. While finetuning alleviates the distributional difference. MS-BART still suffers molecular hallucination and requires further alignment. We therefore introduce a chemical feedback mechanism that guides the model toward generating molecules closer to the reference structure. Extensive evaluations demonstrate that MS-B -ART achieves SOTA performance across 5/12 key metrics on MassSpee and NPLIB and is faster by one order magnitude than competing diffusion-basse methods, while comprehensive ablation studies systematically validate the model's 's effectiveness and robustness. We provide the data and code at https//ggiiabbbcmmmooppttt//////
Introduction
Mass spectrometry is analytical technique that measures the mass-to-charge ratio of ions, enabling the identification, quantification, and structural characterization of molecules. The identification of small molecules from mass spectrometry data represents fundamental task in analytical chemistry,
with broad applications across multiple domains, including drug discovery [ 32 39], environmental
biochemistry (35), and materials science (2I]. Recent advances machine learning have enabled structure elucidation from mass spectra. Existing approaches can be broadly categorized into (I) retrieval-based methods and (2) de nove generative methods. Retrieval-based methods rely on matching query spectra against large annotated spectra databases. However, annotated experimental
Xin Chen and Lu Chen are the corresponding authors.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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230 woveer
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255 15510000
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Figure 1: Randomly selected mass spectra of molecule (SMILES: C#CCNCCI-CC-CC=CI
InChIKey: LDYBFSGEBHSTOQ) from MassSpecGym 77 acquired under varying collision energies.
The z-axis shows the mass-to-charge ratio (m/ 2), the y-axis indicates collision energy (in eV), and color represents normalized relative intensity.
spectra are scarce and costly to obtain [3] In addition, these methods are inherently limited to known molecules and cannot identify novel structures absent from reference databases. De novo generation methods learn to generate molecular structures directly from mass spectra. offering the potential to discover new compounds. However, these generative models (39] by the limited availability of high-quality experimental spectra.
29 are also bottlenecked
In other domains such as natural language processing (NLP) and computer vision (CV), common strategy to overcome data scarcity is to pretrain models on large-scale unlabeled data and then finetune them on task-specific datasets S 28
similar paradigm can also observed in nuclear magnetic resonance (NMR) structure elucidation 48, where the model is first pretrained on 3.6 million unlabeled molecules and then fine-tuned directly on simulated and experimental NMR data. However, adapting this paradigm mass spectrometry remains challenging due to the intrinsic complexity and heterogeneity of mass spectra. As illustrated in Fig. D spectra for the same molecule vary substantially under different collision energies, adduct types, or instrument settings, and may even fluctuate slightly under identical experimental conditions. To address this variability, we propose using molecular fingerprints are binary vectors that encode the presence chemical substructures. Unlike raw spectra, they are invariant experimental conditions and can be reliably computed from molecular structures using cheminformatics toolkits such as RDKit. This eliminates the need to simulate mass spectra under diverse experimental settings 34 17). enabling scalable pretraining dataset construction.
6. 20 19].
intermediate representation of mass spectra. Fingerprints
Building on this insight, we propose ASSBBART, a unified framework for molecular structure elucida- tion from mass spectrometry data, following the pretraining win.iiiinininiinnn--nnn alignment used NLP. We first construct a large-scale pretraining dataset consisting of fingerprint-moles pairs, where molecular fingerprints Based on this dataset, we design multi-tast pretraining objectives to facilitate cross-moda learning between molecular fingerprints and molecular structures. For finetuning, incorporate experimental mass spectrometry data by using MIST [18] to predict fingerprints from spectra, conditioned on asso- ciated metadata and molecular formulas. As these predicted fingerprints subject to dataset-specific noise and systematic biases, we finetune the pretrained model using the predicted fingerprints as input, thereby improving its adaptability real-world experimental conditions. Finally, the generated structures are prone to molecular hallucinations [15), where outputs are chemically valid but deviate from the true molecules. To address this, we introduce alignment step that incorporates chemical feedback by assigning higher probabilities to structures more similar to the ground truth. In summary, our main contributions are follows:
-iiinnnnnnnnnnlllnnnnnnt paradigm widely
computed for million unlabeled molecules using RDKit.
To the best of our knowledge, we are the first to leverage language model for mass spectra structure clucidation by introducing unified vocabulary and multi-task pretraining large corpus of fingerprint-mon mollecll pairs.
We finetune the model on experimental data and incorporate chemical feedback to align the generative distribution with real-world structural preferences.

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We validate our approach on two public benchmarks, achieving SOTA performance across 5/12 key metrics on MassSpecGym[[7] and NPLIB [T]] and is faster by one order of magnitude than competing diffusion-bann baase methods.
2
Related Work
Mass Spectra Modeling. Mass spectra are variable-length, discrete, two-dimensional data, which makes them inherently challenging model. A basic approach involves padding the two-dimensional matrix to a fixed length and projecting it into an embedding space via a linear layer [45]. A more common strategy is spectral binning, which partitions spectra into fixed-width intervals (e.g.. 0.1 Da) to yield fixed-size input vectors [38] For example, ChemEmbed [14] limits molecular weights to 700 Da, encodes spectra into 7000-dimensional vector using bin size of 0.01, and applies a convolutional neural network (CNN) to predict 300-dimensional Mol2vec embeddings, Similarly, Spec2Mol [29 and Sorre 23 convert spectra into bit vectors and train ID CNNs or Siamese networks [5] to learn meaningful spectral embeddings. While binning is simple, it suffers from sparsity and sensitivity to noise, limiting its ability to capture chemically meaningful patterns. To overcome this, some methods leverage molecular fingerprints intermediate representations that encode chemical substructures more robustly. CSI:FingerID [T]] predicts molecular fingerprints from tandem mass spectra using fragmentation trees and machine learning, achieving strong performance in metabolite identification. MSNovelist [4]] builds on this by integrating predicted fingerprints into -ncoderd model for de novo structure generation. Inspired by these approaches, MSTBART adopts molecular fingerprints as a spectrum representation, enabling scalable pretraining while preserving chemical semantics.
Structure Elucidation from Mass Spectra. Two major paradigms dominate structure elucidation from mass spectra: library matching and de novo generation. Library matching formulates the task as an information retrieval problem [40), comparing query spectra against databases of experimental or simulated spectra. Methods such as Veec2Vec [22] and MSBERT [49] learn spectrum embeddings
and perform retrieval over databases like GNPS [4]. Due to the scarcity experimental spectra, (4]) simulate spectra from known molecular databases (c.g.. PubChem). However, the effectiveness of library matching is constrained by database coverage, spectrum quality, and experimental variation, limiting its utility for novel compounds. In contrast, de novo approaches generate molecular structures directly from spectra, bypassing the need for reference databases. Spec2Mol (29] draws inspiration from speech-to-text models, employing an encoder-decodee network to translate spectra into SMILES sequences. MADGEN (45] uses a
some methods (c.g., CFM-ID (3), GRAFF- MS
two-stage framework: scaffold retrieval and scaffold-conditionee molecule generation. Spectra and scaffolds are embedded into a shared latent space using MLPs and GNNs, and RetroBridge [24 generates the final structure conditioned on both inputs. Other models, including MSNovelist 41
and MS2SMILES [30]. use fingerprints predicted by SIRIUS [ as inputs to sequence models for SMILES generation. MS2SMILES further improves atom-level resolution by jointly predicting heavy atoms and their associated hydrogens. DifIMS [4] adopts an implicit fingerprint representation by extracting the final embedding from the precursor peak in MIST [T]] and then generates the target structure by discrete diffusion conditioned on the spectrum embedding and node features (derived from the given formula). Despite promising results, most existing methods treat spectra and molecular structures as separate modalities, which often leads to semantic mismatches and molecular hallucinations [5]. In contrast, MS-BART unifies their modeling through a shared vocabulary. By pretraining on large-scale spectral minllee pairs, MS-BART learns rich representations for both chemical structures and their spectral abstractions. Subsequent finetuning and alignment on experimental spectra further enhance the model's ability to generate accurate and chemically consistent predictions on real-world data.
3 Methodology
Our framework is illustrated in Fig. 2 Section 3.1 introduces a unified vocabulary for representing
both mass spectra and molecules. Section 3.2 describes multi-task pretraining with reliably com- puted fingerprints. Section 3.3
finetunes the model on experimental spectra to adapt to real-world distributions, while Section 3.4 further aligns molecular generation through chemical feedback.
3

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Stepl: Unified Multi-Task Pretraining on Reliably Computed Fingerprints
MS-BART
Step2&3: Finetuning & Alignment on Experimental Spectra
Generative Probability
Tanimoto Similarity
Cross-Entropy Loss
Formula
Pretrained MIST
EXC
MS-BART
Model Inference
Rank Loss
Figure 2: Overview of the MS- MS-BART framework. Square symbols represent unified tokens, where denotes SELFIES tokens, indicates fingerprint tokens, and represents special separator token between SELFIES and fingerprint tokens. The symbol signifies padding tokens. Masked tokens are represented by striped patterns ( The top row illustrates pretraining tasks utilizing reliably computed fingerprints (Computed by RDKit), designed to enable MS-BART to capture fundamental patterns in both fingerprint and SELFIES representations. Transfer learning is subsequently applied to experimental spectra through finetuning and alignment to enhance structure elucidation performance.
3.1 Mass Spectra and Molecular Representation
Mass Spectra Representation. Raw mass spectra consist of variable-length sets of peaks (Pi, P where each peak P. = (M, 4)) represents a mass-to-charge ratio (m/z) and corresponding intensity. Due to noise and variability in experimental spectra, learning from raw spectra directly is challenging. induced dissociation (CID). producing charged product ions and uncharged neutral loss fragments. These fragmentation patterns reflect structural information and correspond to chemical fragment has explored on predicting molecular
tandem mass spectrometry, precursor ions undergo collision-
encoded in molecular fingerprint bits. Prior work [II [8] 2 fingerprints from spectra by leveraging fragmentation information. Following this, we represent each spectrum as 4096-bit cireular Morgan fingerprint FP (0, 1) 14996, where each bit indicates the presence of a specific substructure. For experimental data, we employ the pretrained MIST model [T8) to predict molecular fingerprints FP from mass spectra, conditioned on the chemical formula. The output of MIST is probability vector {Po..... P1095 where each indicates the predicted likelihood of the i-th fingerprint bit being active. To convert these probabilities into a binary fingerprint representation, we apply a threshold e:
1, FP, = 0, otherwise,
pf 2c,
Vi (0, 1, 40953.
Each activated bit (FP; 1) is converted into a fingerprint token of the form <fp (e.g., <fp0123>), producing a token sequence suitable for language modeling. We also compute the fingerprints from unlabeled molecules using RDKit (27 and apply the same tokenization for subsequent pretraining.
Molecular Representation. SMILES 46 and SELFIES 25 are two widely used string-baser
molecular representations. While SMILES is compact and human-readable, it does not guarantee chemical validity. In contrast, SELFIES is designed to ensure that every valid string maps chemically feasible molecule. We adopt SELFIES in MS-BART for its robustness and validity guarantees. To ensure consistency and uniqueness, we use the canonical form of each SELFIES string. Following [15), we employ vocabulary of 185 SELFIES tokens. Although this is significantly smaller than typical language model vocabularies, prior work 37 shows that compact chemical vocabularies are sufficient for effective molecular representation learning.

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3.2 Unified Multi- Tast Pretraining on Reliably Computed Fingerprints
Given the tokenization approach described above, we tokenize mass spectra and molecular sequences by MIST [18] and the aforementioned vocabulary, respectively. As illustrated in Fig. 2 we design three self-supervised denoising tasks for pretraining to recover masked spans, along with one cross- modal translation task to strengthen modality alignment. We pretrain MS-BART dataset of million fingerprint-moles
a simulated pairs. The molecules are provided by MassSpecCiym 7 and are preprocessed by excluding those with an MCES distance of less than two from any molecule the test fold. The pretraining framework comprises four tasks detailed as follows: (1) SELFIES Denoising (L). Randomly mask 30% of tokens in SELFIES sequence S = (......, 8t)
with [MASK] token and recover original tokens. (2) pnneerprinttt-Mooleen
Translation (②).
Generate SELFIES sequences conditioned fingerprint tokens FP. (3) Hybrid Denoising (Q and D). Combine fingerprint tokens and masked SELFIES using separator
with input order variants, [FP, <fps_s seps,,, Smasked] and [Smasked: <fps_sep>> FP,, predicting full SELFIES sequences from both modalities. All tasks follow a conditional generation paradigm optimized via cross-entropy loss:
Lee Σ log Ply Men X;0)
where ! denotes the model parameters, X is the input sequence (c.g., masked SELFIES or finger- prints), y/ is the i-th target token, and / is the target SELFIES length.
3.3 Finetuning on Experimental Spectra
After pretraining on the simulated fingerprint- -mopeull dataset, mental spectra to bridge the domain gap between computational and real-world data distributions. As shown in Fig. 2 the original mass spectra are tokenized into fingerprint tokens, and the model is also optimized through cross-entropy loss (Eq. 2 calculated between the target and predicted SELFIES tokens. This crucial step aims to learn the systematic bias introduced by the MIST [8] model and improve prediction performance.
is fine-tuned on experi-
3.4 Contrastive Alignment via Chemical Feedback
Following pretraining and dataset-specific fine-tuning, MS-BART acquires the capability to interpret molecular fingerprints and generate chemically plausible molecular structures. However, remains susceptible to molecular hallucination (15), where generated molecules maintain chemical validity but exhibit limited consistency with original mass spectra or corresponding fingerprints. Specifically, the model may yield structures deviating substantially from true underlying molecular structures. To alleviate this hallucination and enhance performance, we propose aligning the model's probabilistic rankings of generated molecules with preference rankings derived from chemical contexts. In this paper, we define molecular preference through Tanimoto similarity, denoted as Ps(-). Given fingerprint FP, -ART generates n candidate molecules C (S1, S2, ",,,,,,. S.S)). The preference score for each candidate S, is calculated as Ps(S,) = T [S, S), where S represents the ground- truth molecular structure. Simultaneously, the model (parameterized by 0) assigns a conditional log-probability estimate Po(S.) to each candidate S., given the input FP. Our objective to establish consistency between the model's generative probabilities and Tanimoto similarity metrics. Specifically, for any candidate pair (St, S5), we expect:
Pa(S.) > Pf(S)). Ps(S.) Ps(S)).
To encourage AS-BART to assign higher probabilities to candidate molecules that are more struc- turally similar to the target molecule, we employ contrastive rank loss [31 15), defined as:
Lrank (C) = ΣΣ max (0, Pa())) Pa().) + ng), K.j. Ps(S)) > Ps(S)). (4)
where Tiy = (j -) denotes a margin scaled by the rank difference between candidates, y is hyperparameter. Additionally, we retain the token-level cross-entropy loss (Eq. 2 to preserve the
5

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model's generative capability and the overall loss is defined as:
Lee + rehrank,
where a controls the weight of the rank loss. By jointly optimizing the token-level cross-entropy loss and the sequence-level contrastive rank loss the same finetuning dataset, MS-BART can assign a balanced probability mass across the whole sequence. This optimization strategy elevates the probability of generating molecular structures that not only exhibit higher similarity the target molecule but may also achieve exact matches.
Experiments
4.1 Datasets
We evaluate our MS-BART model on two widely used open-source benchmarks: NPLIB1 [T]] and MassSpecGiym 77. following prior works 4 45
NPLIB1 is subset of the GNPS library, originally curated for training the CANOPUS model. Its name serves to distinguish the dataset from the associated tool. MassSpecGym is the largest publicly available dataset containing 231k high-quality mass spectra spanning 29k unique molecular structures. The dataset is partitioned into training. validation, and test sets based on the edit distance between molecular structures, facilitating robust evaluation. Although MADGEN [45] also reports performance on the NIST23 dataset, access to this resource is restricted due to its commercial licensing requirements.
4.2 Evaluation Metries and Baselines
To evaluate the performance of our model, we employ the following metries:
Top-k accuracy: We measure the exact match between the predicted structure and the ground truth molecule by converting the generated molecule into full InChIKey and comparing it with the gold InChIKey, Since MS fragmentation is largely insensitive to 3D stereochemistry, results based on 2D InChIKey are also reported in Appendix C
Top-k maximum Tanimoto similarity: This metric quantifies the structural similarity between molecules using molecular fingerprints. We compute fingerprints based on the Morgan algorithm (33 with a radius of and a bit length of 2048 using RDKit.
Top-k minimum MCES (maximum common edge subgraph): This metric measures the graph edit distance between molecules, reflecting the largest common substructure shared between the generated and ground-truth molecules [26).
We report the 1, 10 metrics following previous works 4 7 45). Mcanwhile, DIFFMS samples
100 molecules for each spectrum and identifies the top-k molecules based on frequency. To ensure fair comparison, we sample 100 molecules and subsequently rank the generated molecules according to their distance from the given formula. Given two formula F; {(11, n11), (az, n2), (0m, mm) and F2 = {a11 m1), (.)))))))))) (02, m2), (am: m the distance is defined as:
D(F,, F2)
Σ
nn, mal-
(6)
OEAII Atoms
Na, Ma are the counts of atom in F1 and F2. If multiple molecules have the same distance, sort them according to their estimated log-probability. After this re-ranking, we select the first
molecules the top-k predictions.
Baselines. MassSpecGym 7 establishes three baselines for molecular generation: random genera- tion, a SMILES Transformer, and SELFIES-based Transformer. Spec2Mol (29) is retrained on both the NPLIBI and MassSpecGym datasets to enable fair comparison. MIST+MSNovelist modifies the original MSNovelist framework [41] by replacing CSI:FingerID [1 with MIST. In MIST-Neuraldecipher, molecules are encoded into CDDD representations 47). followed by recon- struction of the SMILES strings using a pretrained LSTM decoder. MADGEN 45 and DIFFMS represent recent wate-of-the-an approaches. MADGEN first retrieves molecular scaffolds, then generates complete structures using the RetroBridge model (24), conditioned on both spectra and scaffolds. DIFFMS also adopts MIST as the spectrum encoder and employs Graph Transformer 12 as the diffusion decoder, with separate pretraining of encoder and decoder components.

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Table 1: Performance comparison of MS-BART and baseline methods on the NPLIBI 10 and MassSpecGyym 77. Results marked with are reproduced from MassSpecGyy and DIFFMS. Bold denotes the best performance, underlined indicates the second-best.
ToP-1
TOP-10
Model
ACCURACY
MCES
TANIMOTO 1 ACCURACY
MCES + TANIMOTO 1
NPLIBI
SPEC2MOL
0.00%
27.82
0.12
0.00%
23.13
0.16
MIST NEURALDECIPHER
2.32%
12.11
0.35
6.11%
9.91
0.43
MIST MSNOVELIST
5.40%
14.52
0.34
11.04%
10.23
0.44
MADGEN
2.10%
20.56
0.22
2.39%
12.69
0.27
DIFFMS
8.34%
11.95
0.35
15.44%
9.23
0.47
MS-BART
7.4%%
9.66
0.44
10.99%
8.31
0.51
MS BART(Gold Fingerprint)
73.50%
2.14
0.90
79.12%
1.60
0.94
MASSSPECGYM
SMILES TRANSFORMER"
0.00%
79.39
0.03
0.00%
$2.13
0.10
SELFIES TRANSFORMER'
0.00%
38.88
0.08
0.00%
26.87
0.13
RANDOM GENERATION"
0.00%
21.11
0.00%
18.26
SPEC2MOL"
0.00%
37.76
0.12
0.00%
29,40
0.16
MIST NEURALDECIPHER
0.00%
33.19
0.14
0.00%
31.89
0.16
MIST+ MSNOVELIST"
0.00%
45.55
0.06
0.00%
30.13
0.15
MADGEN
1.31%
27.47
0.20
1.54%
16.84
0.26
DIFEMS
2.30%
18.45
4.25
14.73
0.39
MS-BART
1.07%
16.47
1.11%
MS-BAR BART(Gold Fingerprint)
47.56%
3.26
0.85
64.62%
2.02
0.93
4.3
Implementation
We adopt BART-BASE 28 as the backbone of MS-BART initializing all parameters from scratch using a normal distribution. To convert MIST probabilities into binary fingerprints, we apply threshold of 0.2 for NPLIB1 and 0.11 for MassSpecGym. Further details regarding this selection are provided in Appendix(A) [A] During pretraining, we set the maximum sequence length to 512 accommodate simultaneous learning from both fingerprints and SELFIES. For finetuning and alignment, we fix the input and output token lengths to 256, as the fingerprint inputs and SELFIES outputs rarely exceed this length in practice. When aligning MS-BARD with chemical feedback, we freeze the encoder following practices from prior work [16 36), and update only the decoder. Additional training details are provided in Appendix B
4.4
Main Results
Table ! presents comparison of overall performance, demonstrating that MS-BART outperforms most baseline methods and achieves SOTA performance across 5/12 key metrics on NPLIBI and MassSpecGym. Notably, NPLIB1, I, MS-BARY performs the best actoss all similarity metrics, surpassing the second-bes method by 19. 16% (MCES) and 25.71 17% % (Tanimoto similarity) in the T setting. Significant improvements the effectiveness of MS BART. The high absolute Tanimoto similarity values (close to or exceeding 0.5) suggest that MS-BART generates structurally similar molecules, which particularly valuable to domain experts. However, the Top-1 and Top- accuracy do not surpass DIfIMS 4 primarily because have filtered out similar molecules in the pretraining data (Appendix B). whereas DiffMS only removes all NPLIB1 and MassSpeeGym test and validation molecules from their pretraining dataset, Another recently proposed open-source dataset, MassSpeeGyym, presents a more challenging benchmark than NPLIB I, as NPLIB1 lacks scaffold- based split, resulting in a test set containing molecules with high structural similarity (Tanimoto similarity > 0.85) to those in the training set 7. Meanwhile, MassSpecGym exhibits more complex data composition due to the presence of [M+Na]* adducts. The Na" atom have higher mass than H* atom, leading to more complex fragmentation patterns and consequently a data distribution that differs significantly from that of [M+H]* Furthermore, the [M+Na]* data in MassSpecGym training set relatively searce, constituting only 15.52% of the total samples. This class imbalance, combined with the distinct fragmentation behavior of [M+Na] compared [M+H]] introduces additional noise and potential bias during model training. To avoid fragmentation pattern conflicts and preserve data consistency,
also observed in the Top-10 10 setting, further validating

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Table 2: Performance comparison of MS-BART on the NPLIBI dataset using different pretraining strategies. "NONE" indicates no pretraining. "SD" and "TRANs" denote pretraining with SELFIES Denoising and Fingerprint-to-oole MoMolecule Translation, respectively, and "HYBRID" refers to pretraining with the Hybrid Denoising method described Section 3.2
PRETRAIN
TOP-1
ToP-10
STRATEGY
ACCURACY t
MCES
TANIMOTO
ACCURACY 1
MCES
TANIMOTO
NONE
1.71%
12.93
0.27
3.05%
11.36
0.34
SD
0.37%
14.41
0.24
0.98%
12.42
0.32
TRANS
6,23%
9.37
0.42
10.26%
7.98
0,50
HYBRID
5.13%
9.96
0.41
7.81% %
8.87
0,48
MS-BART
7.45%
9.66
0.44
10.99%
8.31
0.51
Accursty 7611
616
Preteam
PrktennFF
Pretrain- -4laank
1hat mm
PntrannF
Patrain- Tn-an-aankk
Pretrain
Pretzaine
Phetranne
Figure 3: Progressive improvement of MS-BART for molecular hallucination mitigation on MassSpec- Gym. Subfigures show: a) Tanimoto similarity, Accuracy, and c) MCES scores across three training stages under Top-1 1 and Top-10 settings. Stage 1: Pretrain, the base model pretrained on 4M simulated unlabeld molecules. Stage 2: Pretrain-FT, fine-tuned on MassSpecGym. Stage 3: Pretrain-FT Fannkk.. fully optimized MS-BART with chemical feedback.
filter out [M+Na] adducts before fine-tuning and alignment, retaining only the dominant [M+H]* data. However, to ensure fair and comprehensive evaluation, we retain the [M+Na] adducts test set. The results show that MS-BART does not surpass DIffMS most metrics, and the main reason same as for NPLIB Excluding DiffMS, MS-BART also shows SOTA performance across all similarity metrics and demonstrates the robustness and effectiveness of MS BART in handling diverse spectral patterns. Finally, we report the best possible performance of MS-BAR BART we use the gold fingerprint calculated from the true structure rather than predicting it with the pretrained MIST model. It is noteworthy that MS-BART can almost find the exact match or extremely similar candidates, proving the great potential of MS-BART and indicating that further work can be devoted to improving the performance of MIST model.
4.5 Unified Multi-Task Pretraining Enhances Cross-Modal Learning
Pretraining serves as the foundational phase and critical step in training language models, enabling broad understanding of linguistic features such as syntax, semantics, and context, which are essential for effective transfer learning. To investigate the role of multi-task pretraining in enhancing MS- BART'S comprehension of molecular fingerprints and SELFIES, we conduct ablation experiments, with results presented in Table 2 It is obvious that the model trained without pretraining exhibits relatively poor performance compared to its pretrained counterpart. Nevertheless, its accuracy remains above chance level and surpasses baseline methods that encode mass spectra directly, demonstrating the advantage of representing raw mass spectra fingerprints and training with unified vocabulary an end-to-end style. Furthermore, we compare multi-task pretraining with single-task pretraining. Pretraining solely with the denoising task leads to performance degradation rather than improvement, primarily because denoising task is not well aligned with structure elucidation. The substantial improvement observed in the fingerprin--oommleeal translation task further supports this finding. Moreover, the performance gain of MS-BARD over single-task pretraining indicates that the denoising task remains beneficial, it helps MS- BART develop fundamental understanding of molecular

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structures and contributes to the final performance. These results suggest that unified multi-task pretraining on unlabeled data promotes cross- mross-modal interaction and alignment by enabling MS- BART to learn a shared representation space across modalities.
4.6 MS-BART Mitigates Molecular Hallucination
The training paradigm of MS-BART consists of three steps. Fig. iillustrates the impact of these steps on the final performance in MassSpecGym, revealing two key findings. First, continued fine-tuning on the MassSpecGym training fold improves performance and mitigates molecular hallucination. particularly in terms of accuracy. For example, the "Pretrain" model achieves near-zero Top- and Top-10 10 accuracy scores of 0.00% and 0.01%, respectively, while the "Pretrain-FT" model improves these values to 1.07% and 1.12%. Moreover, aligning the model with chemical feedback based on Tanimoto similarity further reduces molecular ballucination. This alignment enables the generation of structurally more similar molecules, evidenced by the improved Tanimoto similarity and the degraded MCES metric. Fig. Asoows a randomly selected case from MassSpecGyym where MS-BAr MS-BART effectively mitigates molecular ballucination.
Ground Truth
Tan, si d.B. MOES 16:00
Tre-2 Ton, Sim MCES 1600
Tax: Smn 0.3. MCES 1500
Tepud Tan Sin. MCES 2000
Topel
Trp-2 MCES:
Tro-1
Toped MOES
1500
0.5%
Ton, Sm
Top-é
Figure 4: Predictions of the Pretrain (top row), Pretrain-FFT (middle row), and Pretrain-FT-Rank (bottom row) models on representative MassSpecGyy sample, with the ground-truth structure shown the first column and the Top-4 generated outputs in the subsequent columns. Results demonstrate that MS-BAA BART can effectively mitigate molecular hallucination and predict more chemically plausible and structurally consistent predictions.
4.7 Sensitivity Analysis Model Hyperparameters
MS-BART employs two key hyperparameters, the fingerprint generation threshold and the rank loss weight a. The fingerprint threshold determines how the MIST probability converted into an active fingerprint, where different thresholds lead to distinct representations. The rank loss weight controls the influence of the token-level rank loss in guiding the alignment training. To comprehensively evaluate the impact of these two parameters, of on MassSpecGym and a on NPLIB and present the results in Table 3 The results show no significant differences among the tested values, indicating that MS MS-BART is not sensitive this parameter. Although c = 0.11 achieves the best performance on the MassSpeeGym validation set and is reported in Table I as the final result, the ablation results suggest that 0.11 is not the best overall. This discrepancy is mainly due to the high difficulty of the MassSpecGym dataset and the distribution mismatch between its validation and test sets. A similar observation holds for NPLIBI. 1. The model with a = 5 performs best terms of Top-1 Tanimoto score on the validation and is also reported as the final result. However, models with competitive results on specific metrics. The variations across different configurations are minor, indicating that MS-BARE BART
experiment with different values
and a also achieve
highly sensitive the rank loss weight.
4.8 Analysis of Decoding Hyperparameters

---

Table 3: Performance comparison of MS-BART under two key model hyperparameters, where the ablation of the fingerprint generation threshold and the rank loss weight are evaluated on MassSpec- Gym and NPLIB1, respectively.
TOP-1
ToP-10
ACCURACY T MCES
TANIMOTO f
ACCURACY 1
MCES 4
TANIMOTO
Fingerprint Generation Threshhold (MassSpecGym)
0.11
1.07%
16.47
0.23
1.11%
15.12
0.28
c
0.15
1.20%
16.88
0.23
1.20%
15.45
0.28
0.20
1.19%
17.27
0.23
1.20%
15.75
0.28
Rank Loss Weight (NPLIB1)
Q
7.08%
10.56
0.44
12.45%
9.10
0.52
0
3
7.20%
9.53
0.44
10.50%
8.21
0.51
5
7.45%
9.66
0.44
10.99%
8.31
0.51
MS-BART employs beam-search multinomial sampling for structure prediction, governed by two key decoding hy- perparameters while maintaining default values for other parameters. First, the temperature parameter [50 42 regu-
Accuracy To-23 Acurusy
lates output randomness during inference, a factor known substantially influence generation quality in typical lan- guage models. Interestingly. MS-BART'S outputs demon- strate remarkable stability across temperature variations, which is likely attributable to the model's 's high confidence next-token prediction gained through multi-task pre- training and its relatively small vocabulary size of 185, Detailed temperature analysis provided in Appendix D The second critical parameter, beam width, governs the search space breadth during decoding. We systemati- cally evaluated MIS-BART's performance on the complete MassSpecGym test fold using an NVIDIA A800-SXM4- 80GB GPU, exploring beam widths from 10 to 100. As shown Fig. 5] both Top-1 and Top- 10 accuracies demon- strate consistent improvement with increasing beam width, while inference latency exhibits linear scaling. Notably, when tested on common consumer-gres grade GPU like the RTX 4090 with a beam width of 100, the average inference time per spectrum is about 3 seconds, which is 53x times faster than DiffMS's approximately 160 seconds and remains practically acceptable.
24
68
100
Figure 5: Performance of MS-BART with beam-search multinomial sampling decoding under different beam widths: Top-1 1 accuracy. Top- accuracy, and average inference time per spectrum.
Conclusion
In this work, we propose MS-BART, novel language model for mass spectra structure elucidation within unified modeling framework. Specifically, we first represent mass spectra as fingerprints and tokenize both the fingerprints and molecular representations (SELFIES) using unified vocabu- lary. Subsequently, MS-BART BART undergoes the standard pretraining vecrrining-finetuuinng. commonly employed in NLP, enabling the model to interpret spectral fingerprints and generate plausible molecular structures. Extensive experiments demonstrate that MS-BART achieves SOTA performance on two widely adopted benchmarks across 5/12 key metries on MassSpecGym and NPLIB1 and is faster by one order of magnitude than competing diffusion- based method, while ablation studies further validate its effectiveness and robustness.
paradigm
10

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Acknowledgments and Disclosure of Funding
This work was supported by the National Science and Technology Major Project (20232D0120703) the China NSFC Projects (U23B2057, 62120106006, and 92370206), and Shanghai Municipal Science and Technology Projects (2021SHZDZZ0102 and 25X010202846).
References
[1] Ruedi Aebersold and Matthias Mann. Mass spectrometry- proteomics. Nature, 422(6928):198-2000 2003.
[2] Sadjad Fakouri Baygi and Dinesh Kumar Barupal. Idsl mint: deep learning framework to predict molecular fingerprints from mass spectra. Journal of Cheminformatics, 16(1):8, 2024.
[3] Wout Bittremicux, Mingxun Wang, and Pieter C Dorrestein. The critical role that spectral libraries play in capturing the metabolomics community knowledge. Metabolomics, 18(12):94, 2022.
[4] Montgomery Bohde, Mrunali Manjrekar, Runzhong Wang, Shuiwang Ji, and Connor W Co- ley. Diffms: Diffusion generation of molecules conditioned on mass spectra. arXiv preprint arXiv:250..09571, 2025.
[5] Jane Bromley, Isabelle Guyon, Yann LeCun, Eduard Säckinger, and Roopak Shah. Signature verification using a" siamese" time delay neural network. Advances in neural information processing systems, 6, 1993.
[6] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33: 33:1877-1991, 2020.
[7] Roman Bushuiev, Anton Bushuiev, Niek de Jonge, Adamo Young, Fleming Kretschmer, Raman Samusevich, Janne Heirman, Fei Wang, Luke Zhang, Kai Dührkop, et al. Massspecgym: A benchmark for the discovery and identification of molecules. Advances in Neural Information Processing Systems, 37:1 177110001011007,, 2024.
[8] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume (long and short papers), pages 4171 11186, 2019.
[9] Kai Dühhkop, Markus Fleischauer, Marcus Ludwig, Alexander A Aksenov, Alexey V Melnik, Marvin Meusel, Pieter C Dorrestein, Juho Rousu, and Sebastian Böcker. Sirius 4: rapid tool for turning tandem mass spectra into metabolite structure information. Nature methods. 16(4):299-302, 2019.
[10] Kai Dührkop, Louis-Félix Nothias, Markus Fleischauer, Raphael Reher, Marcus Ludwig, Martin A Hoffmann, Daniel Petras, William H Gerwick, Juho Rousu, Pieter C Dorrestein, et al. Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra. Nature biotechnology, 39(4):462-44 471, 2021.
[11] Kai Dührkop, Huibin Shen, Marvin Meusel, Juho Rousu, and Sebastian Böcker. Searching molecular structure databases with tandem mass spectra using csi: Fingerid. Proceedings of the National Academy of Sciences, 1 112(41); 1 15580-1255, 2015.
[12] Vijay Prakash Dwivedi and Xavier Bresson. A generalization of transformer networks to graphs. arXiv preprini arXiv:2012.09669 2020.
[13] David Elser, Florian Huber, and Emmanuel Gaquerel. Mass2smiles: deep learning based fast prediction of structures and functional groups directly from high-resolution ms/ms spectra. bioRxiv, pages 2023-07, 2023.

---

[14] Muhammad Faizan-Khan, Roger Giné, Josep M Badia, Maribel Pérez-Ribera Alexandra Junza, Maria Vinaixa, Marta Sales-Pardo, Roger Guimerá, and Oscar Yanes. Chemembed: A deep learning framework for metabolite identification using enhanced ms/ms data and multidimensional molecular embeddings. hioRxiv, pages 2025 02, 2025.
[15] Yin Fang, Ningyu Zhang, Zhuo Chen, Lingbing Guo, Xiaohui Fan, and Huajun Chen. Domain- agnostic molecular generation with chemical feedback. In The Twelfth International Conference on Learning Representations.
[16] Giacomo Frisoni, Paolo Italiani, Stefano Salvatori, and Gianluca Moro. Cogito ergo summ: abstractive summarization of biomedical papers via semantic parsing graphs and consistency rewards. In Proceedings of the AAAI conference on artificial intelligence, volume 37, pages 12781-12789 2023.
[17] Samuel Goldman, John Bradshaw, Jiayi Xin, and Connor Coley. Prefix-tree decoding for predicting mass spectra from molecules. Advances in neural information processing systems, 36:48548-444 48572, 2023.
[18] Samuel Goldman. Jeremy Wohlwend, Martin Stražar, Guy Haroush, Ramnik J Xavier, and Connor W Coley. Annotating metabolite mass spectra with comainninssiiee chemical formula transformers. Nature Machine Intelligence, (9):9955-979 2023.
[19] Yang Han, Yiming Wang, Rui Wang, Lu Chen, and Kai Yu. Alignsum: Data pyramid hierarchical fine-tuning for aligning with human summarization preference. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 8506 8506-8522,110 2024.
[20] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 16000-16009, 2022.
[21] Xiu Huang, Huihui Liu, Dawei Lu, Yue Lin, Jingfu Liu, Qian Liu, Zongxiu Nie, and Guibin Jiang. Mass spectrometry for multi-dimensional characterization of natural and synthetic materials at the nanoscale. Chemical Society Reviews, 50(8):5243-5280 2021.
[22] Florian Huber, Lars Ridder, Stefan Verhoeven, Jurriaan H. Spaaks, Faruk Diblen, Simon Rogers, and Justin J. J. van der Hooft. Spec2vee: Improved mass spectral similarity scoring through learning of structural relationships. PLoS Comput. Biol., 17(2), 2021.
[23] Florian Huber, Sven van der Burg, Justin JJ van der Hooft, and Lars Ridder. Ms2deepscore: a novel deep learning similarity measure to compare tandem mass spectra. Journal of cheminfor- matics, 13(1):84, 2021.
[24] Ilia Igashov, Amee Schneuing. Marwin Segler, Michael M Bronstein, and Bruno Correia. Retro- bridge: Modeling retrosynthesis with markov bridges. The Twelfth International Conference on Learning Representations.
[25] Mario Krenn, Florian Häse, AkshatKumar Nigam, Pascal Friederich, and Alan Aspuru-Guzik. Self-referencing embedded strings (selfies): A 100% robust molecular string representation. Machine Learning: Science and Technology, 144):045024, 2020.
[26] Fleming Kretschmer, Jan Seipp, Marcus Ludwig, Gunnar W Klau, and Sebastian Böcker. Small molecule machine learning: All models are wrong, some may not even be useful. bioRxiv, pages 2023-03 03, 2023.
[27] Greg Landrum. Rdkit documentation. Release, 1(1-79):4, 2013.
[28] Mike Lewis. Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. Bart: Denoising sequence-to-sequemne pre- training for natural language generation, translation, and comprehension. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, page 7871. Association for Computational Linguistics, 2020.
12

---

[29] Eleni E Litsa, Vijil Chenthamarakshan, Payel Das, and Lydia E Kavraki. An end-to-end deep learning framework for translating mass spectra to de-novo molecules. Communications Chemistry, 6(1 ((1):132, 2023.
[30] Yanmin Liu, Xuan Zhang, Wei Zhao, Daming Zhu, and Xuefeng Cui. De novo molecular struc- ture generation from mass spectra. In 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pages 373-378 IEEE, 2023.
[31] Yixin Liu, Pengfei Liu, Dragomir Radev, and Graham Neubig. BRIO: Bringing order to abstractive summarization. In Smaranda Muresan, Preslav Nakov, and Aline Villavicencio, editors, Proceedings of the 60th Annual Meeting of the Association for Computational Linguis- tics (Volume I: Long Papers), pages 2890-2903, Dublin, Ireland, May 2022. Association for Computational Linguistics.
[32] Felix Meissner, Jennifer Medstes-MAAlster,, Matthias Mann, and Marcus Bantscheff. The emerging role of mass spectrometry- proteomics in drug discovery. Nature Reviews Drug Discovery, 2179):637-664, 2022.
[33] Harry L Morgan. The generation of a unique machine description for chemical structures- a technique developed at chemical abstracts service. Journal of chemical documentation. 5(2):107-113, 1965.
[34] Michael Murphy, Stefanie Jegelka, Ernest Fraenkel, Tobias Kind, David Healey, and Thomas Butler. Efficiently predicting high resolution mass spectra with graph neural networks. In International Conference Machine Learning, pages 25562. PMLR, 2023.
[35] Yolanda Picó and Damià Barceló. Pyrolysis gas chromatography- spectrometry in envi- rommental analysis: Focus on organic matter and microplastics. TrAC Trends in Analytical Chemistry, 130: 15964, 2020.
[36] Alessandro Raganato and Jörg Tiedemann. analysis of encoder representations in transformer-basee machine translation. In Proceedings of the 2018 EMNLP workshop Black- boxNLP: analyzing and interpreting neural networks for NLP, pages 287-297, 2018.
[37] Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C Lawrence Zitnick, Jerry Ma, al. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proceedings of the National Academy of Sciences, 118(15)ee216223111 2021.
[38] Khawla Seddiki, Philippe Saudemont, Frédéric Precioso, Nina Ogrine, Maxence Wisztorski, Michel Salzet, Isabelle Fournier, and Arnaud Droit. Cumulative learning enables convolu- tional neural network representations for small mass spectrometry data classification. Nature communications, 11(1::5595, 2020.
[39] Michael A Skinnider, Fei Wang. Daniel Pasin, Russell Greiner, Leonard J Foster, Petur W Dals- gaard, and David S Wishart. A deep generative model enables automated structure elucidation of novel psychoactive substances. Nature Machine Intelligence, 3(1 3(11):973 9884, 2021.
[40] Stephen Stein. Mass spectral reference libraries: an ever-expanding resource for chemical identification, 2012.
[41] Michael A Stravs, Kai Dührkop, Sebastian Böcker, and Nicola Zamboni. Msnovelist: de novo structure generation from mass spectra. Nature Methods, 19(7):865-87) 8700 2022.
[42] Chi Wang, Xueqing Liu, and Ahmed Hassan Awadallah. Cost-effective hyperparameter opti- mization for large language model generation inference. In Aleksandra Faust, Roman Gamett, Colin White, Frank Hutter, and Jacob R. Gardner, editors, Proceedings of the Second Interna- tional Conference on Automated Machine Learning, volume 224 of Proceedings of Machine Learning Research, pages 21/1-17. PMLR, 12-15 Nov 2023.
[43] Fei Wang, Jaanus Liigand, Siyang Tian, David Arndt, Russell Greiner, and David S Wishart. Cfm-id 4.0: more accurate esi-ms/ms spectral prediction and compound identification. Analyti- cal chemistry, 91924))11622-1170000 2021.
13

---

[44] Mingxun Wang. Jeremy J Carver, Vanessa V Phelan, Laura M Sanchez, Neha Garg, Yao Peng. Don Duy Nguyen, Jeramie Watrous, Clifford A Kapono, Tal Buzzatto-Knaani et al. Sharing and community curation of mass spectrometry data with global natural products social molecular networking. Nature biotechnology. 34(8):828 837, 2016.
[45] Yinkai Wang, Xiaohui Chen, Liping Liu, and Soha Hassoun. MADGEN; Mass-spec attends de novo molecular generation. In The Thirteenth International Conference on Learning Representations, 2025.
[46] David Weininger. Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences, 28(1):31-36, 1988.
[47] Robin Winter, Floriane Montanari, Frank Noé, and Djork-Aréé Clevert. Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations. Chemical science, (096))1692-17000 2019.
[48] Lin Yao, Minjian Yang, Jianfei Song, Zhuo Yang, Hanyu Sun, Hui Shi, Xue Liu, Xiangyang Ji, Yafeng Deng, and Xiaojian Wang. Conditional molecular generation net enables automated structure elucidation based on 13c nmr spectra and prior knowledge. Analytical chemistry. (5(12):5333-5400) 2023.
[49] Hailiang Zhang, Qiong Yang, Ting Xie, Yue Wang, Zhimin Zhang, and Hongmei Lu. Msbert: Embedding tandem mass spectra into chemically rational space by mask learning and contrastive learning. Analytical Chemistry,
16608, 2024.
[50] Yuqi Zhu, Jia Li, Ge Li, YunFei Zhao, Zhi Jin, and Hong Mei. Hot or cold? adaptive temperature sampling for code generation with large language models. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 437 4474 2024.
A Selection of the Probability Threshold
We selected the threshold value for NPLIB1 I and MassSpecGyn using different methods. Since NPLIB1 is known to be much easier dataset, we chose its threshold empirically. After examining the probability distribution, we observed that most probabilities were smaller than 0.3. A large threshold could cause information loss, whereas small threshold might introduce noise. Therefore, we selected = 0.2 to balance these factors, which resulted in excellent performance. For MassSpecGym, we trained MS-BARTusing values ranging from 0.1 to 0.2 in increments of 0.01. We then validated these on the validation set and selected the best threshold based on the highest Top-1 Tanimoto similarity. As shown in Table 4 the Top-1 Tanimoto similarity values for different thresholds were very close. We ultimately chose = 0.11 as the final threshold, although performance did not vary significantly across the different threshold values.
Table 4: Top-1 Tanimoto Similarity under different values.
0.10
0.11
0.12
0.13
0.14
0.15
0.16
0.17
0.18
0.19
0.20
Tanimoto Similarity 0.1666
0.1678
0.1640
0.1651
0616
061660
0.1654
0.1643
0.1651
0.1649
0.1636
B MS-BART Training Details
Pretraining. We pretrain MS-BAR' from scratch on simulated dataset of 4M fingerprint-molecule pairs. This dataset was generated from a refied subset of the 4M unlabeled molecules provided by MassSpeeGym (7]. The refinement process involved excluding any molecule with an MCES distance of less than two from any molecule in the MassSpecGyn test fold. For the NPLIB1 dataset, we first evaluated the structural similarity between its test set and our pretraining data. As shown Table 5 approximately 3% of the pretraining molecules were structurally similar to those in the NPLIB I test set. To prevent data leakage, we further filtered the pretraining set to remove any molecules that were structurally similar or identical to those in the NPLIBI test set. Specifically, refined the
14

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pretraining dataset by removing molecules with maximum Tanimoto similarity greater than 0.5 to any molecule in the NPLIBI set.
The training was conducted on four NVIDIA A800-SXM4-80GB GPUs using 6 precision. A per-device batch size of 96 and gradient accumulation steps were employed, resulting in an effective total batch size of 768 across three training epochs. The optimization process adopted cosine learning rate scheduler with a warm-up phase of 10,000 steps. The learning rate increased linearly from zero to a peak value of 6e-4 during warm-up and subsequently decayed following cosine schedule minimum value of le-5. The entire multi-task pretraining process required approximately 34 hours to complete.
Table 5: Distribution of max Tanimoto Similarity between pretraining set and NPLIB] I test set.
Similarity Interval 0.0-0.1 0.1-0.2
0.2-0.3
0.3-0.4
0.4-0.5
0.5-0.6
0.6-0.7
0.7-0.8
0.8-0.9
0.9-1.0
Proportion 0.09%
8.40%
55.85%
21.58%
6.79%
3.79%
1.84%
1.20%
0.40%
0.06%
Finetuning. Fine-tuning performed on a single NVIDIA A800-SXM4-80GB GPU using bfloat 16 precision, with consistent parameter settings across both MassSpecGym and NPLIB datasets. We adopt a learning rate 5e-5 combined with a warm-up phase covering 10% of total training steps. Each training iteration processes 128 samples per batch. For validation monitoring, we implement an early stopping criterion that evaluates model performance every 400 steps on MassSpecGym and every 200 steps NPLIB1. 1. Training terminates when the validation set's Top-1 Tanimoto similarity fails to improve for three consecutive evaluations.
Alignment Training. The aligmment phase is more complex than pretraining and fine-tuning, requiring careful hyperparameter optimization. As specified in Table we explore multiple config- urations during this stage while maintaining a fixed batch size of 128 and 6 precision on single NVIDIA A800 GPU. The learning schedule includes a 10% warm-up ratio of total training steps. Validation frequency is set to 400-step intervals for MassSpecGym and 50-step intervals for NPLIBI. We extend the patience to five consecutive evaluations without improvement Top-1 Tanimoto similarity before triggering early stopping. Final hyperparameter selection is determined by maximizing the validation set's Top-1 Tanimoto similarity.
Table 6: parpmmte settings.
flyee-paraaetter
Values
Learning Rate
[le-5, 5e-5 {0.05, 0.2)
Candidate Margin y
Rank Loss Weight a
[1, 3,
Number of Candidates
(3.5)
Length Penalty Coefficient
{1.4, 1.6)
C 2D InChIKey Based Accuracy
We evaluate the Top-1 and Top-10 accuracy based on 2D InChlKey matching and present the results in Table On NPLIB1, the calculation methods yield the same results. However, for MassSpecGym, the 2D InChIKey-based accuracy of MS-BARI shows a slight improvement compared to evaluation with the full InChIKey because the 2D InChiKey uses only the first 14 characters, which do not include 3D stereochemistry. To maintain consistency with the DifIMS 4 we use the full InChlKey results as the final results.
D The Impact of Sampling Temperature Model Performance
Table g presents the performance evaluation MS-BAN BART during decoding with varying temperature values on MassSpecGyym. The results demonstrate that MS-BART'S performance remains largely consistent across different temperature settings. This stability can be attributed to the model's 's high confidence in next-token predictions, which likely stems from its multi-task pretraining framework.
15

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Table 7: 2D InChIKey based Top-1 and Top- 10 accuracy on NPLIBI and MassSpecGym.
Calculation Method
Top-1 Accuracy
Top- 10 Accuracy
NPLIB1
MS-BART (InChlKey) MS BART (2D InChIKey)
7.45%
10.99%
7.45%
10.99%
MASSSPECGYM
MS- MS-BART (InChlKey) MS-BART (2D InChlKey)
1.07% 1.26%
1.11% 1.28%
Table 8: The performance of MS-BART when decoding with different temperature on MassSpecGym.
TOP-1
TOP-10
TEMPERATURE
ACCURACY
MCES
TANIMOTO
ACCURACY
MCES
TANIMOTO
Prtrain-FT
0.2
1.07%
17.16
0.22
1.12%
15.51
0.27
0.4
1.07%
17.17
0.22
1.12%
15.51
0.27
1.07%
17.16
0.22
1.12%
15.50
0.27
MS-BART
0.2
1.07%
16.47
0.23
1.11%
15.12
0.28
0.4
1.07%
16.47
0.23
1.11%
15.12
0.28
1.07%
16.47
0.23
1.11%
15.11
0.28
Limitations
Our primary limitation lies in the reliance the external MIST model (18 for predicting fingerprints from experimental spectra. The accuracy of these fingerprint predictions significantly impacts the overall performance. Future work may focus on fine-tuning the MIST model using task-specific datasets to improve prediction accuracy, or it could explore directly modeling the fragments from raw spectra. Another limitation is that the formula information is used only for re-ranking and not for training the model. However, these additional formulas definitely include important information and are very likely to boost the model's performance. We plan explore effective ways to incorporate these informational hints into MS- BART in future work.
F
Ethics Statement
data used in this work are obtained from publicly available sources, including molecular structure datasets and mass spectrometry benchmarks such NPLIBI and MassSpecGym. We strictly follow the corresponding licenses and usage protocols. No modifications have been made to the original datasets beyond necessary preprocessing using standard cheminformatics tools (c.g., RDKit) to compute molecular fingerprints. No personal. private, or sensitive data are involved. The proposed model, MS-BARD BART, is trained and evaluated solely for the task of molecular structure elucidation from mass spectrometry data. Our work does not involve human subjects, biometric data, or decision- making in socially sensitive applications. We do not foresee any immediate negative societal impact. On the contrary, improving molecular identification from mass spectrometry may benefit fields such drug discovery, environmental chemistry, and materials science.
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Reviews:
[{"review_id": "9bj51RCvQV", "reviewer": "Reviewer_huv7", "summary": "This paper proposes MS-BART, a unified modeling framework that represents both mass spectra and molecular structures using a shared vocabulary, enabling pretraining–finetuning–alignment in the style of large language models. The authors represent mass spectra via molecular fingerprints, then pretrain on 4M unlabeled fingerprint–molecule pairs, and finally adapt to experimental data using MIST-derived fingerprints and a contrastive alignment step based on Tanimoto similarity. The model demonstrates state-of-the-art performance on NPLIB1 and MassSpecGym benchmarks across most key metrics.", "questions": "- **Pretraining Task Ablation**:\nThe paper mentions three pretraining tasks—SELFIES denoising, fingerprint-to-molecule translation, and hybrid denoising—but only compares \"translation only\" versus \"multi-task\" in the ablation study. Could you provide a detailed ablation study of each pretraining task individually to clarify their contributions to the final performance?\n- **Impact of MIST Fingerprint Predictions**:\nYour approach relies on MIST for generating fingerprints from experimental spectra. Can you discuss how inaccuracies in these predictions might affect the overall model performance, and whether there are plans to improve or replace MIST in future versions?\n- **Practical Applicability and Accuracy**:\nThe top-1 accuracy on the MassSpecGym dataset remains under 5%, which is still quite low. How do you envision the model being used in practical applications, and what steps will be taken to improve performance in real-world scenarios? Are there specific strategies to enhance accuracy?", "limitations": "Yes, the authors have addressed the limitations, particularly with regard to the reliance on MIST for fingerprint predictions. However, it would be beneficial for the authors to more explicitly discuss how these limitations may affect the model's generalizability and any strategies they plan to implement to overcome these challenges.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strengths**:\n- The paper addresses a timely and challenging problem: molecular structure elucidation from mass spectrometry data, where annotated spectra are scarce and modeling remains difficult.\n- The proposed MS-BART framework integrates ideas from large language models into the specific problem - structure elucidation from MS. The use of a shared vocabulary between mass spectra (via fingerprints) and molecules (via SELFIES) is conceptually elegant and enables a unified pretraining and generation paradigm.\n- The paper is generally well-organized and readable, with helpful visualizations (e.g., Figures 2–5), and includes detailed training and evaluation protocols in the appendix to support reproducibility.\n\n**Weaknesses**:\n- While the method includes three distinct pretraining tasks (SELFIES denoising, fingerprint-to-molecule translation, and hybrid denoising), the ablation study only compares “translation only” and “multi-task” training. It is unclear how each task individually contributes to final performance. More fine-grained ablation would help validate the design choices.\n- The components of the proposed framework—including fingerprint representations, masked sequence modeling, and rank-based alignment loss—are adapted from prior work. And similar methods have been applied to very closely related tasks, such as in “Conditional Molecular Generation Net Enables Automated Structure Elucidation Based on 13C NMR Spectra and Prior Knowledge”, which also uses a unified token vocabulary, similar pretraining tasks, and finetuning for structure elucidation. In this paper, the primary contribution seems to lie in adapting these methods to mass spectrometry, rather than introducing fundamentally new techniques. \n- Despite improved performance over prior models, the absolute accuracy remains low (e.g., top-1 accuracy < 4% on MassSpecGym), suggesting the method remains far from real-world application. This limits immediate practical applicability and calls for more discussion about failure cases, limitations, or downstream usage scenarios.\n- Minor typographical errors (e.g., “hallucaination”, “simliair”, “slected”).", "was_revised": true, "final_justification": "The authors provide detailed rebuttal and additional experiments. My concerns have been addressed. I am raising my score to 4.", "rebuttal": "Dear Reviewer huv7,\n\nThank you for your thoughtful comments and the time you have taken to review our paper. We are pleased that you find the shared vocabulary conceptually elegant and the paper well-organized and readable. In response to your concerns, we will address each point below.\n> W1&Q1:  More fine-grained ablation would help validate the design choices.\n\nThank you for your suggestion. Following your feedback, we conducted a more detailed ablation study on the NPIB1 dataset using clean pretraining data (4M_leq50, as detailed in our response to Reviewer n2SS, Major Concern 2). The results are as follows.\n\n| Pretrain strategy  | Top1 Accuracy | Top1 MCES | Top1 Tanimoto | Top10 Accuracy| Top10 MCES | Top10 Tanimoto |\n|-----|------|------|-------|---------|-------|--------|\n|    None            | 1.71%    | 12.93  | 0.27  | 3.05%   | 11.36 | 0.34   |\n| SELFIES denoising | 0.49%    | 14.43  | 0.24  | 1.83%   | 12.35 | 0.32   |\n| translation       | 7.57%    | 9.10   | 0.43  | 11.84%  | 7.78  | 0.51   |\n| hybrid denoising  | 5.86%    | 10.14  | 0.42  | 8.55%   | 9.07  | 0.49   |\n| MS-BART(All)      | 7.20%    | 9.53   | 0.44  | 10.50%  | 8.21  | 0.51   |\n\nThe table shows that pretraining with translation or hybrid denoising improves performance compared to no pretraining. While SELFIES denoising pretraining shows slightly lower performance, this may stem from insufficient pretraining (all experiments use the same hyperparameters as the final MS-BART). However, this does not contradict our proposed pretraining-finetuning-alignment paradigm, as pretraining still enhances cross-modal learning.\nIn future work, we plan to explore why the denoising task negatively affects final performance and how to optimize it so that all pretraining tasks contribute positively.\n\n\n> W2:  Similar method proposed in paper: \"Conditional Molecular Generation Net Enables Automated Structure Elucidation Based on 13C NMR Spectra and Prior Knowledge\" \n\nThank you for identifying the related paper. However, our approach has clear distinctions from the referenced work.\n\nFirst, mass spectrometry is much more complicated than NMR because it suffers from great complexity and heterogeneity, making it hard to model. Moreover, annotated experimental mass spectra are scarce, and the predicted mass spectra vary significantly from the experimental ones, which are not available for direct large-scale pretraining.\n\nSecond, our model transfers these two disparate modalities into a shared Transformer vocabulary, enabling end-to-end learning. This is definitely novel, as the reference paper only uses number sequences to represent the NMR shift.\n\nThird, the alignment stage is clearly different from fine-tuning because it further aligns the model to generate more accurate predictions using the same training set, whereas the reference paper uses more data, which is scarce in the mass spectrometry domain.\n\nFinally, thank you for pointing this out. We will also discuss this paper in the related work section of our future version.\n\n> W3&Q3: Despite improved performance over prior models, the absolute accuracy remains low (e.g., top-1 accuracy < 4% on MassSpecGym), suggesting the method remains far from real-world application. This limits immediate practical applicability and calls for more discussion about failure cases, limitations, or downstream usage scenarios.\n\nThank you for your insightful feedback. Our work primarily aims to **discover novel molecules from the dark chemical space, where the spectra are not in the annotated spectra library** or even the corresponding molecules are not in the molecule library, making traditional library matching definitely invalid and resulting in zero accuracy performance. Although the exact accuracy is low, the retrain experiments on the clean dataset reveal that MS-BART can indeed discover truly novel molecules. Moreover, as discussed in paper [1], generating similar but not exactly matching structures is still useful for domain experts to narrow down the chemical space.\n\nPrevious experiments conducted on the contaminated data, along with the strong performance observed on the golden fingerprint benchmark (detailed in response to Reviewer n2SS, Major concern 1), suggest two clear strategies to improve overall results. First, future work can be devoted to improving the performance of fingerprint prediction, such as DreaMS [2]. Second, for the known molecules and targeted spectra annotation, it would significantly boost the performance if similar molecules or the true molecule are in the training data.\n\nFinally, the unified vocabulary also provides **strong insights on how to incorporate MS spectra into LLMs rather than using the directly noisy number sequence**, opening a new strategy to align the spectra modality with language models. This would inspire many innovative ideas, such as reasoning models for explainable strcuture elucidation.\n\n\n[1] DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectra. ICML2025.\n\n[2] Self-supervised learning of molecular representations from millions of tandem mass spectra using DreaMS. Nat. Biotechnol. 2025.\n\n\n> W4: Minor typographical errors (e.g., “hallucaination”, “simliair”, “slected”).\n\nThank you for pointing this out. We have fixed it.\n\n> Q2&Limitations: Impact of MIST Fingerprint Predictions\n\nThank you for your insightful comments. Since MS-BART uses MIST to process the MS spectra into fingerprints, how MIST performance affects downstream tasks is a common question. In our response to Reviewer LqRY (Q1), we have verified that the threshold controlling the activation of the fingerprint is robust and the performance fluctuation is not significant. Regarding the impact of MIST performance on downstream accuracy, in the table responding to Reviewer n2SS (Major Concern 1), we found that poor fingerprint prediction reduces the exact match accuracy but does not affect the similarity metric as much. These quantified results demonstrate that MS-BART is robust to fingerprint noise and is promising for real-world applications, as similar but not exactly matching structures are still useful for domain experts to narrow down the chemical space [1]. Future work can focus on improving MIST with more high-quality data, projecting MIST embeddings into LLM space for joint pre-training and training, or directly modeling formula peaks with LLM following MIST.\n\n[1] DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectra. ICML2025."}, {"review_id": "yrCOEc8Pkh", "reviewer": "Reviewer_gPth", "summary": "MS-BART addresses the challenging task of inferring molecular structures from tandem mass spectra by recasting both spectra and molecules into a unified token language—spectra become sequences of 4,096-bit Morgan fingerprint tokens and molecules are encoded as SELFIES tokens—so that a single Transformer can learn cross-modal correspondences . It employs a three-stage training pipeline: multi-task pretraining on millions of simulated fingerprint–molecule pairs (combining denoising and translation objectives), finetuning on experimental spectra using MIST-predicted fingerprints to adapt to real-world noise, and a novel contrastive “chemical feedback” alignment loss that ranks generated candidates by Tanimoto similarity to the true structure to suppress hallucinations . When evaluated on the NPLIB1 and MassSpecGym benchmarks, MS-BART achieves state-of-the-art top-1 and top-10 accuracies while substantially reducing erroneous predictions, demonstrating that large-scale pretraining and feedback alignment can effectively bridge the gap between spectral data and molecular graphs .", "questions": "To what extent do errors in MIST-predicted fingerprints degrade downstream structure elucidation?\n\nDoes MS-BART generalize to spectra acquired under different collision energies, adduct types, or instrument platforms?", "limitations": "Yes", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "## Strength\nBy tokenizing spectra as Morgan-fingerprint tokens and molecules as SELFIES, MS-BART brings two disparate modalities into a shared Transformer vocabulary, enabling end-to-end learning rather than separate feature engineering and model training.\n\n\n\nLeveraging 4 million simulated fingerprint–molecule pairs and combining denoising plus translation objectives helps the model learn robust spectral–structure correspondences before seeing scarce experimental data.\n\n\n\nMS-BART outperforms prior methods on both NPLIB1 and MassSpecGym in top-1 and top-10 accuracy, as well as in MCES andaverage Tanimoto metrics, demonstrating real-world value on public benchmarks.\n\n\n\nThe paper includes ablations showing the payoff of each stage (pretrain → finetune → align) and experiments on beam width and temperature, giving practitioners guidance on hyperparameter trade-offs.\n\n## Weakness\n\nFinetuning relies on MIST-predicted fingerprints; errors or biases in MIST could propagate through the model, but the paper does not quantify how fingerprint noise impacts downstream accuracy.\n\n\n\nThe unified Transformer and beam search may be resource-intensive; the paper reports relative decoding latency trends but lacks concrete runtime or memory benchmarks for real-time or large-scale deployments.\n\n\n\nThe ranking loss favors candidates with higher global Tanimoto scores, but that metric can be dominated by common substructures rather than spectral features. This may encourage the model to pick “average” molecules that share generic motifs with the ground truthrather than truly spectrally justified structures. \n\n\n\nSince the MADGEN has uploaded updated benchmark results, incorporating those into the evaluation would help readers understand MS-BART’s gains relative to the other model.", "was_revised": true, "final_justification": "The authors did a great job on molecule generation with the guidance of mass spectra. I would like to recommend to accept this paper.", "rebuttal": "Dear Reviewer gPth,\n\nThank you for recognizing our work and for your insightful comments. We are glad you find that MS-BART enables end-to-end learning by bringing two disparate modalities into a shared vocabulary and can learn robust spectral–structure correspondences before seeing scarce experimental data. Regarding your concerns, we will respond to them one by one as follows.\n\n> W1&Q1: Finetuning relies on MIST-predicted fingerprints; errors or biases in MIST could propagate through the model, but the paper does not quantify how fingerprint noise impacts downstream accuracy.\n\nIn response to Reviewer LqRY (Q1), we have verified that the threshold controlling the activation of the fingerprint is robust and the performance fluctuation is not significant. Regarding the impact of MIST performance on downstream accuracy, in the table responding to Reviewer n2SS (Major concern 1), we found that poor fingerprint prediction reduces the exact match accuracy but does not affect the similarity metric as much. These quantified results demonstrate that **MS-BART is robust to fingerprint noise and is promising for real-world applications**, as similar but not exactly matching structures are still useful for domain experts to narrow down the chemical space [1].\n\n[1] DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectra. ICML2025.\n\n> W2: The unified Transformer and beam search may be resource-intensive; the paper reports relative decoding latency trends but lacks concrete runtime or memory benchmarks for real-time or large-scale deployments.\n\nThank you for pointing this out. We have reported that the average decoding latency on one A800 with 80G memory in a computer cluster is around 1.45s. To facilitate large-scale and real-world deployment, we also tested the model on a consumer-grade RTX 4090 with 24G memory. The average inference time is around 3s, and the memory consumption is around 10GB when processing one spectrum with a beam width set to 100. Although MS-BART uses beam search, the vocabulary size (185) is small, and the total number of parameters is around 104M, so the inference latency and memory consumption is acceptable. At the same time, current LLM inference and serving packages, such as VLLM, also support encoder-decoder models, which would further improve the serving throughput.\n\n\n> W3: The ranking loss favors candidates with higher global Tanimoto scores, but that metric can be dominated by common substructures rather than spectral features. This may encourage the model to pick “average” molecules that share generic motifs with the ground truth rather than truly spectrally justified structures.\n\nWe appreciate this observation. While the Tanimoto scores can assess the similarity between predicted molecules and real molecules, we acknowledge that the potential limitation you mentioned may exist. However, there are no existing metrics more suitable for rewards, as the edit distance is time-consuming. Future work can focus on finding more reasonable rewards to reflect truly spectrally justified structures\n\n> W4: Since the MADGEN has uploaded updated benchmark results, incorporating those into the evaluation would help readers understand MS-BART’s gains relative to the other model.\n\nThank you for the reminder, we have updated the results.\n\n> Q2: Does MS-BART generalize to spectra acquired under different collision energies, adduct types, or instrument platforms?\n\nYes. MS-BART does not process the spectra directly but relies on MIST to predict the fingerprint. After a careful review of MIST [2], we found it highly dependent on proper chemical formula assignments from MS1 precursor masses, which are independent of collision energies, adduct types, and instrument platforms. It should also be noted that MassSpecGym presents this complexity, as each structure has multiple associated spectra (averaging more than 5 per structure) with different collision energies, adduct types, and instrument platforms. MS-BART can handle this complexity well and achieves sub-optimal or even SOTA (Top1 MCES) performance in similarity metrics.\n\n[2] Annotating metabolite mass spectra with domain-inspired chemical formula transformers. Nat. Mach. Intell. 2023."}, {"review_id": "cQkEqZJeyJ", "reviewer": "Reviewer_dSeN", "summary": "This paper introduces a method to translate tandem mass spectrometry data to molecules. The method operates on predicted fingerprint tokens derived from an off-the-shelf model and performs seq-to-seq translation of the fingerprint tokens to SELFIE tokens.\nThey perform 2 stages of training: unsupervised pre-training phase and supervised fine-tuning that additionally performs contrastive learning leveraging Tanimoto similarities.\nAt inference, they perform beam search to produce SELFIE tokens.", "questions": "I would like to understand how significant the results presented in this paper are.\n\n- How well does the model perform on the variant of the MassSpecGym task that provides chemical formulas as input? Is the additional chemical formula information complementary to what the model learns?\n- How does the method compare against library matching approaches?\n- How statistically significant are the ablation and other results?", "limitations": "not discussed", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n- The use of active fingerprints as tokens is an interesting and intuitive tokenization scheme.\n- The contrastive alignment is a nice and intuitive idea to introduce chemical feedback into the model.\n- The authors have shown impressive performance that outperforms existing generative methods on MassSpecGym and NPLIB1 datasets.\n- Furthermore, they ablate the performance of pre-training and contrastive learning objectives.\n\n**Weakness**\n- The results sections lack comparisons to library matching methods discussed in the related works.\n- The comparisons in Table 1, Table 2, and Figure 3 do not have error bars so it is tricky to determine if the ablations show statistically significant results.\n- L283 typo \"similar\"", "was_revised": true, "final_justification": "The authors have addressed my concerns.", "rebuttal": "Dear Reviewer dSeN, \n\nThank you for recognizing our work and the time spent reviewing our paper. We are glad you find the use of active fingerprints as tokens interesting and intuitive, and that further alignment is a nice idea. Regarding your concerns, we will address them one by one as follows.\n\n> W1&Q2: The results sections lack comparisons to library matching methods discussed in the related works.\n\nThis paper aims to discover novel molecules from MS/MS, where library matching methods are not applicable. As we responded to Reviewers LqRY and n2SS, the pretraining and training sets are strictly separate from the test set, so it is obvious that library matching will achieve zero accuracy.\n\n> W2&Q3: The comparisons in Table 1, Table 2, and Figure 3 do not have error bars so it is tricky to determine if the ablations show statistically significant results.\n\nAs we respond to Reviewer LqRY, we adopt re-ranking (described in Lines 215–218) and beam-search multinomial sampling (refer to the Hugging Face text generation documentation), which makes MS-BART demonstrate high confidence in the generated structure. As shown in the table responding to Reviewer LqRY (Q4), MS-BART is not sensitive to temperature, and multiple experiment results are consistent. This high confidence ensures the performance gain mainly comes from different stage training rather than randomness.\n\n> W3: L283 typo \"similar\"\n\nThank you for pointing this out. We have fixed it.\n\n> Q1: How well does the model perform on the variant of the MassSpecGym task that provides chemical formulas as input? Is the additional chemical formula information complementary to what the model learns?\n\nThank you for your suggestions. It should be noted that all recent diffusion-based methods use chemical formulas to determine the atom set and achieve promising results. Additional formulas definitely include important information and are very likely to boost the model's performance. We plan to explore effective ways to incorporate these informational hints into MS-BART in the future work, such as extending the vocabulary with numbers and specific atoms."}, {"review_id": "rSfUT5F2V1", "reviewer": "Reviewer_LqRY", "summary": "The paper proposes MS-BART, a novel cross-modal framework with a multitask training objective that jointly model mass spectra modality and molecular structure modality. To improve MS-BART robustness and generalizability, the authors propose a finetuning stage using experimental data generated by MIST, and further a chemical feedback mechanism. With extensive evaluation, MS-BART show state-of-the-art performance on structure prediction task.", "questions": "I hope the authors could address Q1-Q8 I listed in Strengths And Weaknesses section. If the author could clarify my confusion in the experiment design and conduct some additional analysis to provide more insights, I will be happy to raise my score.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 2, "strengths_and_weaknesses": "**Quality**: The paper is generally of good quality with solid experiment and comparison with baselines, however, the following need further clarification:\n- Q1 in Section 4.3 implementation for binarizing MIST probability, is there any reason the authors choose threshold epsilon=0.2? Have the author evaluated the sensitivity of this threshold hyperparameter?\n- Q2 Could the author provide some error analysis by visualizing how MS-BART in failure case for each of the three stages generated molecule structure compared with the ground truth?\n- Q3 Could the author explain why in Figure 3 middle figure Top-1 accuracy decrease a bit (3.50 --> 3.46) from stage 2 to stage 3?\n- Q4 Given MS-BART's high confidence in next-token predictions as shown in Table 4, is it possible that the model overfit on MassSpecGym? The author \"pretrain MS-BART on a simulated dataset of 4 million fingerprint–molecule pairs\" from MassSpecGym and then evaluate the model on MassSpecGym. Is there some data leakage here or I am no understanding it correctly? It would be appreciated if the author could provide more details regarding how they created simulated dataset containing 4 million fingerprint-molecule pairs and how they ensure that there is no leakage for the test set.\n- Q5 In addition to Q4, suppose the author is doing a clean train validation and test split, they are encouraged to show the structure similarity (e.g. edit distance) between the simulated pre-training dataset, MassSpecGym train, validation and test  and NPLIB1 train, validation and test set.\n- Q6 Table 1 for baseline performance, have the author finetuned MIST on MassSpecGym? It is a bit concerning to see MIST+Neuraldeciper/MSNovelist has non-zero accuracy for NPLIB1 but zero accuracy on MassSpecGym.\n- Q7 It would be great if the author could analyze the model sensitivity of temperature before stage 3 Chemical Feedback to see how stage 3 influence model's confidence.\n- Q8 How sensitive is MS-BART to hyperparameter alpha in equation (5)?\n\n**Clarity**\nThe paper is very well-written. It is well-organized and make it very easy for the reader to follow.\n\n**Novelty & Significance**\nThe paper is the first work applying the pretrain-finetune-alignment paradigm from NLP into mass spectrometry data modeling. However, the model architecture used for each stage is not new, i.e., BART, MIST, and Tanimoto similarity. It is a well-designed application paper, and could be of interest for the computational mass spectrometry analysis, but not very novel in terms of methodology.", "was_revised": true, "final_justification": "I have decided to increase my score to 4, as the author’s rebuttal effectively addressed all of my concerns (Q1–Q8).  I would encourage the authors to include the  error analysis in the camera-ready version to further improve transparency of the model performance.", "rebuttal": "Dear Reviewer LqRY,\n\nThank you for your insightful comments and time spent reviewing our paper. We are glad you found our paper well-written and generally of good quality. Regarding the novelty, as you mentioned, while every component is not a new method, incorporating them together and being **the first work to apply the pretrain-finetune-alignment paradigm from NLP to mass spectrometry data modeling in an end-to-end way** is indeed novel. We will respond to your questions one by one as follows.\n\n> Q1: Ablation of threshold epsilon choice.\n\nWe first choose the threshold epsilon=0.2 empirically, as we have checked the probability distribution and found most probabilities are smaller than 0.3. A big threshold may cause information loss, while a small threshold would introduce noise, so we choose epsilon=0.2 for a balance empirically and achieve excellent performance, surpassing the SOTA.\n\nHowever, as Reviewer n2SS pointed out the potential data leakage, we replicate the experiment with clean data and choose epsilon by evaluating on the val set, selecting the best threshold based on the highest Tanimoto similarity.\n\n| epsilon | 0.10 | 0.11 | 0.12 | 0.13 | 0.14 | 0.15 | 0.16 | 0.17 | 0.18 | 0.19 | 0.20 |\n|----------|------|------|------|------|------|------|------|------|------|------|------|\n| Top1 Tanimoto | 0.1666 | **0.1678** | 0.1640 | 0.1651 | 0.1660 | 0.1660 | 0.1654 | 0.1643 | 0.1651 | 0.1649 | 0.1636 |\n\nWe chose epsilon=0.11 for MassSpecGym. To evaluate the sensitivity of the hyperparameter choice to the final result, we also selected epsilon=0.15 (intermediate validation Tanimoto) and epsilon=0.20 (the worst validation Tanimoto) for comparison and presented the results as follows.\n\n| Model   | Top1 Accuracy | Top1 MCES | Top1 Tanimoto | Top10 Accuracy| Top10 MCES | Top10 Tanimoto |\n|-------   |-------|------|---------|--------|----|----------|\n| MS-BART(epsilon=0.15)  | 1.20%(0.12%)   | 16.88(24.54)  | 0.23(0.19)  | 1.20%(0.95%)   | 15.45(18.24)  | 0.28(0.27)  |\n| MS-BART(epsilon=0.20)  | 1.19%(0.00%)   | 17.27(25.62)  | 0.23(0.19)  | 1.20%(0.73%)   | 15.75(18.46)  | 0.28(0.26)  |\n| MS-BART(epsilon=0.11)  | 1.07%(0.08%)   | 16.47(23.00)  | 0.23(0.20)  | 1.11%(0.62%)   | 15.12(17.41)  | 0.28(0.27)  |\n\nThe results in parentheses indicate results that have not undergone re-rank (Line 215-218), and will also be reported in the later table.\nIt is obvious that the re-rank by formula match and generative probability significantly boosts the final performance. As for the threshold sensitivity, there is no significant difference between the epsilons with the same rank post-processing.\n\n> Q2: Could the author provide some error analysis by visualizing how MS-BART in failure case for each of the three stages generated molecule structure compared with the ground truth?\n\nDue to the NeurIPS rebuttal format restrictions, we cannot provide visualized error analysis in the rebuttal. We appreciate your suggestions and will add error analysis in the future version.\n\n> Q3: Could the author explain why in Figure 3 middle figure Top-1 accuracy decrease a bit (3.50 --> 3.46) from stage 2 to stage 3?\n\nThank you for pointing this out. A possible explanation is that the alignment is tuned with Tanimoto similarity rather than being based on an exact match. The rank loss aims to align the predicted structure's Tanimoto similarity with the gold structure to the model's generative probability, so it is difficult to control the accuracy. As we have replicated the experiments, we updated the figure data in the table below.\n\n| Model   | Top1 Accuracy | Top1 MCES | Top1 Tanimoto | Top10 Accuracy| Top10 MCES | Top10 Tanimoto |\n|-------  |---------|-----|--------|---------|-------|----------|\n| Pretrain  | 0.00%(0.00%)   | 23.32(30.22)  | 0.18(0.18)   | 0.01%(0.01%)   | 21.67(24.66)  | 0.21(0.21)  |\n| Pretrain-FT   | 1.07%(0.00%)   | 17.17(26.30)  | 0.22(0.17)   | 1.12%(0.59%)   | 15.51(18.54)  | 0.27(0.26)  |\n| Pretrain-FT-Rank  | 1.07%(0.08%)   | 16.47(23.00)  | 0.23(0.20)   | 1.11%(0.62%)   | 15.12(17.41)  | 0.28(0.27)  |\n\nWe can observe obvious benefits between each stage with or without re-ranking and demonstrate the effectiveness of MS-BART. It is more convincing to observe the top-1 similarity improvement, which can directly show the payoff of the contrastive alignment since we only generate three candidate structures for alignment in this stage.\n\n> Q4:  Potential overfit or data leakage.\n\nThe results in Table 4 are the evaluation results after re-ranking (described in Line 215-218). Re-ranking can improve the evaluation stability. As for data leakage, we use the 4M pretraining dataset provided by the MassSpecGym official data, which has been refined by excluding any molecules with an MCES distance of less than two from any molecule in the test fold of MassSpecGym by the authors, ensuring there is no data leakage. Since we have replicated the experiments, we reevaluate the results in Table 4 with and without re-ranking as follows.\n\n| Model    | Top1 Accuracy | Top1 MCES | Top1 Tanimoto | Top10 Accuracy| Top10 MCES | Top10 Tanimoto |\n|------- |----------|------|--------|---------|------|---------|\n| MS-BART(t=0.2) | 1.07%(0.08%)  | 16.47(23.00) | 0.23(0.20) | 1.11%(0.62%) | 15.12(17.41) | 0.28(0.27)  |\n| MS-BART(t=0.4) | 1.07%(0.08%)  | 16.47(23.00) | 0.23(0.20) | 1.11%(0.62%) | 15.12(17.41) | 0.28(0.27)  |\n| MS-BART(t=0.8) | 1.07%(0.08%)  | 16.47(22.96) | 0.23(0.20) | 1.11%(0.62%) | 15.11(17.40) | 0.28(0.27)  |\n\nUpon re-evaluation, it can also be observed that our model is not sensitive to temperature changes, even for the top1 result without re-ranking. The main reason can be attributed to the beam-search multinomial sampling (refer to the Hugging Face text generation documentation), which combines beam search (beam width=100) and stochastic sampling, and the SELFIES vocabulary size of 185 (Line 139), resulting in this high confidence.\n\n> Q5: In addition to Q4, suppose the author is doing a clean train validation and test split, they are encouraged to show the structure similarity (e.g. edit distance) between the simulated pre-training dataset, MassSpecGym train, validation and test and NPLIB1 train, validation and test set.\n\nIn response to Q4, there is no data leakage between the pretraining dataset and the MassSpecGym test set, so further structure similarity analysis is not necessary.\nAs for NPLIB1, there is some overlap (7 cases) between the pretraining dataset and the test set. Since MCES is time-consuming, we demonstrate the structure similarity using Tanimoto similarity. More details are provided in the response to Reviewer n2SS (Major concern 2).\n\n\n> Q6:  Table 1 for baseline performance, have the author finetuned MIST on MassSpecGym? It is a bit concerning to see MIST+Neuraldeciper/MSNovelist has non-zero accuracy for NPLIB1 but zero accuracy on MassSpecGym.\n\nThe baseline results are reproduced from MassSpecGym and DiffMS as we state in the Table 1 caption. After a careful review of the DiffMS paper, MIST+NeuralDecoder/MSNovelist undergoes the same pretraining and finetuning, aligning with DiffMS.\n\n> Q7:  It would be great if the author could analyze the model sensitivity of temperature before stage 3 Chemical Feedback to see how stage 3 influence model's confidence.\n\nThank you for your suggestion. We evaluated the MS-BART after stage 2 fine-tuning and showed the results as follows.\n\n| temperature    | Top1 Accuracy | Top1 MCES | Top1 Tanimoto | Top10 Accuracy| Top10 MCES | Top10 Tanimoto |\n|-------            |---------------|-----------|---------------|---------------|------------|----------------|\n| t=0.2    | 1.07%(0.00%)  | 17.16(26.30)  | 0.22(0.17)   | 1.12%(0.59%)   | 15.51(18.54)   | 0.27(0.26) |\n| t=0.4    | 1.07%(0.00%)  | 17.17(26.30)  | 0.22(0.17)   | 1.12%(0.59%)   | 15.51(18.54)   | 0.27(0.26) |\n| t=0.8    | 1.07%(0.00%)  | 17.16(26.28)  | 0.22(0.17)   | 1.12%(0.59%)   | 15.50(18.54)   | 0.27(0.26) |\n\nIt is not surprising to find our model also remains stable before stage 3, mainly due to the decoding strategy in response to Q4. This is also strong evidence demonstrating the effectiveness of MS-BART, as we can observe the similarity metric improving between stage 2 and stage 3 (evaluation results shown in Q4) under different temperatures.\n\n> Q8: How sensitive is MS-BART to hyperparameter alpha in equation (5)?\n\nThank you for your suggestion. Due to the large size of MassSpecGym, we evaluated the sensitivity using NPLIB1. We conducted experiments on the clean pretraining data (4M_leq50, details are provided in our response to Reviewer n2SS, Major Concern 2). We tested alpha values in {1, 3, 5} and present the results below.\n\n| alpha             | Top1 Accuracy | Top1 MCES | Top1 Tanimoto | Top10 Accuracy| Top10 MCES | Top10 Tanimoto |\n|-------            |---------------|-----------|---------------|---------------|------------|----------------|\n| alpha=1           | 7.08%    | 10.56  | 0.44  | 12.45%  | 9.10  | 0.52     |\n| alpha=3           | 7.20%    | 9.53   | 0.44  | 10.50%  | 8.21  | 0.51     |\n| alpha=5           | 7.45%    | 9.62   | 0.44  | 10.99%  | 8.27  | 0.51     |\n\nThe final result we report is alpha=3, as it performs the best in the top1 Tanimoto on the validation set. Although there are some fluctuations between different alpha settings, it is clear that MS-BART is not very sensitive to the alpha selection.\n\n---\n\nFinally, thank you again for your insightful and constructive comments. The idea of active fingerprints as tokens and the unified modeling of mass spectra and molecules provides a new paradigm for structure elucidation, as most recent methods are based on diffusion. This paper provides **strong insights on how to incorporate MS spectra into LLMs rather than using the directly noisy number sequence**, opening a new strategy to align the spectra modality with language models. This would inspire many innovative ideas, such as reasoning models for explainable strcuture elucidation."}, {"review_id": "qCS8M8Z36a", "reviewer": "Reviewer_n2SS", "summary": "The paper presents MS-BART — a BART-based model for predicting SELFIES-encoded molecular structures from Morgan fingerprints with the application to annotate tandem mass spectra. The method is trained in three stages:  \n(i) pretraining on SELFIES–fingerprint pairs from a dataset of molecular structures,  \n(ii) fine-tuning on a dataset of SELFIES–fingerprint pairs from a mass spectral library, where the fingerprints are predicted using a pretrained MIST model,  \n(iii) additional contrastive alignment based on Tanimoto similarities of sampled fingerprints to improve fingerprint prediction.\n\nThe method is overall solid and clearly presented. The authors also provide ablation studies and qualitative examples showing the benefits of multiple training stages and several pretraining tasks during stage (i). However, the experimental results have some issues.", "questions": "- Would MS-BART have a state-of-the-art performance under corrected evaluation protocols? My current score is 3: Borderline reject. However, if the authors address my major concerns and demonstrate that the method indeed achieves state-of-the-art results, I will raise my score to 5: Accept.\n- MIST does not appear to have a particularly excellent performance on MassSpecGym. Can MS-BART effectively handle predicted fingerprints which may be low-quality?\n- Did the authors experiment with adding chemical formula tokens to the input/output sequence of MS-BART?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Below, I summarize the strengths of the paper, as well as, my major concerns and minor comments.\n## Strengths\n\n### Quality\n\nThe method is well structured and technically sound, with thoughtful integration of generative modeling, fingerprint conditioning, and contrastive learning. The three-stage training pipeline is clearly explained, and ablation studies support the benefit of each stage.\n### Clarity\n\nThe paper overal easy to follow. The description of the model architecture and training stages is well organized.\n\n### Significance\n\nThe task of molecular structure elucidation from tandem mass spectra is highly relevant to the metabolomics and computational chemistry communities. The proposed method addresses this problem with a combination of modern tools and training strategies that, if properly validated, could set a new performance baseline on public benchmarks.\n\n### Originality\n\nThe work is original in its integration of BART-based decoding with fingerprint supervision and contrastive alignment. While prior models have used generative approaches and fingerprint conditioning, this work offers a unified and scalable framework that extends current techniques.\n\n## Weaknesses\n\n### Major concerns\n\n- Current experimental results appear to suffer from data leakage:\n\n    - MassSpecGym. Could the authors clarify what data was used to train the MIST fingerprint predictor or what checkpoint was used for each benchmark (MassSpecGym and NPLIB1)? If MIST was trained on NPLIB1 data (i.e., using the checkpoint from the official repository), the reported results on MassSpecGym are not valid. In this case, MIST’s training data may contain entries from the MassSpecGym test folds, since both datasets include spectra from mass spectral libraries such as MoNA and GNPS. This would mean that test results on MassSpecGym are indirectly based on training the fingerprint predictor on the same data. Additionally, the InChIKey-based split of NPLIB1 can promote overfitting, further increasing the risk of data leakage. Suggestion: Please recompute the MassSpecGym results using a MIST model retrained on MassSpecGym or the pre-trained checkpoint available at https://zenodo.org/records/11580401.\n\n    - NPLIB1. The authors use the 4M dataset from MassSpecGym for model pretraining and show a clear benefit via ablation studies. They note that this dataset does not contain any molecules with MCES distance < 2 to any test molecules from MassSpecGym. However, this does not hold for NPLIB1. Suggestion: Please report experimental results on NPLIB1 with similar structures excluded from the pretraining data. This does not have to rely on computationally expensive MCES filtering; a Tanimoto-based filter would suffice. Without this, test fold results may be based on the same training examples up to a difference in the groundtruth and predicted fingerprints.\n\n- Top-k accuracy definition. The current definition of Top-k accuracy (Line 202) appears inconsistent with prior work, such as NPLIB1 and MassSpecGym, which use the first 14 characters of InChIKeys (i.e., 2D InChIKeys) for exact matching rather than SELFIES strings. Suggestion: Please recompute the metric using 2D InChIKeys to ensure consistency and comparability with prior studies.\n\n### Minor comments\n\n- Lines 3–6; Lines 37–38. Please note that there is a prior work applying large-scale pretraining on mass spectra. Please include these papers when discussing related works:  \n  - https://www.nature.com/articles/s41587-025-02663-3 (self-supervised pretraining in metabolomics)  \n  - https://www.nature.com/articles/s41592-022-01496-1 (contrastive pretraining in proteomics)\n\n- Line 69. Please clarify why mass spectra are described as discrete. Each peak corresponds to a 2D continuous data point (m/z, intensity).\n\n- Line 98. Typo: RetorBridge → RetroBridge\n\n- Line 102. Please mention that DiffMS uses the chemical formula as input. This is an important practical detail, as computing the formula is often computationally expensive.\n\n- Line 125. Please note that bits in hashed Morgan fingerprints are not associated with specific substructures.\n\n- Figure 2. In the caption, please clarify what is meant by “reliably computed fingerprints.” While MIST is a strong open-source fingerprint predictor, it is difficult to assess reliability without further explanation or supporting statistics.\n\n- Lines 143; 167; 316. I recommend explicitly stating that tokenization of mass spectra is done via MIST. As currently written, this could be misunderstood as simple spectrum binning or a similar procedure.\n\n- Line 181. Typo: loss[ → loss [\n\n- Line 186. Please explain what is meant by “MS-BART can assign a balanced probability mass across the whole sequence.” This statement is unclear.\n\n- Line 213. Please clarify why “generated molecules are not repeated.”\n\n- Lines 245–247. Please note that a Tanimoto threshold of >0.75 is generally interpreted as a “close match” in the literature.\n\n- Lines 253–258, 293–296, 307–308. Consider moving these details to “datasets” or “implementation” sections.\n\n- Line 283. Typo: train → trained\n\n- Line 290. Typo: slected → selected\n\n- Line 313. Please specify the number of parameters in the MS-BART model.", "was_revised": true, "final_justification": "My concerns regarding data leakage were addressed by the authors, I am raising my score to 5.", "rebuttal": "Dear Reviewer n2SS,\n\nThank you for your insightful comments, suggestions, and time spent reviewing our work. We are glad that you find our work overall solid and technically sound, offering a unified and scalable framework that extends current techniques. Regarding your concerns, we will address them in detail one by one as follows:\n\n> Major Concern 1: Potential data leakage on MassSpecGym\n\nThank you for pointing this out.\nFor the two benchmarks (MassSpecGym and NPLIB1), we used the official MIST weights pretrained on CANOPUS, downloaded from the official repository (`mist_fp_canopus_pretrain.ckpt`).\nTo verify potential data leakage, we downloaded the CANOPUS training set and computed the structural overlap with the MassSpecGym test fold, finding an 18% exact match (exact matches / total test cases). We agree with your assessment that the originally reported MassSpecGym results are invalid.\n\nWe appreciate your suggestion and the provided checkpoints. However, we find that the provided checkpoints may not align with the official weights, as the model sizes differ (103.4 MB vs. 60.8 MB). We then trained our own MIST model under three settings:\n\n1) CANOPUS_filter. We filtered the CANOPUS data (train/val/test) and the augmentation data (simulated spectra from a forward model) provided by the MIST repository by removing structures with high similarity (Tanimoto similarity > 0.75, as suggested below) to the test and validation sets in MassSpecGym. A total of 703 structures were excluded from the CANOPUS data and 559 structures from the augmentation data. The model was trained on the CANOPUS training data for 0.69 hours on an RTX 4090 GPU, achieving the best checkpoint with a test loss of 0.298 at epoch 138.\n2) CANOPUS_filter_Contrastive. Using the same CANOPUS training data as in CANOPUS_filter and the cleaned augmentation data, we trained the model for 2.81 hours on an RTX 4090 GPU, obtaining the best checkpoint with a test loss of 1.328 at epoch 46.\n3) MassSpecGym. We trained MIST on MassSpecGym training fold using one RTX 4090 GPU. After 1.8 hours of training, we obtained the best checkpoint with a test loss of 0.487 at epoch 22. The test loss was significantly higher than that of CANOPUS_filter, and since there are many hyperparameters in MIST training, this may indicate insufficient optimization and a suboptimal fingerprint model.\n\nAfter obtaining the uncontaminated MIST model, we followed MS-BART's three-stage training pipeline and evaluated its performance on MassSpecGym. The results are presented as follows:\n\n| Model    | Top1 Accuracy | Top1 MCES | Top1 Tanimoto | Top10 Accuracy| Top10 MCES | Top10 Tanimoto |\n|-------   |-------|-----|--------|---------|-------|--------|\n| ~~MS-BART(Original)~~  | ~~3.46%~~ | ~~16.29~~ | ~~0.30~~ | ~~4.49%~~ | ~~14.89~~ | ~~0.35~~ |\n| Spec2Mol | 0.00%  | 37.76 | 0.12 | 0.00%  | 29.40 | 0.16   |\n| MADGEN | 1.31%  | 27.47 | 0.20 | 1.54%  | 16.84 | 0.26   |\n| DiffMS | **2.30%** | 18.45     | **0.28** | **4.25%** | **14.73** | **0.39** |\n| MS-BART(CANOPUS_filter)   | 1.07%  | 16.47  | 0.23  | 1.11%   | 15.12  | 0.28  |\n| MS-BART(CANOPUS_filter_Contrastive) | 0.97%  | 16.37  | 0.23  | 1.22%  | 14.81  | 0.29  |\n| MS-BART(MassSpecGym)  | 0.08%   | **16.17** | 0.21  | 0.10%   | 14.93   | 0.27   |\n| MS-BART(Gold Fingerprint) | 47.56% | 3.26   | 0.85 | 64.62%  | 2.02  | 0.93  |\n\nIt is obvious that MS-BART suffers a performance degradation after data cleaning. However, the performance of our model is still **acceptable, achieving sub-optimal or even SOTA (Top1 MCES) in similarity metrics** while being significantly faster than DiffMS. The average inference time for a spectrum on one RTX4090 is around 3s for MS-BART, **which is 53x faster than DiffMS's approximately 160s**. \nIt should also be noted that the performance degradation is attributed to the MIST model's bad performance. The fact that MS-BART (CANOPUS_filter_Contrastive) slightly outperforms MS-BART (CANOPUS_filter) demonstrates this, and the poor performance of MS-BART (MassSpecGym) further supports this observation.\nFinally, if the fingerprints are correctly predicted (given the gold fingerprint), MS-BART shows excellent performance, demonstrating great potential for structure elucidation with the rapid evolution of fingerprint prediction (such as DreaMS, as mentioned in Minor comments 1)\n\nMoreover, MS-BART represents **the pioneering work** in applying the NLP paradigm to structure elucidation. It can be **readily scaled up with additional pretraining data and larger model parameters** (such as at the Qwen/LLaMa level) to further boost performance.\n\n\n> Major Concern 2: Potential data leakage on NPLIB1\n\nWe agree with you and thank you for your suggestion. We checked the structural similarity between the NPLIB1 test set and the pretraining dataset. The exact structure match is 0.85% (exact matches / total test cases, 7 cases). We also calculated the maximum Tanimoto similarity (with the NPLIB1 test set) distribution of the 4M pretraining data as follows:\n\n| Interval   | 0.0-0.1 | 0.1-0.2 | 0.2-0.3 | 0.3-0.4 | 0.4-0.5 | 0.5-0.6 | 0.6-0.7 | 0.7-0.8 | 0.8-0.9 | 0.9-1.0 |\n|-----  |----|-------|------|-------|-------|-----|-----|------|----|------|\n| Proportion | 0.09%   | 8.40%   | 55.85%  | 21.58%  | 6.79%   | 3.79%   | 1.84%   | 1.20%   | 0.40%   | 0.06%   |\n\nThere is indeed some possible data leakage, but **most (>98%) of the pretraining data are not similar** (Tanimoto Similarity < 0.7) to the test set. We also appreciate your suggestion and pretrained again on two clean pretraining sets. The first set filters out similar structures with a Tanimoto Similarity > 0.5 and is denoted as 4M_leq5. The second set filters out similar structures with a Tanimoto Similarity > 0.75 and is denoted as 4M_leq75. After fine-tuning and alignment, we obtained the new results as follows.\n\n| Model     | Top1 Accuracy | Top1 MCES | Top1 Tanimoto | Top10 Accuracy| Top10 MCES | Top10 Tanimoto |\n|------- |---------|--------|--------|---------|---------|---------|\n| ~~MS-BART(Original)~~    | ~~11.11%~~ | ~~8.58~~  | ~~0.46~~ | ~~16.97%~~ | ~~7.29~~ | ~~0.54~~ |\n| Spec2Mol | 0.00%     | 27.82     | 0.12     | 0.00%     | 23.13     | 0.16   |\n| MADGEN        | 2.10%      | 20.56     | 0.22     | 2.39%      | 12.69    | 0.27     |\n| DiffMS    | **8.34%**  | 11.95     | 0.35     | **15.44%**  | 9.23     | 0.47     |\n| MS-BART(clean, 4M_leq50)  | 7.20%      | **9.53**  | **0.44** | 10.5%      | **8.21** | **0.51** |\n| MS-BART(clean, 4M_leq75)    | 7.94%      | 10.37     | 0.44     | 11.97%     | 8.91     | 0.51     |\n\nThe results demonstrate that MS-BART still maintains competitive performance, **outperforming baseline models in all similarity metrics at a strictly-separated pretraining dataset**, although the exact match accuracy suffers performance degradation.\nHowever, we are wondering if this is really a data leakage issue, as this is inevitable with data and parameter scaling. We only pretrain on the structure with computed fingerprints, not the MIST-generated fingerprints.\nIn contrast, we think this is a strong evidence showing the pretraining of MS-BART is effective, as MS-BART can learn meaningful molecule structures in pretraining and generalize to MIST-predicted fingerprints through finetuning and alignment. We hope to discuss this further with you during the discussion period.\n\n> Major Concern 3: Top-kaccuracy definition\n\nWe generate the predicted molecule in SELFIES. Since SELFIES representations are not unique, we first use the `selfies` library to transform the SELFIES into SMILES and then use `RDKit` to obtain the corresponding structure, followed by generating the canonical SMILES using `Chem.MolToSmiles(mol, canonical=True)`. We appreciate your suggestion and have checked DiffMS, where they use `Chem.MolToInchi(mol)` for structure matching. Both methods correspond to a unique structure, ensuring a fair comparison. We have also validated this through experiments and found that the accuracy remains consistent.\n\n---\n\nResponses to minor comments are omitted due to the character limit. We will share them during the discussion period if requested and permitted.\n\n---\n\n> Q1: Performance.\n\nMS-BART still achieves the SOTA performance on NPLIB1 in terms of MCES and Tanimoto under corrected evaluation protocols, while on MassSpecGym, we also achieve SOTA on Top1 MCES metric. However, there is indeed a performance degradation, especially on MassSpecGym. We identify two main reasons for this:\n1) MassSpecGym presents greater complexity, as each structure has multiple associated spectra (average > 5 per structure), which introduces additional noise for our model. Additionally, the use of a different pretraining dataset (DiffMS, which trains on self-collected data and removes exact-match molecules) may also lead to an unfair comparison.\n2) The most important reason may be that the MIST model performs poorly on MassSpecGym. Training a robust MIST model on this specific dataset requires extensive hyperparameter tuning.\n\n> Q2: Low-quality fingerprints.\n\nMS-BART exhibits a degree of robustness in handling low-quality predicted fingerprints, and the overall performance would get worse if the quality of the predicted fingerprints gets lower, as demonstrated in Major Concern 1. \n\n> Q3: Adding chemical formula.\n\nThank you for your suggestions. It should be noted that all recent diffusion-based methods use chemical formulas to determine the atom set and achieve promising results. Additional formulas definitely include important information and are very likely to boost the model's performance. We plan to explore effective ways to incorporate these informational hints into MS-BART in the future work, such as extending the vocabulary with numbers and specific atoms.\n\n---\n\nFinally, we sincerely appreciate your time and valuable suggestions, which have helped enhance and refine our work. We welcome further discussion during the rebuttal period."}]

Author rebuttal:

Meta-review: (a) Summarize the scientific claims and findings of the paper based on your own reading and characterizations from the reviewers.
The paper introduces MS-BART, a unified modeling framework for elucidating molecular structures from MS data. The core innovation is the mapping of both mass spectra (represented as molecular fingerprints) and molecular structures (represented as SELFIES strings) into a shared token vocabulary. This enables the application of the pretraining-finetuning-alignment paradigm, common in Natural Language Processing, to the MS domain. The authors claim that MS-BART achieves state-of-the-art or competitive performance on the MassSpecGym and NPLIB1 benchmarks.


 
(b) What are the strengths of the paper?

The reviewers found the paper to be well-organized and clearly written. The primary strength lies in its novel application of the pretrain-finetune-alignment paradigm from NLP to mass spectrometry, addressing the challenging problem of data scarcity. The concept of a unified vocabulary for both spectra and molecules was highlighted as "conceptually elegant" and interesting, enabling an end-to-end, scalable training paradigm. The model demonstrates strong empirical results, outperforming existing generative methods on public benchmarks. The thoughtful integration of generative modeling, fingerprint conditioning, and contrastive learning was also praised as technically sound.


 
(c) What are the weaknesses of the paper? What might be missing in the submission?

The most significant weakness identified during the initial review was a potential for data leakage in the experimental validation. The MIST model used for fingerprint prediction was likely trained on data that overlapped with the test sets, and the pretraining data was not adequately filtered for the NPLIB1 benchmark. Other weaknesses included a lack of fine-grained ablation studies for each pretraining task (huv7) , the fact that the contribution is more of an adaptation of existing methods to a new domain rather than fundamentally new techniques (huv7) , and that the absolute accuracy on MassSpecGym remained low, limiting immediate real-world applicability (huv7). The reliance on the external MIST model for fingerprint generation was also noted as a potential bottleneck where errors could propagate.
 
(d) Provide the most important reasons for your decision to accept.

The decision to accept this paper is primarily based on the authors' thorough and transparent response to the critical issue of data leakage. They conducted extensive re-evaluations with a retrained MIST model on cleaned data, which, despite a drop in performance, still demonstrated competitive results against state-of-the-art methods. This effort successfully addressed the reviewers' main concerns and validated the robustness of their framework. The paper introduces a novel and promising direction by applying a large language model paradigm to structure elucidation, and its unified vocabulary is a significant conceptual contribution. Furthermore, the model is orders of magnitude faster in inference than competing diffusion-based methods, which is a significant practical advantage. The work opens new avenues for research in this area and is a valuable contribution to the community. All reviewers were satisfied with the rebuttal and raised their scores to "accept".   

 
(e) Summarize the discussion and changes during the rebuttal period.

The rebuttal period was highly productive and pivotal. Key points raised and addressed were:

1.	Data Leakage (n2SS): This was the most critical concern. The authors acknowledged the issue, retrained the MIST fingerprint predictor on properly filtered data, and reran all experiments for both MassSpecGym and NPLIB1 benchmarks. They presented new results tables, demonstrating that while performance on exact match accuracy decreased, the model remained competitive on similarity metrics. This comprehensive response satisfied the reviewer, who then raised their score to "Accept".  

2.	Impact of MIST Fingerprint Quality (LqRY, n2SS, huv7): In response to questions about robustness to MIST's prediction errors, the authors conducted new ablation studies. They showed the model is not overly sensitive to the fingerprint binarization threshold and that performance with "gold" (ground-truth) fingerprints is excellent, confirming that fingerprint prediction is the main bottleneck. This addressed the reviewers' concerns.

3.	Lack of Fine-grained Ablation (huv7): The authors performed a more detailed ablation study on the individual pretraining tasks (translation, hybrid denoising), providing better insight into their contributions. This satisfied the reviewer. 

4.	Top-k Accuracy Definition (n2SS): The reviewer pointed out the standard practice of using 2D InChIKeys for matching. The authors re-evaluated their results using this metric, discussed the implications, and agreed to add it to the final version for a fair comparison. This resolved the issue.
Decision: Accept (poster)