SpectraLLM: Uncovering the Ability of LLMs for Molecule Structure Elucidation from Multi-Spectra
Authors: Yunyue Su
Jiahui Chen
Zao Jiang
Zhenyi Zhong
Liang Wang
Qiang Liu
Zhaoxiang Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: structure elucidation
spectral
molecular
large language model
domain specific training
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
TL;DR: We introduce SpectraLLM, a large language model that unifies multiple spectroscopic modalities into a shared semantic space to jointly infer molecular structures.

Abstract: Automated molecular structure elucidation remains challenging, as existing approaches often depend on pre-compiled databases or restrict themselves to single spectroscopic modalities. Here we introduce **SpectraLLM**, a large language model that performs end-to-end structure prediction by reasoning over one or multiple spectra. Unlike conventional spectrum-to-structure pipelines, SpectraLLM represents both continuous (IR, Raman, UV-Vis, NMR) and discrete (MS) modalities in a shared language space, enabling it to capture substructural patterns that are complementary across different spectral types. We pretrain and fine-tune the model on small-molecule domains and evaluate it on four public benchmark datasets. SpectraLLM achieves state-of-the-art performance, substantially surpassing single-modality baselines. Moreover, it demonstrates strong robustness in unimodal settings and further improves prediction accuracy when jointly reasoning over diverse spectra, establishing a scalable paradigm for language-based spectroscopic analysis.

Published as a conference paper ICLR 2026
SPECTRALLM: UNCOVERING THE ABILITY OF LLMs FOR MOLECULE STRUCTURE ELUCIDATION FROM MULTI-
SPECTRAL
Yunyue Su Jiahui Chen Zao Jiang
Zhaoxiang Zhang (NLPR), Institute of Automation, Chinese Academy of Sciences (CASIA)
Zhenyi Zhong' Liang Wang Qiang Liu
New Laboratory of Pattern Recognition
'The Hong Kong Polytechnic University
Tianjin University Peiyangyuan Campus
ABSTRACT
Automated molecular structure elucidation remains challenging, as existing approaches often depend on pre-compiled databases or restrict themselves to single spectroscopic modalities. Here we introduce SpectraLLM, a large language model that performs end- to-end structure prediction by reasoning over tional spectrum-to-ssrructuree pipelines, Spectral.LM represents both continuous (IR, Ra- man, UV-Vis, NMR) and discrete (MS) modalities in shared language space, enabling it to capture substructural patterns that are complementary across different spectral types. We pretrain and fine-tune the model on small-molecule domains and evaluate on four public benchmark datasets. SpectraL achieves state-of-the-art performance, substan- tially surpassing
multiple spectra. Unlike conven-
baselines. Moreover, demonstrates strong robustness in unimodal settings and further improves prediction accuracy when jointly reasoning over diverse spectra, establishing scalable paradigm for language- based spectroscopic analy- sis. Code is available at httpss//gthabb.oo//PPPigiii/eeee
INTRODUCTION
Structure elucidation is fundamental across chemistry, biology, and materials science, enabling the determi- nation of molecular and crystal structures (Shklover et al. [021] Ali et al. 2022: Beermann & Brockamp
2005
Nie-lee Materlik 1995 Ishchenko al.] 2011 Filipponi et al. 1995| Le et al. 20244] Fang
et al. 2024 Ju et al. (225)). A wide array of spectroscopic techniques, including infrared (IR), Raman, ultravioletvisiible (UV,Vis), nuclear magnetic resonance (NMR), and mass spectrometry (MS), have become indispensable tools for this purpose, each probing distinct physicochemical properties through interactions with electromagnetic radiation or ionization (Stuart
2004.
Smith 2018 Butler et al.] 2016 Perkampus Quinn et al. 2020). Table summarizes their
2013
Picollo et al.) 2019, Bovey al.) 1988 James 2012:
detection mechanisms, sensitivities, and characteristic outputs. Taken together, these modalities provide
complementary molecular insights, and human experts routinely rely on their joint interpretation resolve
structural ambiguities.
Machine learning has emerged as a promising approach to automate spectrum-to-strrctum inference, show- ing effectiveness in functional group recognition (Fine et al., 2020 Judge et al. 22008] Klawun & Wilkins 1996 Fessenden & Györgyi 1991. Hemmer & Gasteiger 2000) and molecular property regression from
"Yunyue Su and Jiahui Chen contributed equally to this work.
Corresponding author: qiang liu @ nipria.ac.cn

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Published as a conference paper ICLR 2026
Table I: Overview of spectroscopic modalities.
Modality
Detection Principle
Structural Sensitivity
Common Output Features
Functional groups with dipole changes
IR
Molecular vibration absorption
Peak positions, intensities
Symmetric bonds and polarizahiiity changes Conjugated systems, and n x7 transitions
Raman
Inelastic light scattering
Shifted peak patterns
UV-Vis
Electronic transitions
Absorbance wavelengths, spectra
Carbon backbone structure. hybridization
ISC NMR
Carbon- 13 spin resonance
Chemical shifts of 15C nuclei
'H NMR
Proton spin resonance (IH)
Proton environments, local chemical shifts
Peak multiplicity, integration
HSQC NMR
MCCHH H correlation spectroseopy
Coupling between proton and carbon atoms
2D cross peaks ( H*HH pairs)
Molecular weight and fragmentation patterns
Mass (MS)
Mass to charge ratio (m/z)
m// peaks
spectral data (Wang et al.) 2022, Chen et al. 2023). Early work has primarily adopted conventional archi-
tectures such as convolutional neural networks (LeCun et al. 2002 Li et al. 2021 | O'shea Nash 2015)
and Transformers (Vaswani et al., 2017: Han et al.) 2022 2021). framing spectral analysis as sequence-
to to-sequence task. Examples include Spec2Mol (Litsa et al. (2023), which encodes MS/MS spectra using a
CNN and decodes SMILES via RNN, well subsequent efforts leveraging Transformer decoders (Hu
let al. 2023; Kanakala et al. 2024). Other extensions combine molecular formulas with spectra (Alberts
et al. 2024a). achieve reconstruction from IR alone without expert rules (French et al., (025). Recently,
diffusion-based approaches such as DifIMS (Bohde et al., 2025) have framed inverse mass spectrometry conditional molecular generation task, improving both diversity and synthesizability. Despite these advances, most existing models remain confined to single modalities and lack the flexibility to integrate heterogeneous spectroscopic evidence.
key limitation of these approaches lies in their architectural rigidity: spectra are treated as fixed numerical features, which hinders the incorporation of additional modalities contextual information such exper- imental conditions and annotations. In contrast, large language models (LLMs) offer a natural foundation for this problem. By transforming spectral peaks into textual representations that encode physical attributes, LLMS can unify heterogeneous modalities within a shared semantic space, enabling joint reasoning over diverse spectroscopic signals. This paradigm leverages LLMs' strengths in symbolic reasoning, contextu- alization, and compositional generalization, which conventional architectures cannot easily replicate, which motivates our design of SpectraLLM.
In this study, we present SpectraLLM, large language model for molecular structure elucidation from spec- troscopic data. Unlike previous modality-specifte approaches, SpectraLLM employs a unified language- based architecture that accepts structured descriptions of one or more spectral modalities and directly in- fers molecular structures via natural language reasoning. Although we initially experimented with vision- language models (VLMs), their performance was not as good as the language-centered approaches (Ap- pendix AA which is likely due to the cross-modal encoding artifacts. By leveraging LoRA- adapta- tion on top of frozen foundation model, SpectraLLM aligns spectral features with linguistic priors while maintaining scalability. Our systematic evaluation across four public benchmark datasets demonstrates that SpM achieves state-of-ther the-art performance, outperforming both traditional pipelines and recent mul- timodal transformer-basee baselines. Notably, the model exhibits strong generalization across modality com- binations: it supports inference from single and multiple spectra, with accuracy improving monotonically spectral diversity increases.
The main contributions of this work follows:
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We propose language-basse model for molecular structure elucidation that performs symbolic reasoning over multi-spectral data.
By expressing spectral peaks and experimental conditions in natural language, the model supports both single-spectrum and multi-spectral inputs, generalizing effectively across combinations of spectral data, benefiting from spectral complementarity.
LLM achieves state-of-the-nn performance four public chemical datasets, showing strong bustness in unimodal settings and substantial gains when jointly reasoning over multiple spectra.
2 SEMANTIC-DRIVEN SPECTROSCOPIC REASONING
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M DMO
(You
provide componnd) (Givn mutiol spectra
("C-ahalut" 177
Raman
UV-Mi
NMR
ISQC
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Figure I: Overview of the training pipeline for structure elucidation. Characteristic spectral peaks are ex- tracted from raw IR, Raman, UV, NMR, MS data and used construct natural language prompts. These are input to a frozen large language model fine-tuned via LoRA. The model is trained to autoregressively generate molecular structures in SMILES format, supervised by the ground-truth sequence.
2.1 PRELIMINARIES
We consider the task of molecular structure elucidation from spectroscopic data. Formally, let (51, $2,..., 3 denote a set of one or more spectra corresponding to the same molecule. Each spectrum arises from modality m, (IR, Raman, UV, UV, NMR, MS). A single spectrum finite set of peak observations. s; (P,, a,),,,)))" ) ) measurement characteristic of the modality (such as wavenumber, chemical shift, or mass-to-charge ratio), and aj the corresponding normalized intensity or abundance.
be described as where ( is the number of peaks, py denotes physical
The objective is to infer the molecular structure, represented as a SMILES sequence = (y11 .2..... where T is the number of tokens in the sequence. This task is cast autoregressive modeling problem,
.t.......777 yr),
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in which the conditional probability of generating a sequence given the spectra factorizes as
T
Po(v)s) = II (e y<e):
(1)
where Ver denotes the prefix previously generated tokens, and are the model parameters.
Given training corpus of N paired examples (s(*), (y))) where s is the spectroscopic input and y is the corresponding SMILES sequence, the model is trained to minimize the negative log-likelihood the reference sequences. This corresponds to the cross-entropy objective:
CCE =
log (0 (32)
(2)
2.2 JOINT TINE-TUNING OF MULTIPLE SPECTRA
2.2.1 CORPUS CONSTRUCTION AND PREPROCESSING
A prerequisite for joint spectroscopic reasoning is a sufficiently large and diverse corpus of molecules an- notated with multiple types of spectral data. To this end, we combined four complementary sources span- ning simulated and experimental data. The Q.99peecra (QM9s) dataset (Zou et al., 2023) provided sim- ulated IR, Raman, and UV-Vis Vis spectra for approximately 134,000 small organic molecules, generated by frequency analysis and time-dependent density functional theory at the B3LYP/def2-TZVV level. The Mul- timodal Spectroscopic dataset (Alberts et al., (024b) extends this coverage with simulated IR, MS, and mul- tiple NMR spectra, including 'H, Cs and HSQC correlations, for over 790,000 molecules extracted from USPTO reactions (Lowe 2012). For experimental validation, we included MassSpecGym (Bushuiev et al., 2024 with 230,000 high-tesoluttin spectra across 29,000 compounds, and MassBank (Horai al.) (2100), which contributed additional MS and MS/MS spectra obtained under varied instrument conditions. Together, these resources contribute more than 5.5 million spectra paired with over 940,000 unique molecules, forming one of the largest corpora for end-to-end structure elucidation. Table summarizes their composition.
then processed to produce compact, interpretable peak-level features. For vibrational and electronic spectra. IR and Raman signals were truncated to the 500-4000 cm -1 region and uniformly sampled, while UV-Vis spectra were truncated to 1.0-15.0 eV 0.02 eV intervals. Intensities were normal- ized by their maximum value, and characteristic peaks were extracted using local maxima and prominence thresholds. NMR spectra were processed by cropping to the minimal region containing significant peaks and discarding signals below one percent of the maximum intensity. For 'H and I&C spectra, chemical shifts and intensities were retained, while HSQC spectra preserved two-dimensional correlations between proton and carbon shifts along with estimated proton counts. Mass spectra were standardized by rounding m/z values to two decimals, scaling intensities into the range [0,100], and preserving auxiliary metadata such as ionization mode, collision energy, and instrument type. To support downstream model training, all spectra were ultimately represented as structured arrays of peaks and intensities, scrialized in JSON-like format. Appendix A.2 contains more details for processing.
Raw spectra
2.2.2 LANGUAGFFBASED FORMULATION OF SPECTRA
To unify heterogeneous spectra within single modeling framework, we transformed peak arrays into natural language descriptions. A mapping function & converts each spectrum s; into a textual prompt ((x;) E V where V denotes the model vocabulary. Multiple spectra can jointly encoded via concatenation:
x ((si) 6(82) @ ... ((8)).
(3)
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Prompts explicitly specify modality and report salient peaks in textual form, thereby embedding vibrational frequencies, chemical shifts, and fragmentation patterns into the same linguistic space. In addition to peak values, we also incorporate relevant experimental conditions and metadata, such as ionization mode, colli- sion energy, solvent, and instrument type, so that the model can contextualize spectra under varying acqui- sition settings. Training examples are cast in an instruction-response format: the "Human" " side presents the spectroscopic description and the query to infer structure, while the "GPT" " side provides the reference SMILES. This formulation enables symbolic reasoning over complementary spectroscopic cues, bridging continuous and discrete modalities.
2.2.3 AUTOREGRESSIVE BACKBONE AND PARAMETER-EFIICHE FINE-TUNING
As illustrated in Fig. we adopt Qwen3 Yang et al. 2025) as the backbone model, chosen for its scal- ability and strong reasoning performance. To specialize ize it for spectroscopy-to-stnee
generation, employ Low-Rank Adaptation (LoRA) (Hu et al. 2022), freezing the backbone weights while introduc- ing lightweight trainable matrices in each transformer layer. This parameter-efficien tuning preserves the general linguistic competence of the model while enabling effective alignment with spectroscopic inputs.
Given prompt derived from one or more spectra and reference SMILES sequence y (y1.... yr), the model is optimized using the autoregressive cross-entropy loss introduced in Sec. [.1] This objective encourages the model to maximize the conditional likelihood (o (r, V<t) for each token yt, with @ denoting the LoRA parameters. By training over millions of paired examples, the model learns to map spectroscopic patterns into chemically valid and structurally faithful sequences.
2.2.4 INFERENCE AND DECODING
During inference, one or more spectra are preprocessed. converted into natural language prompts, and fed into the fine-tuned model. Candidate SMILES strings are generated autoregressively:
arg max (y)
(4)
We employ nucleus sampling with threshold p 0.7 and temperature scaling t to balance determinism and diversity. As detailed in Appendix A.6] moderate temperatures (r 0.4) yield the best trade-off be- tween structural accuracy and output diversity. Notably, no modality-ppecific encoders, handcrafted rules, or retrieval components are introduced, ensuring a fully end-to-end generative process.
3 EXPERIMENTS
3.1 EXPERIMENTAL SETUP
We evaluate SpectraLLM across multiple types of spectra and benchmark datasets. Following the prepro- cessing described in Sec. A.2 we constructed training and evaluation sets from QM9s, the Multimodal Spectroscopic dataset, MassSpecGym, and MassBank, covering simulated and experimental IR, Raman, UV-Vis, NMR, and MS spectra.
Model performance is assessed using a suite of complementary metrics that quantify both exact structure re- covery and chemically meaningful similarity. Specifically, we report functional group overlap, fingerprint- based Tanimoto coefficients (ECFP4 and MACCS), cosine similarity, maximum common edge substruc- ture (MCES), and Fraggle similarity. These criteria jointly capture correctness the level of global struc- ture, local substructures, and functional groups. Full definitions and calculation details are provided in Appendix A.3
For baseline comparison, we include representative state-of-the-art spectrum- to--nnucttttttuuure models across dif- ferent modalities: DiffMS (Bohde al. 2025) and Spec2Mol (Litsa et al. 2023) for mass spectrometry,
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Table 2: Comparative evaluation of SpectraLLM and conventional approaches under individual spectral inputs.
Tanimoto (MACCS)
Spectrum Method
Validity
Tanimoto
Cosine
MCES
Functional Group 1
Fraggle
QM9s
IR-to-Structure
100.00%
0.0718
0.1311
11.3187
0.3151
0.1585
0.1747
IR
Spectra2Structure
100.00%
0.0965
0.1695
10.1081
0,4383
0.2162
0.2308
LM
99.82%
0.1921
0.3120
7,5651
0.6599
0,4330
0.3194
IR-to-Structure
100.00%
0,0766
0.1395
11,3516
0,3525
0.1639
0.1959
Raman
Spectra2Struttuee
100.00%
0. 1089
0.1901
9.4164
0.4419
0.2388
0.2504
SpectraLLM
99.08%
0.2500
0.3786
6,4076
0.7317
0,5071
0.2500
It-to-Structure
100.00%
0.0728
0.1326
I1:424
0.3151
0.1512
0,1837
UV-Vis
Spectra2Structure
100.00%
0.0716
0.1313
11.1222
0.3901
0.1418
0.2092
LP
100.00%
0,0790
0.1426
10.6374
0.3713
0,2026
0.2100
Multimodal Spectroscopic
NMR2Struct
47.62%
0.0433
0.1029
30.6938
0.1718 0.7209
0.1294
0.0962
NMR
SpectraLLM
98.92%
0.4151
0.5322
8.3091
0.6367
0,5862
Table 3: Benchmarking SpectraLLM against established mass spectrometry-based inference models.
Tanimoto (MACCS)
Method
Validity
Tanimoto
Cosine
Functional Group
Fraggle
MassSpecGym
Spec2Mol
62.86%
0.0849
0.1511
0.3111
0.2709
0.2065
Diffiss
57.16%
0.1597
0.2422
0.4890
0,4305
0.3539
SpectraLLM
99.74%
0.1533
0.2558
0,5003
0,4723
0,3610
Multimodal Spectroscopic
Spec2Mol
75.39%
0.0988
0.1739
0.3042
0.2440
0.2587
Diffims
78.77%
0.1535
0.2351
0.4248
0.3730
0,3635
LP
99.64%
0.1844
0.2993
0.4929
0,4254
0,4282
MassBank
Spec2Mol
71.63%
0.0857
0.0006
0.1539
0.1102
Diffiss
23.63%
0.0742
0.2088
0.17995
0.1007
0.0238
SpectraLLM
98.44%
0.1286
0.2229
0.4539
0.3787
0.3150
IR-to-Structure (Alberts al., 2024a) and Spectra-to-Steee Struutturrruur (Kanakala et al. 2024) for vibrational and electronic spectra, and NMR2Struct (Hu et al. 2024) for NMR-based prediction. Detailed descriptions of these baselines, including training configurations and evaluation protocols, are given in Appendix A.4
3.2 STATE-OF-THE-AM PERFORMANCE OF SPECTRALLM
To provide a direct comparison with established spectrum-to-strice approaches, we evaluated Spec- traLLM under unimodal inputs across IR, Raman, VVs NMR, and MS. As shown in Table Table SpectraLL.M consistently outperforms existing baselines in both predictive accuracy and structural fidelity.
On the QM9s dataset, SpectraLLM surpasses traditional spectrum spectrum-to-struetune tosstuucturee pipelines across all three uni- modal conditions. With IR spectra alone, the model achieves a Tanimoto similarity of 0.1921 and cosine similarity of 0.3120, more than doubling the scores previous neural baselines, while simultaneously low- cring MCES from over 10 to 7. 7.5651, reflecting improved substructural alignment. The gains pronounced for Raman spectra: SpectraLLM reaches Tanimoto similarity of 0.2500 and cosine similarity of 0.3786, outperforming the strongest baseline by over 30%, and achieving the highest functional group recovery (0.7317). Even with UV- spectra, which are intrinsically less structurally informative, Spec- traLLM maintains superior performance, with modest but meaningful improvements in Tanimoto similarity
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Ground-Trus Truth Structures
Structures Predicted by SpectraLLM
Spectra
and UV-Vis Spectra
IR Rarmnn
Raman UV-VIs
Only IR
Only UN-Vis
Only Raman
BARMANGLO
COCCISSOPN
PAMANI UALL
UES
OI
SPCEPHOERPCIR
Groud OHOrcloent
DICMECLENCE
RAINS3333
RHRAWANS
CHONGEROUL
BUSUSERE
OH
PAASE BANDRO
BUOVDREY NH,
CECCESOCIIOO
CCTNETOL BOOO
PANAN
Grues 0ress
CCSCO DICICIC
NANANS BAL CESCCROOC10
Gman6CCOCCOCOO
CROOCOCO
DICCODO
CHOCOCCO
RAMANN SEET CRCOOCROCCO
Figure 2: Case studies illustrating complementary roles of Raman and IR spectra in joint structure elucida- tion. Top: Three representative examples where incorporating Raman spectra corrects mispredictions made using only IR or UV-Vis inputs, reflecting Raman's sensitivity to seeriiaabllit--deeeddd Bottom:
substructures. Three representative examples where IR spectra are indispensable for identifying carbonyl groups and resolving branched chain configurations; without IR input, predictions based Raman and UV-Vis remain ambiguous or incorrect.
(0.0790) and functional group recovery (0.3713). These results indicate that the model can extract structural cues even from sparse or globally delocalized modalities, highlighting its robustness under information- limited conditions.
The advantage of SpectraLLM becomes even clearer in the NMR domain. On the Multimodal Spectroscopic dataset, the model achieves a Tanimoto similarity 0.4151 and cosine similarity of 0.5322, compared 0.0433 and 0.1029 for NMR2Struct. MCES decreases dramatically from 30.6938 to 8.3091, underscoring the improved recovery of substructural scaffolds. Moreover, functional group accuracy rises from 0.1718 0.7209, reflecting the model's 's ability to map NMR patterns onto interpretable chemical substructures with far greater reliability than prior specialized models. Importantly, validity remains near 99%, eliminating the decoding instability observed in traditional architectures.
Mass spectrometry presents a distinet challenge owing to its fragmented and context-dependent signal. Across the three evaluation settings- MasssBnnk MassSpecGym, and the Multimodal Spectroscopic bench- mark-Spoen markk-SpcctraLLM consistently delivers state-of-the-ar performance while preserving near near-perfect struc- tural validity (Table 3). On MassBank, SpectraLLMM substantially improves all similarity metrics relative baseline models, including a functional-grome recovery of 0.4539 and a MACCS- based Tanimoto of 0.3787, outperforming both Spec2Mol and Diffms by sizeable margins. Moving to MassSpecGym, which expands
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Table 4: Enhanced structure elucidation through fusion of complementary spectroscopic modalities.
Tanimoto (MACCS)
Inputs
Validity
Tanimoto
Cosine
MCES /
Functional Group
Fraggle
QM9s
IR
99.82%
0.1921
0.3120
7.5651
0.6599
0.4330
0,3194
Raman
99.08%
0.2500
0.3786
6.4076
0.7317
0,5071
0.2500
UV-Vis
100.00%
0.0790
0.1426
10.6374
0,3713
0.2026
0.2100
Jointly
98.72%
0.3355
0,4560
4,9647
0.7934
0,5785
0.4117
Multimoaal Spectroscopic
IR
99.63%
0,1720
0.2868
15.3234
0.6023
0,4031
0.3906
MS
99.64%
0,1844
0.2993
11.3243
0.4929
0.4254
0,4282
IR+MS
99.25%
0,2300
0.3519
10.4164
0.6345
0.4887
0,4566
C NMR
99.64%
0,1016
0.1801
14,8865
0.4249
0,2952
0,3607
H NMR
99.10%
0.0720
0.1341
18.6141
0,3329
0,2203
0,2572
HSQC NMR
99.64%
0,2058
0.3221
13.4919
0,5495
0,4392
0,4274
Jointly NMR
98.92%
0,4151
0.5322
8,3091
0.7209
0.6367
0,5862
Jointl NMR+IR
99.60%
0,4121
0,5341
8,3855
0.7764
0.6575
0.5809
Jointly NMR+MS
98.37%
0.4518
0.5601
8.16
0.7618
0.6760
0.6063
Jointl NMR+IR+MS
99.79%
0.4875
0.5973
8.1151
0.8103
0.7099
0.6222
upon MassBank with additional curated spectra and molecular diversity, SpectraLLM maintains this strong performance profile: it attains 99.74% validity, the highest cosine similarity (0.2558), and the strongest functional-group recovery (0.5003), while achieving Tanimoto similarity (0.1533) competitive with Diffms (0.1597). Finally, on the Multimodal Spectroscopic dataset, SpectraLLLM again obtains the best overall struc- tural alignment, with the highest Tanimoto similarity (0. 1844), cosine similarity (0.2993), and functional- group accuracy (0.4929). Collectively, these results highlight Spectral.LMM capacity generalize across di- verse MS conditions while consistently surpassing specialized architectures on detailed cructural-similarii metries.
Taken together, these results demonstrate the breadth and flexibility of SpectraLLM, Even when restricted to a single spectroscopic modality, the model extracts chemically meaningful signals and consistently surpasses the strongest baselines in both structural similarity and functional group recovery. This robustness under unimodal constraints not only underscores the strength of linguistic representation of spectra, but also provides the foundation for the more substantial gains observed in multi-spectral integration.
3.3 SYNERGISTIC INTERACTION AMONG MULTIPLE SPECTRA
In line with our central hypothesis that integrating complementary spectroscopic modalities enhances molec- ular structure prediction, systematically benchmarked Spectral.LM across single-spectrum and multi- spectral configurations on two representative benchmarks: QM9s, which provides simulated IR, Raman, and UV-Vis spectra, and the Multimodal Spectroscopic dataset, which includes IR, MS, and multiple NMR modalities (HH, 1&C HSQC). Importantly, the results reported in Table reflect controlled test-time condi- tions: although SpectraLLM is trained on datasets containing mixtures of single- and multi-spectral inputs, at inference we restrict the prompting to either single-spectrun or multi-spectral inputs to isolate the con- tribution of each spectra-fusion strategy. As shown in Table 4 single-spectrum inputs alone impose clear limits on predictive accuracy, whereas their fusion yields smooth and substantial performance gains across all structural metrics. On QM9s, for example, jointly leveraging IR, Raman, and UV-Vis increases Tan- imoto similarity from 0.2500 (Raman alone) to 0.3355 and lowers MCES from more than 10 4.9647, highlighting the additive value of combining vibrational and electronic signatures. A similar trend holds for the Multimodal Spectroscopic benchmark: integrating 'H, 13C, and HSQC NMR yields strong boost substructural fidelity (Tanimoto 0.4151; MCES 8.3091), with further improvements when NMR is fused with MS and IR (functional- -roup recovery 0.8103; Fraggle 0.6222).
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To more precisely disentangle inter-spectra interactions and quantity which spectra contribute most joint reasoning, we include an extended set of ablation experiments in Appendix A.9 Concretely, on QM9s we train a separate model for every possible input configuration and evaluate each model both in-domain and cross-domain. These experiments reveal clear hierarchy of informational complementarity across spectro- scopic signals. Cross-domain evaluation further shows that models trained on multi-spectral inputs general- ize better to unseen single-spectrum conditions than the reverse, indicating that fused spectra representations act as a more expressive structural prior.
Overall, these ablations corroborate our main finding that the benefits of multi-spectral fusion stem not only from aggregating signal strength but also from enabling SpectraLLM to form a shared latent space that captures spectra- inneataannvvaiant- structural cues.
To gain mechanistic insight into the synergistic effects of multi-spectral integration, we conducted a qual- itative case analysis focusing on representative molecules that were successfully reconstructed only when specific combinations of modalities were available (Fig- 2 The analysis provides a clearer understanding of how Raman and IR spectra contribute uniquely to accurate molecular reconstruction and why their fusion is essential in overcoming the limitations of unimodal spectral inputs.
In the top panel, where Raman spectra play crucial role, we observe several cases where IR UV-Vis alone misassigned key structural motifs, but the inclusion of Raman spectra rectified these errors. For in- stance, the molecule COCc1 cc [nH] c1 (imidazole scaffold) is correctly reconstructed with the joint IR Raman input. While IR spectra alone struggle to accurately assign the heteroaromatic ring structure, Ra- man's sensitivity to baariiaabiityydepeeddde
substructures allows for precise identification of the imidazole ring, which otherwise remains ambiguous in IR spectra. Similarly, for C#CC (=O) C#CC, the carbon-carbon triple bond with adjacent ketone groups is correctly identified only when Raman spectra are combined with IR or UV-Vis data, revealing how Raman's is ability to detect bond polarizability is key to distinguishing these motifs. The case of CC (C) (C) C (=O) C highlights Raman's 's power in resolving functional groups and dif- ferentiating between isomers that IR alone cannot clarify. In the bottom panel, we observe the indispensable role of IR spectra in resolving critical structural details, especially for carbonyl functionalities and complex branching configurations. For example, CC (C) (C) C (=Ō) C is correctly identified when IR is included, but remains ambiguous with Raman and UV-Vis alone. This reflects IR's R ability to distinctly capture the position and environment of carbonyl groups, which cannot be unambiguously inferred from Raman's 's more general vibrational modes or UV-Vis electronic transitions. Similarly, when CCICC (C) (0) C1 is reconstructed, the inclusion of IR data resolves the branching pattern, which Raman and UV-Vis could not distinguish. This emphasizes the importance of IR in delineating carbonyl positioning and the molecular architecture at specific branching points, showing its unique contribution to correct isomeric assignment.
These examples confirm that while no single spectrum suffices, the integration of multiple modalities pro- vides complementary structural constraints that significantly reduce ambiguity and improve the accuracy of molecular reconstruction. However, it is important to acknowledge that the analysis presented here show- cases only the successful and accurate cases. To better understand the limitations and boundaries of this ap- proach, further explore "bad cases" in Appendix A.13 These analyses reveal that the upper bound of Tanimoto similarity and the observed performance ceiling are often constrained by the inherent ambigu- ity in spectral data, where structurally different molecules share similar spectral features. These challenging cases demonstrate that while multi-spectral fusion provides substantial gains in structural prediction, the model's ability distinguish between these highly similar spectra remains fundamental challenge.
4 RELATED WORKS
Structure elucidation supports mechanistic studies and functional analysis in biomolecules (Krishnan & Rupp 2012 Brünger al. 1998), guides material design and defect analysis (Hemath et 2020 Gu

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ct al. 2024), and optimizes product performance (Rantanen & Khinast 2015). Powered by techniques such as Cryo-EM (Adrian et al., 1984), NMR (Hall) 1964). IR (Ng & Simmons 1999), and MS (De Hoffmann
& Stroobant| 2007), it involves ives interpreting spectral features to identify functional groups and connectivity
patterns (Okada & Kotani, 1992, Coates et al. 22000). These are matched against spectral databases (e.g..
NIST (Linstrom & Mallard 2001), MoNA (Horai et al. 2010). SDBS (Punjabi et al. 2025). GNPS (Aron
et al. 2020) to infer candidate structures (Shiferaw et al. 2020 Platte & Heise [014])
IR, Raman, and UV-Vis spectroscopy are widely used for molecular structure elucidation, probing functional
groups, vibrational modes, and electronic transitions, respectively Stuart (2004); (Perkampss (2013); Picollo
et al. (201)); WWilson et al. 1980); Zhang et al. (2020); et al. (2013): Skinnider et al. (2021). While they ( are fast and non non-destructive, these techniques suffer from peak overlap, weak signals, and background in- terference, and their interpretation often requires expert knowledge. NMR spectroscopy offers richer struc- tural resolution, revealing atomic connectivity, chemical environments, and stereochemistry through one- dimensional (HH, 18 and two-dimensional (COSY, HSQC, HMBC) spectra Bovey et al. (1988); y James (2012): Binev et al. (2007); Meiler (2003); Elson& & Magde (1974); Mueller (1979). However, NMR analy- sis remains consumnng and combinatorially complex. Mass spectrometry (MS), by contrast, provides high sensitivity and low sample requirements Quinn et al. (2020); Gentry al. (2024), inferring partial structures or formulas from fragmentation patterns Coelho & Franco (2013): Nalbantoglu & Amri (2019). Yet, fragmentation depends strongly on experimental conditions, and spectra often exhibit high ambiguity and poor reproducibility across settings [Bittremiexx et al. (2022); da Silva et al. (2013); (Seein (2012); |Seger (2012): Dührkop et al. (2015). These diverse limitations underscore why no single spectroscopic modality suffices for robust, automated structure elucidation.
Automating structure elucidation has therefore become a longstanding challenge. Machine learning and deep learning methods (Li et al., 22022; Specht et al. 20244; Sridharan et al. [022] SSapegin & Bear 2024
Rippel et al. 2015) Defferrard et al. 2016 Cho et al. 2014b at Liu et [011 Tang et al. 2020 Berman et al.] 2023] Zhang et al.) 2024 Xu et al. 2017] He al. 2021 [ et al., 2023) have advanced tasks
such as substructure identification, candidate filtering, and molecular property prediction. Yet, most remain confined to a single spectral modality. By contrast, human experts routinely integrate complementary spectra
to resolve ambiguities and narrow the candidate space. Recent works support this direction: multi-spectral models combining IR, H-NMR. and 13 C-CNMM achieve notable accuracy gains when aided by formulas or partial structures Alberts et al. ); Yao et al. (2023), and even without formulas. "H/LSCNNME alone can reconstruct molecules up to 19 heavy atoms Alberts et al. (2023); Hu al. (2024). These results demonstrate that end-to-ene multi-spectral fusion is not only feasible but also critical for improving accuracy
and generalization in automated structure elucidation.
5 CONCLUSION
In this work, we presented SpectraLLM, a unified language- framework for molecular structure eluci- dation. By transforming diverse spectroscopic signals into a shared textual representation, the model lever- ages the reasoning capabilities of large language models to capture complementary structural constraints across modalities. Systematic evaluation on four benchmark datasets demonstrated that SpectraL.LM not only surpasses existing baselines under unimodal settings, but also achieves consistent and substantial gains when integrating multiple spectra, mirroring expert analytical practice. Beyond empirical performance, our results highlight a broader conceptual finding: a general-purpose language model can internalize spec- trum-structur relationships purely from paired data, without spectroscopy-spectnn tion hcuristics. This demonstrates that non-linguistic scientific measurements can be mapped into a language token space in which structured chemical reasoning becomes feasible. We view this text-only reframing of spectroscopic inference as a promising direction for building general scientific inverse solvers and for ex- tending language-mode model based reasoning to a wider range of physical measurement domains.
encoders fragmenta-
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ETHICS STATEMENT
All datasets used in this study are derived from publicly available and standardized chemical databases. No personally identifiable, proprietary, otherwise sensitive information was included. The research does not involve human subjects, animals, or environmental interventions, and therefore poses no foreseeable ethical risks.
REPRODUCTBIL STATEMENT
We have taken steps to ensure reproducibility of results. The datasets employed all publicly available and are cited in the main text. Detailed descriptions of data preprocessing, model architecture, training objectives, and evaluation protocols are provided the Methods and Appendix sections. Hyperparamnters, prompt templates, and implementation details are specified in the supplementary material. Source code and processed data will be released upon publication facilitate independent verification.
ACKNOWLEDGMENTS
The authors thank all colleagues and collaborators for their valuable discussions and constructive feedback during the preparation of this work. We also gratefully acknowledge the financial support from the Strategic Priority Research Program the Chinese Academy of Sciences (Grant No. XDA0480102) and the National Natural Science Foundation of China (Grant Nos. 62576339, 92570204, 62236010).
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A APPENDIX
A.1 DATASETS
We pre-trained the model using diverse data compiled from multiple publicly available datasets, Table 1 presents the individual characteristics of each spectrum, while Table presents the basic information of all the datasets collected.
Table 5: Overview of four spectroscopic datasets and their modality coverage.
Dataset
IR
Raman
UV
NMR
Mass
Molecule
Spectral
QM9:
129,817
Multimodal Spectroscopic dataset
CSSOC
Positive, Negative
794,403
4,766,418
PaysSpeeGym
231,104
231. 231,104
MassBlank
122.746
122,746
ALL
943,732
5,510,655
Our data preprocessing pipeline consists four key steps: SMILES standardization and molecular align- ment, dataset merging and splitting, spectral feature extraction, and prompt formulation for language mod- eling. We first standardized all SMILES strings across the four constituent datasets using RDKit ! For each molecule, parsed the provided SMILES using MolF FromSmi les to obtain internal molecular graph, followed by CanitizeMo 1 to canonicalize valence states, remove invalid structures, and generate a consistent, RDKit-normalized SMILES representation. This canonicalization step ensures that molecules
'hups//www.rdkit.org
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originating from different datasets share an identical structural encoding, even when the raw SMILES strings differ in ordering, stereochemical formatting, or atom-group conventions. With the unified SMILES repre- sentation as unique key, we merged all datasets and naturally deduplicated molecules appearing across multiple sources. Table (oble/)]illusee ifllustrate how spectra from four datasets are integrated under single standard- ized SMILES identifier. Molecules with multiple spectra are concatenated using the " "1" separator.
Table 6: Mapping of Molecular Identifiers Across Datasets
multimodal spectroseopic dataset Idx
SMILES
QM9s @M9.enumber
MassBank file name
MussSppeGGym_idetiieer
MussSpeeGGymIDD066222
Celeenenl
898
MSBNK-CASMI 2016-SM826401
124352
MusSSpeCGym//D006623 MassSpecGym/ID0062222
After consolidation, we obtained 943,730 unique molecules, each associated with one to five spectrum types and up to five total spectral instances. Before performing dataset splitting, we preserved the official train/- validation/test partition provided by MassSpecGym, ensuring strict comparability with their benchmark. The remaining molecules- those originating from QM9s, MassBank, and the Multimodal Spectroscopic dataset- were then randomly divided into an 8:1:1 train/validationntest split based on standardized molecu- lar identity. This guarantees that all spectra belonging the same molecule are placed in the same split and that each spectral modality is sufficiently represented across all subsets (Table 7 All reported metrics are computed on the resulting held-out test set. Next, we applied modality-specifie preprocessing procedures convert each spectrum into a structured textual representation suitable for language model training.
Vibrational and electronic Spectra. For continuous spectra, we retained effective signals through modality truncation and normalization procedures. In the IR modality, wavenumbers were restricted 500-4000 cm for the QM9s dataset and 400-4000 cm- for the Multimodal Spectroscopic dataset, uni- formly resampled at 1 cm intervals (3501 and 3601 points, respectively). Raman spectra adopted the same 500-4000 cm c1 range and sampling. UV-Vii spectra were truncated to the 1.0-15.0 eV range, with 0.02 eV sampling intervals (700 points). All spectral intensities were normalized by their maximum value to ensure comparability across molccules and suppress variation introduced by experimental conditions. To further support model interpretability and reduce input complexity, extracted discrete peak- repre- sentations from the preprocessed spectra. More realistically, characteristic peaks from all continuous spectra extracted using built-in functions from the SeiPy library (Virtanen al., (022)) based on local maxima and prominence thresholds. Each spectrum was encoded in structured JSON format, recording only the coor- dinates and relative intensities of characteristic peaks, such as: "Wavenumbers": "wavenumber"") "Int Intensiiies"
(" ("wavenumber) ",
("intensity"" ", "intensity>"
Nuclear magnetic resonance spectrum. For nuclear magnetic resonance (NMR) spectra, we applied modality-specific preprocessing and discrete feature extraction. For 19C NMR and 'H NMR, chemical shift ranges were set to 220-0 ppm and 12-0 ppm, respectively. dynamic threshold (with the threshold being 1% of the maximum spectral intensity) was used to filter background noise, and the spectra were cropped to the minimal region containing significant peaks. The intensity values have all been normalization. For HSQC NMR, which contain 2D correlation peaks between hydrogen and carbon atoms, only peak-level informa- tion was retained. Each peak was characterized by its 'H and 1&C chemical shifts and associated proton count (nH), defaulted to 1 if unspecified. To support downstream prompt generation, we represented NMR signals in structured formats. For I&C and "H spectra, data were stored as key-value pairs of shift and in- tensity values, e.g., "C-shifts' "intensityz" ". For HSQC, the 2D correlations were encoded as: "H-shifts": ["hi", "h "C-shifts"; ["c]" ", "c>"], "], " "Intensities": ["intensity," ", "intensity:" "].
["shift,", ,, "shiftz "I, "Intensities".
["intensity]" "
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Table 7: Data distribution across spectral modalities and splits.
Spectrum
Train
Val
Test
ALL
Mass MS/MS Positive
301,133
15.849
14.247
331.229
643,605
71,511
79.287
794,403
LBC NMR
643,604
71.511
79.287
"H NMR
643,5886
71.509
79,285
794,380
Single
HSQC NMR IR
643,605
71,511
743,152
82,594
87,688
913.634
13,062
UV-Vis
105,078
11,675
13,062
129,8155
IR-Raman=+UV-
129.024
Mulii
"CC+ H+HSQC NMR
494,173
54, 908
78,882
627, 966
IR+Raman+ -Mass
42,652
10,937
ALL
4,8120034
517,055
567,439
5,896,528
Mass Spectrometry. For mass spectrometry (MS), we retained spectra containing between 2 and 1280 peaks. All mass-to-charge ratio (m/z) values were rounded to two decimal places, and peak intensities were converted to relative abundances within a [0, 100] range, also to two decimal places. To preserve experi- modeling, auxiliary metadata (such as instrument type, collision
mental context and enable condition-awer energy, and adduct ion species) was retained when available, distinguishing spectra acquired under different experimental settings for the same molecule. For downstream prompt construction, MS spectra were rep- resented structured arrays of m/zzintensity pairs in JSON format, for example: "mzs": ["m/z1" ", "m/Z2" inntensities":
("intensity]", "intensity,"
Given that the prediction target is the SMILES representation of molecular structures, naturally formu- lated the structure elucidation task as a sequence-to-sequees generation problem. As illustrated Table 30 and Table 29] training data were organized in dialogue-style format, where each example consists of "Human" prompt and a corresponding "GPT" response. The "Human" component encodes the query, including the spectrum modality, extracted features, and task directive infer molecular structure. The "GPT" component provides the expected model output, i.e., the target SMILES string.
To promote generalization across varying data configurations, designed diverse of input formats:
1. Single-spectrum setting (Table. 30): molecular structure prediction based solely on one spectrum type, including individual IR, Raman, UV.Vis, 'H NMR, 1&C NMR, HSQC NMR, MS spectra.
2. Multi-spectral setting (Table. 29p: molecular structure prediction using two more spectra from the same molecule. This includes:
Joint IR, Raman, and UV spectra from the QM9s dataset;
Joint IR, NMR, and MS spectra from the Multimodal Spectroscopic dataset;
Combined 'H NMR, 13C NMR, and HSQC NMR spectra;
This formulation allows the model to reason across heterogeneous spectral evidence while remaining robust missing modalities and variations in experimental settings. We finally construct large-scale multi- dataset, whose composition is summarized Table 7 To ensure compatibility with the input length constraints of the base language model, exclude examples with tokenized prompts exceeding 1024 tokens. Leveraging this dataset, we perform supervised fine-tuning of the foundational Qwen3-32B language model to enable spectrum-informel molecular reasoning.
spectral goustion--answenig
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Molecular Composition Statistics
Nolcou of Wright BMW) Ditribution
Hoavy Abom Count (HAC) Disaribution
Loge Distrbbttio
730000
25:000
230033
900.0
200000
000.0
22000
30000
50000
59060
15:00
2000 2002
-20
30
Hiary
Figure 3: Molecular Property Distributions of the Dataset. This panel of three histograms summarizes critical molecular properties of the datasct: Molecular Weight (MW) Distribution (Left): Shows the frequency molecules across a weight range (0-2500 Da), with a concentration around 500 Da (consistent with typical small-molecule datasets). Heavy Atom Count (HAC) Distribution (Middle): Characterizes the number of non-hydrogen atoms per molecule, peaking at 25 heavy atoms (reflecting moderate molecular complexity). LogP Distribution (Right): Depicts the octanol-water partition coefficient (a measure of lipophilicity), with a sharp peak near () (indicating a skew toward moderately hydrophilic molecules).
A.2 DATASETS ANALYSIS
We conducted detailed exploratory data analysis (EDA) on datasets, examining molecular composition and functional group distributions as shown in Figures 3 and 4 The datasets encompass total of 943,729 molecules, reflecting both high structural diversity and strong chemical relevance.
The molecular weight (MW) distribution indicates that the vast majority of molecules fall within the 100-500 Da range, peaking around 250 Da. This confirms that our datasets are predominantly composed of small molecules, which remain critically important across multiple domains. For instance, in metabolomics and clinical diagnostics, most metabolites and disease-related biomarkers are below 300 Da. Environmental and food safety monitoring typically targets small organic molecules such as pesticides, plasticizers, and pollutants. In chemical and materials manufacturing, quality control of monomers, solvents, intermediates, and byproducts similarly relies on small-molecule spectral analysis. The prevalence of small molecules in widely used public spectral datasets (e.g., NIST, MassBank, GNPS, QM9) further supports their relevance and generalizability.
Heavy atom count (HAC) is mostly concentrated in the 10-45 range, suggesting that the datasets cover molecules of moderate structural complexity. LogP values primarily between 0 and 5, indicating that the dataset includes compounds spanning from moderately hydrophilic to moderately lipophilic, which is critical for modeling both solubility and spectral properties across different chemical environments.
Overall, the EDA confirms that our datasets are chemically diverse, structurally complex, and rich in func- tionally relevant substructures. This combination ensures that models trained on these data learn robust representations suitable for a wide range of small-molecule spectral prediction tasks, making the datasets both practically relevant and scientifically challenging.
A.3 METRICS FOR STRUCTURAL SIMILARITY
Our evaluation goes beyond assessing the exact recovery of molecular structures, and also probes whether the model has internalized the fundamental chemical cues embedded within the spectra, such as generat- ing chemically plausible candidates, reconstructing key substructures, and correctly identifying functional
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Functional Group Distribution (Total Molecules: 943,729)
Number of Molecules Containing the Group
300000
27.2%
23.0%
200000
100000
8.%
Aromatic Ring
Ether (K-O-8)
Ketene (R-C-R)
Halogen IP. n
Alcotal (R-ON)
Amine (R-1)
Ester (8-C0D-))
Suffone/Sufflle
Carbaryiic Acid (R-COOH)
Functional Group
Figure 4: Functional Group Distribution in the Dataset. This bar plot quantifies the prevalence of key functional groups across the 943,729 molecules our training dataset. The y-axis represents the number of molecules containing each functional group, with the corresponding percentage (relative to the total dataset size) labeled atop each bar.
groups. To quantify this, we only evaluate the accuracy of functional group prediction coarse-grained measure of structural fidelity, but also assess molecular similarity at a finer granularity using fingerprint- based Tanimoto metrics and maximum common substructure (MCES) analysis. The subsequent section provides detailed account of each evaluation criterion.
A.3.1 FUNCTIONAL GROUP SIMILARITY METRIC
To quantify the similarity between molecules in terms of their functional group composition, define a functional group similarity score based on SMARTS pattern matching. Using a curated dictionary of 17 common functional groups (c.g., alcohols, ketones, ethers; see Table 8). we identify the presence of each group via substructure search implemented with RDKit (Landrum et al. 2016). Let G₁ and G2 denote the of functional group types identified in molecule 1 and molecule respectively. The functional group similarity SPG is then computed as the Jaccard index over these sets:
G1 O G2
(ro(mol,, molz))
G1 UG2
This metric reflects the qualitative overlap in functional group composition, regardless of group count spatial arrangement. Ranging from 0 (no shared functional group types) to 1 (complete overlap), it offers a chemically interpretable measure that complements structure-level similarity metrics.
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Table 8: Functional group definitions used.
Index
Group
Definition
Alkane
[CX4]
Alkene
[C3]=[[X3]
Alkyne
[CX2] #C
Arene
[$ ([cX3] (:*) :*) ( [[[[22++]]]] (*))
5
Alcohol
[#6] [OX2H]
Ether
[#6]
7
Aldehyde
[CX3#1] (=0) [#6]
Ketone
[46] (=0) [46]
Carboxylic acid
[CX3] (=0) [CX2H1]
Ester
[cx] (=O) [OX2H0] [#6]
11
haloalkane
[F,] C1,
12
Alkyl halide
[CX3] (=[[x1] [F, C1, Cr,
13
Amine
[NX3;$ (NC=0)
Amide
[NX3] [CX3] (=(x1]] [#6]
15
Nitrile
[NX1] [CX2]
16
Sulfide
#16X2H0]
Thiol
[122H
A.3.2 MOLECULAR FINGERPRINT SIMILARITY METRICS
Molecular fingerprints are vector representations that encode the presence specific substructures within a molecule, and widely used assess molecular similarity. In this work, compute fingerprint-based similarities between predicted and reference molecules using RDKit and several established metrics:
Tanimoto Similarity. Given two binary fingerprints A and B, with Tanimoto coefficient is defined as:
|A], B), and IAn B), the
|An B
Tanimoto(A,
|A U B
a
This metric is computed using ECFP4 circular fingerprints, which capture atom-centered substructures within a radius bonds.
MACCS Tanimoto Similarity. Identical in form to the standard Tanimoto coefficient, but computed over 166-bit MACCS keys, which encode the presence of predefined structural fragments rather than circular environments.
Cosine Similarity. For continuou-vvaluee fingerprints (c.g., neural embeddings), similarity computed as the cosine of the angle between vectors:
Cosine(A, B) A B
|| A B
where A and denote the continuous fingerprint vectors (embeddings) of the predicted molecule and the reference molecule, respectively. This metric captures the overall similarity of their learned feature repre- sentations in high-dimensional space.
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Fraggle Similarity. Based on fragment matching rather than fixed-length vectors, this metric decomposes both molecules into substructures and computes alignment-based similarity. It is implemented via RDKit's FraggleSin in module, and reflects partial structural overlap beyond atom-level matching.
A.3.3 DERIVED STATISTICAL METRICS BASED ON FINGERPRINT SIMILARITY
In addition raw similarity scores, report threshold-basee classification metrics that stratify prediction quality according to Tanimoto similarity levels. These derived measures capture not only exact matches but also structurally plausible alternatives that retain key substructures of the target molecule.
Approximate Match Rate. The proportion predicted molecules with Tanimoto similarity 2 0.675 to the reference structure. This threshold is widely adopted indicate moderate-to-higee structural similarity in cheminformatics.
Acceptable Match Rate. The proportion of predictions with Tanimoto similarity 2 0.4, often associated with weak but potentially chemically relevant similarity.
A.3.4 MAXIMUM COMMON SUBSTRUCTURE SIMILARITY
The Maximum Common Edge Subgraph (MCES) similarity quantifies the extent structural overlap be- tween two molecules by identifying their largest common subgraph under graph isomorphism. Given molecular graphs G2 and G2, the MCES is defined as the largest subgraph Geommon such that Geommon S G1 and Geommon S G2. The similarity is computed as the ratio of shared bonds to the maximum number of bonds in either molecule:
common)
MCES Similarity max((E(()))
where FE() denotes the of edges (chemical bonds) in graph G. This metric captures partial structural correctness and is particularly informative when global fingerprint-based measures fail to reflect local sub- structure similarity.
A.4 BASELINE MODELS
To benchmark the performance of SpectraLLM, we compare it with state-of-the-art spectrum-to--srrutture models spanning various spectroscopic modalities, including mass spectrometry, vibrational spectroscopy, and NMR.
For mass spectrometry data, we compare our model with DifIMS (Bohde et al. (025). DiffMS is diffusion- based molecular generation framework specifically designed for mass spectrometry-baseee structure elucida- tion. The encoder of DiffMS uses Transformer architecture to model spectral features such as peak for- mulas and neutral losses, while its decoder is a discrete graph diffusion model conditioned on the chemical formula. During training, the decoder is pre-trained on large-scale fingerprint-structure dataset, allowing it to robustly map latent spectrum embeddings molecular structures. DifIMS demonstrates superior perfor- mance across various benchmarks, with stable improvements as the amount of pre- mre-trainig data increases.
In comparison, another baseline model for mass spectrometry structure elucidation is Spec2Mol (Litsa et al. (202)). Spec2Mol adopts an end-to-end encoder-decoder framework inspired by speech recogni- tion (Speech2Text).. The encoder maps MS/MS spectra to embeddings, and the decoder, a structure auto- encoder pre-trained on large compound datasets such as PubChem and ZINC, decodes these embeddings into SMILES strings to propose potential molecular structures. Spec2Mol measures the similarity between
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generated structures and the ground truth using metrics such as fingerprint similarity and maximum com- mon substructure. The results show that Spec2Mol can identify key information (key substructures) from the spectra and generate structures with similarity to the real molecules comparable traditional methods that rely on fragmentation trees.
Since our dataset is large and training time extensive, we directly use the open open-source embedding parame- ters of both baseline models without additional fine-tuning. Specifically, Spec2Mol's spectrum encoder was trained on the NIST Tandem MS library, and DiffMS's embedding comes from pre-training on MassSpec- Gym, which includes subset of MassBank. Therefore, the evaluation of DifIMS MassBank can be considered in-domain, while Spec2Mol, trained on a subset NIST MS/MS, is evaluated out-of-domain. To ensure comparability with Spec2Mol, set the number of generated results in DiffMS's decoding pro- cess to 1.
In our evaluation of vibrational and electronic spectroscopy data, adopt two baseline models: Spectra-to-Structure (Kanakala et al. (024) and IR-to-Structure (Alberts et al.) 2024a). Spectra-to-Strucuure implements a dual-encoder architecture (named SMEN) comprising transtormer- spectral encoder and graph-based molecule encoder (EGNN), trained via contrastive loss so that correct molecule-spectrum pairs occupy nearby points in shared embedding space; candidate molecules are then ranked by embed- ding similarity, and a supplementary generative module decodes top embeddings into SMILES when needed. to--uuttuuturr deploys
transformer that takes as input an IR spectrum (and the original work also the molecular formula) and outputs a SMILES string describing the predicted molecule. To ensure a fair comparison under our own experimental constraints, we omit the molecular formula and sup- ply only spectral data as input for both baselines. The original models were trained under different regimes on small simulated-IR QM9 molecules, tal IR spectra for 6-13 heavy-atom molecules), yet we re-adopt their published training configurations and preprocessing pipelines, applying them to our adapted IR, Raman, and UV-Vis subsets drawn from QM9s dataset; summary statistics of training and test splits provided in Table 9 and detailed training hyperpa- rameters (epochs, batch sizes, learning rates) follow those in their respective sources (see Appendix A2.12).
Eppectr-to-Structie
to-Structure on simulated and experimen-
In our evaluation of NMR- based spectral prediction, include NMR2Struct (Hu al.) 2024) as baseline. NMR2Struet implements an end-to-end multitask machine learning framework that combines a convolu- tional neural network encode raw one-dimensional 'H and/or L3C NMR spectra with wansformer-based decoder that predicts molccular connectivity and full structure without requiring molecular formula or pre-defined fragments. The model first pretrained on substructurr-tt--stttuuure task, assembling small molecular fragments into complete molecular graphs, then extended into full cnnctrum-to-strutuun model. our experiments, we re-trained NMR2Struct using the HH and I&C NMR subsets drawn from our Mul- timodal Spectroscopic dataset under the same preprocessing and training protocols, and we report dataset statistics in Table
Table 9: Dataset Split for Different Baseline Models
Model
Dataset
Train/Val
Test
QM9s(IR-only)
105,079/1 (079/11,676
13,062
IR-to- Structure
QM9s(Raman-onlyy)
105,079/1 1,676
13,062
QM9>(UV-only)
105,079/1 1,676
13,062
QM9s(IR-only)
105,079/11.66
13,062
Spectra2Structure
(M9+(Raman-onll)
105.079/1 1,676
13,062
QM9>(UV-only)
105,079/1 1,676
13,062
NMR2Struct
Muli((MMR-only)
643,604/71.512
79,287
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Table 10: Comparison of Sp and VLM-LLM performance on the QM9s dataset, demonstrating superior performance of the unified language model in structure elucidation.
Tanimoto (MACCS)
Inputs
Validity
Tanimoto
Cosine
MCES
Functional Group
Fraggle 1
VLM-LLM
IR
99.67%
0.1434
0.2446
8.7010
0.5665
0.3510
0.2767
Raman
98.17%
0.1616
0.2716
7.8993
0.6005
0.3676
0.3131
UV-Vis
99.63%
0,0874
0,1576
10.2960
0,3645
0,2158
0,21 12
Jointly
99.40%
0.1954
0,3139
7.0543
0.6828
0,4205
0,3571
LLM
99.82%
0,1921
0,3120
7.5651
0.6599
0.4330
0.3194
Raman
99.08%
0.2500
0.3786
6,4076
0.7317
0.5071
0,2500
UV-Vis 100.00%
0.0790
0,1426
10.6374
0.3713
0.2026
0,2100
Jointy 98.72%
0.3355
0,4560
4,9647
0.7934
0,5785
0.4117
A.5 SELECTION OF MODEL ARCHITECTURE
Designing an effective model architecture for structure elucidation from spectroscopic data requires careful consideration of the modality-specific characteristics and their interaction with large-scale language mod- eling. We explored two principal approaches for integrating multiple spectral inputs with large language models: (1) a daaiionlanguaee-cnee encoder, and their latent representation is subsequently merged with discrete mass spectra for structure gen- eration; and (2) a unified language- based architecture that encodes both continuous and discrete spectra as structured textual descriptions, directly processed by pretrained LLM.
pipeline in which continuous spectra are first interpreted via a visual
Inspired by recent advances in vision-language models (VLMs), we initially explored a two-stage architec- ture for multimodal spectral inference: IR. Raman, and UV-Vis spectra processed by a vision encoder (e.g., 20we22.5VVL)) The encoder output was decoded via beam sampling into a preliminary SMILES candidate. This intermediate representation, alongside the mass spectrum (encoded as m/z-intensit pairs peak descriptors), was subsequently input into large language model (LLM, c.g., Qwen3-32B) to generate the final molecular structure.
rendered into 2D images and
While this hybrid approach leveraged the representational flexibility of VLMs for interpreting unstructured plots, it introduced several critical limitations:
The intermediate SMILES representation was lossy and error-prone, potentially propagating inac- curacies from the vision stage to the language model.
The handoff between modalities disrupted end-to-end optimization, the system lacked shared latent space and consistent gradient flow.
When input spectra were incomplete or noisy, the intermediate SMILES failed to preserve chemi- cally meaningful cues, leading to reduced interpretability and robustness.
By contrast, the language-centric architecture presented in this study employs discrete peak features from all modalities as structured input to a unified LLM, eliminating the need for intermediate symbolic forms. As shown in Table I0 and Table 11 we benchmarked both architectures on the QM9s and Multimodal Spectroscopic datasets, reporting structure reconstruction performance across multiple criteria.
Across all metrics and datasets, the unified LLM paradigm consistently outperformed the VLM-LLM cas- cade. Notably, improvements LLM's superior reasoning capabilities a purely linguistic input space. We attribute this performance gain the model's ability jointly process all modalities in chemically coherent and interpretable manner,
pronounced under stricter evaluation thresholds, underscoring the
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Table 11: Comparison M and VLM-LLM performance on the Multimodal Spectroscopic
dataset, highlighting the advantages of multi-spectral fusion in SpectraLLM.
Tanimoto (MACCS)
Inputs
Validity
Tanimoto
Cosine
MCES
Functional Group
Fraggle
VLM-LLM
IR
100.00%
0,1392
0.2385
14.3495
0.5166
0.3373
0.3415
MS
98.56%
0,1855
0.2965
10.8124
0.5268
0,4326
0,4258
IR+SS
99.66%
0,1902
0,4450
10.2871
0,4812
0,4450
0,4664
C NMR
100.00%
0,1404
0.2357
15.6020
0.4386
0,3140
0,3632
H NMR
100.00%
0,1484
0.2515
17.5893
0.4098
0,3373
0,3252
IIQQC NMR
99. 82%
0,1532
0,2582
16,5800
0.4239
0,3346
0,3438
Jointy NMR
0,2221
0,3425
13.2811
0,4341
0,4520
Jointly NMR+IR
99.60%
0,2598
0.3858
10.7038
0.6493
0.5070
0,4876
Jointly NMR+MS
98.37%
0,2822
0,4102
12.4570
0.6130
0.5595
0.5415
Jointly NMR+IR+
98.74%
0,2455
0,3681
9,9964
0,6045
0,5098
0,4785
LLM
IR
99.63%
0,1720
0.2868
15.3234
0.6023
0.4031
0,3906
99.64%
0.1844
0.2993
11.3243
0.4929
0,4254
0,4282
IR+MS
99.25%
0,2300
10.4164
0,4887
0,4566
MC NMR
99.64%
0.1016
0.1801
14.8865
0.4249
0,2952
0.3607
H NMR
99.10%
0,0720
0.1341
18.6141
0.3329
0.2203
0.2572
ISQC NMR
99. 64%
0.2058
0.3221
13,4919
0.5495
0,4392
0,4274
Jointl NMR
98.92%
0,4151
0,5322
8,3091
0.7209
0.6367
0,5862
Jointly NMR+IR
99.60%
0.4121
0.5341
8.3855
0.7764
0.6575
0,5809
Jointly NMR+MS
98.37%
0,4518
0.5601
8.1682
0.7618
0,6760
0.6063
Jointly NMR MR
99.79%
8.1151
0.7099
0.6222
without cross-modal encoding artifacts. Furthermore, unlike the VLM--LM pipeline, where the information from one modality (e.g., i the rision-based representation IR or Raman spectra) is susceptible to distortion before reaching the LLM, SpectraLLM directly processes peak-level features from all spectral inputs, suring consistent gradient flow and maximizing interpretability and robustness, particularly challenging, noisy, incomplete spectral data.
These findings reinforce the adoption of a language-base based modeling framework for multi-spectra analysis. While VLMs remain valuable for spectrum visualization and downstream interpretabilityy symbolic generation, particularly in the context of small molecule structure elucidation, best achieved via a unified language model trained curated, peak-based prompts.
A.6 DECODING STRATEGIES FOR SPECTRUM-TO-STRRE SO-RUUCUUE GENERATION
To generate molecular structures from spectroscopic inputs, we use the fine-tuned SpectraLLMM model in a greedy decoding setup with fixed temperature and nucleus sampling parameters. Specifically, we adopt a cemperature-contron controlled sampling strategy with a nucleus sampling threshold p = 0.7 (default setting), and vary the temperature to investigate its influence on generation diversity and accuracy. Lower temperatures bias the model toward more deterministic predictions, while higher temperatures encourage exploration by flattening the probability distribution. As shown in Table 12] moderate temperature of 0.4 achieves the best overall performance across multiple metrics, including Tanimoto similarity, functional group recov- cry, and substructure alignment scores (c.g., Fraggle). Extremely low temperatures (c.g.. 0.2) slightly improve structure similarity metrics such as cosine and MCES, but at the cost of reduced diversity. Con- versely, high temperatures lead to degraded accuracy and increased structural inconsistency. We do not employ beam search or advanced decoding control mechanisms; instead, decoding performed greedily (beam size 1), with syntax validity implicitly learned from training data. Although this setup is relatively
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simple, proves effective in generating syntactically valid and chemically plausible SMILES strings under constrained sampling.
Beyond temperature sampling, we further conduct a systematic study of decoding strategies to assess their impact on spectrum-to-structun generation. We extend our analysis from basic temperature sampling to a richer set of generation-control methods, including beam search, diverse sampling, and other commonly used decoding variants. These strategies are widely adopted in language generation to improve diversity, enhance global consistency, or mitigate mode collapse. To evaluate their behavior in our setting, we run experiments on the QM9s Jointly test subset with SpectraLLM, and compare 6 different decoding strategies. The configurations of each strategy are summarized in Table 13 and the corresponding generation quality metries reported in Table [4]
We observe that Qwen's 's default sampling configuration (temperature T 0.6, Top-k = 20, Top-p = 0.95) achieves the best performance among these off off-the-shelf strategies (Tanimoto 0.3355). In comparison, greedy decoding leads to a substantial drop to 0.2746, indicating that spectrum-to-structure generation in- duces highly non-convex search space where the absence of stochasticity easily traps the model in low- quality local optima. At the same time, further increasing Top-k or Top-p (i.c., injecting more randomness) typically degrades performance, suggesting that excessive exploration introduces noise and weakens struc- tural accuracy. Diverse beam search (DBS) attains slightly lower overall Tanimoto score than the default sampling, but it consistently produces multiple candidate structures with stable and chemically reasonable scores (around 0.18 0.19), exhibiting diverse yet conservative behavior that is particularly desirable in sce- narios without single ground-truth answer. contrast, Contrastive Search yields the worst performance, implying that its penalization of high-frequency tokens may harm the model's ability to represent regular patterns such as repeated functional groups or chain-like motifs. Importantly, even the best of these main- stream decoding strategies does not match the overall performance of our custom sampling setup, further demonstrating that spectrum parsing benefits from Komain-aware task-specific optimization of decoding.
In summary, the different decoding strategies expose clear trade-off off between exploration and accuracy. and our proposed configuration achieves the most favorable balance among them. This highlights the importance of task-specific decoding design for spectrum- ciirruum--t-stuuttre generation with large language models.
Table 12: Effect of Decoding Temperature on Molecular Structure Prediction Performance.
Temperature
Tanimoto
Cosine
MCES
Functional Group
Tanimoto(MACCS)
Fraggle
0.2
0.4872
0.5985
6.9840
0.8084
0.7070
0.6302
0.4875
0.5973
0.8103
0.6222
0.6
0.4740
0.5846
6.6784
0,5039
0.7022
0.6218
0.5660
7,0897
0.7980
0.6075
1.0 0.4367
0,5518
7,1902
0,7867
0.6752
0.6019
0.5352
7.3024
0.7840
0.5805
Table 13: Decoding configurations for SpectraLLM.
Decoding Setting
do sammle
tempersture
top.k
penalty alphaa
mam, num_beams
mum
mum_beam_ beam. groups
diversity penalty
length.pe nalty
Qwen default
0.6
20
0.95
Grandy decnding
Top sampling
0.95
50, 100)
Top-p (sueleus) sampling
0.95
(0.7, 0.9, 0.98)
Conirusivve seanch
false
(4.
(0.3, 0.5.
Diverso beam
Eilse
(4.6)
f0.3, D.S. 0.))
1.0
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Table 14: Impact of decoding strategies on spectrum-to-structun generation on the QM9s Jointly bench- mark.
Spectral.LM QM9s Jointly
Functional Group
Tanimato (MACCS)
Setting
Validity 1
Tanimoto
Cosine
MCES
Fraggle 1
Default
98.72%
0.3355
0.4560
4,9647
0.7934
0.5785
0.4117
Greedy
93.41%
0.1746
0.2782
7,9078
0.5835
0.3711
0.2881
Top-k
89.38%
0.1357
0.2283
10.2059
0.3129
0.2451
50
90.66%
0,1421
0,2363
9.8636
0.5052
0.3253
0.2495
89.74%
0.1392
0.2338
9.5745
0,5307
0.3172
0.2402
Top-p
0.7
94.51%
0.1591
0,2590
8.6744
0.5538
0.3469
0.2546
0.9
91.76%
0,1420
0,2410
9,2804
0,5128
0.3298
0.2489
0,98
91.39%
0.1346
0,2278
9.3838
0,5025
0.3130
0.2412
Diverse beamn
[4, 0.3]
96. 15%
0,1880
0,2939
8.0324
0,6106
0.3843
0.2966
[4, 0.5]
96.15%
0.1865
0,2926
7.2010
0.6051
0.3814
0.2947
[4. 0.7]
96.34%
0,1860
0,2923
7.1473
0,6102
0.3825
0.2913
16, 0.3]
95.97%
0.1908
0,29169
7.1126
0.6041
0.3872
0.2936
[6, 0.5]
95.42%
0,1885
0.2955
9.1881
0,6042
0,3835
0.2851
[6, 0.7]
94.69%
0,1872
0,2941
9.9826
0,6030
0,3808
0,2858
Contrastive search
10.3, 41
60%
0.1677
0,2714
8.0077
0,5512
0.3576
0.2744
[0.3.
95. 60%%
0.1724
0,2770
10,3884
0.5673
0.3629
0.2747
[0.5.
97. 99%
0,1593
0,2628
7,8598
0,5402
0.3500
0.2615
[0.5.
98.35%
0.1643
0,2659
8. 1965
0.5535
0.3617
0,2523
[0.7,
6.6
0,1507
0,2494
8.2458
0,4948
0.3346
0.2470
[0.7. 8]
87.36%
0.1608
0,2584
9.0210
0.5217
0.3488
0,2610
A.7 IMPACT OF FEATURE PEAK ON MODEL. SENSITIVITY
For the parameter choices in feature-peak extraction, we first systematically evaluate the impact of the peak- intensity filtering threshold on model performance. As shown in Table [15] when the threshold is increased from 0% to 1% and 2%, the model exhibits consistent improvements across multiple key metrics (e.g., Tan- imoto, Cosine, MCES, MACCS), with the 1% threshold achieving the best overall performance (Tanimoto 0.4404). This result indicates that moderate filtering of low-intensity peaks is crucial for suppressing background noise and highlighting chemically relevant peak patterns: a too-low threshold introduces many weak, non-discriminative spurious peaks, while a too-high threshold removes chemically important peaks and makes it difficult for the model to recover structural details. Notably, when the threshold is raised to 10% or 20%, almost all cructure-rrelted metrics degrade significantly, suggesting that the loss of chemically informative peaks is the main cause of the performance drop. Overall, the I 1%-2% range strikes the best bal- ance between noise suppression and information retention, and serves as the optimal and stable parameter choice in our & mapping.
In addition, this ablation reveals typical yet reasonable phenomenon: the model trained only on QM9s slightly outperforms the mixed-data SpectraLLM on the QM9s test set. This reflects the presence of negative transfer, and at the same time highlights structural trade-off between generalization and performance on a homogeneous dataset. QM9s highly clean, with regular peak shapes and strong spectral consistency, so a model fine-tuned purely on QM9s can more easily achieve a local optimum on its in in-distribution test set. In contrast, the mixed-training variant must simultaneously adapt to real-world spectra from MassBank, MassSpecGym, etc., which contain noisy peaks, distorted peak shapes, and missing peaks, forcing the model and robust spectrum spectrum-structtre mapping. Thus, this local degradation
to learn a more compromise-orice is inevitable cost of pursuing cross- cross-domain generalization and noise robustness juutt as shown in Table of the main text, Spectral. LLM's consistently superior performance on real experimental spectra demonstrates the value of this trade-off.
In the ablation on peak-width features (Table we further compare the model's behavior with and without wiak-width descriptors. For single spectral modalities, the influence of peak width exhibits clear differences.
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On IR data, including peak width slightly degrades performance, while on Raman data we observe the opposite trend: moderate peak-width information improves the model's ' ability to discriminate continuous band-like peaks. For the UV modality, the impact of peak width unstable, mainly because UV absorption spectra are inherently sparse and highly susceptible to noise, so peak width contributes relatively little to structure prediction. However, when all three spectra are jointly used as input ("Al1"), 1), the model with peak width consistently achieves better overall performance, suggesting that peak width is not universally
beneficial, but in multi suetii-pecttal fusion acts as an additional complementary structural cue that helps reduce representation bias across different spectral modalities.
Taken together, these two sets of ablations clearly illustrate the key design trade-off's in spectral preprocess- ing: (1) an appropriate peak-intenssty filtering threshold is decisive factor for model performance, while thresholds that are too low or too high will both damage the ppectrum-strucce
mapping; (2) the utility of and becomes more valuable under multi-spectral integration. These results not only validate the rationality of our preprocessing strategy, but also underscore that, for real- istic chemical applications, joint optimization of spectral feature engineering and model decoding strategies is indispensable.
peak-width information
Table 15: Effect of peak-intensity threshold on spectrum-to-strucct performance on QM9s Jointly.
Spectral.LM QM1% Jointly
Validity
Tanimoto 1
Cosine 1
MCES
Functional Group
Tanimolo (MACCS)
Fraggle
0%
99.45%
0.4149
0.5388
4,36
0.8743
0.6769
0.4696
99.82%
0,4404
0.5613
4.1606
0,8754
0.6978
0.4615
2%
99.635%
0.4411
0.5622
3,9908
0,8744
0.6917
0.4568
5%
99.08%
0.414
0,5357
4.3983
0.865
0.6748
0.4495
10%
99.08%
0.4071
0.53
4.4732
0.86
0.6683
0.4505
20%
98.90%
0.369
0,4988
4.7213
0,8513
0.6366
0.4441
Table 16: Effect of peak-width features and spectral modality on spectrum-too-structure performance on QM9s Jointly.
SpectraLLM QM% Jointly
Validity 1
Tanimoto
Cosine
MCES |
Functional Group
Tanimoto (MACCS)
Fraggle
All
98.53%
0.4213
0.5272
4.0046
0.8349
0.6394
0.4504
width
98.53%
0.4047
0.5195
4.0288
0.8342
0.6481
0.4397
IR
99.82%
0.1921
0,3120
7,5651
0.6599
0.4330
0.3194
peak width
99.08%
0.1459
0.2427
7,2218
0,4978
0.3345
0.2795
Raman
99.27%
0,2500
0.3786
6.4076
0.7317
0.5071
0.3681
-wo peak width
99.08%
0.2977
0.4223
5.8364
0.7597
0.5443
0.3883
UV.Vis
93.96%
0,0640
160
13,3928
0.2978
0.2129
0.0685
-wo peak width
99.82%
0.0812
0.1467
10.7138
0.3453
0.1967
0.2108
A.8 ROBUSTNESS OF 6 TO SPECTRAL NOISE
To investigate the sensitivity of the mapping & from spectra to text, as well as the impact of peak extraction on downstream performance, we conduct a series of controlled noise experiments. We first inject synthetic noise directly into the input spectra of the QM9s test set to study how different noise patterns affect the robustness of SpectraL.LM. Concretely, we apply three levels of simulated noise (mild, moderate, severe) to IR, Raman, and UV-Vis spectra, with parameterizations summarized in Table 17
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Table 18: Effect of modality-specifer simulated noise on spectrum spectrum-to-strucee strutuuee performance on QM9s Jointly. "I", "R", and "O" respectively represent IR, Raman, and UV-Vis.
SpectraL.LM QM9 Jointly
Noisy Strong
Functional Group
Tanimoto (MACCS)
Validity 1 Tanimoto
Cosine
MCES
Fragele 1
I+R+U
98.72%
0.3355
0,4560
4.9647
0.7934
0.5785
0.4117
mald
96.89%
0.3365
0.4659
5.3299
0.7819
0.5955
0.4011
P+R+U
moderate
0.3963
0.5212
0.3598
severe
87.18%
0.1712
0.2799
8,4433
0,5010
0,3774
0.2556
98.17%
6.4272
0.5073
0.3498
I+R*+U
moderatee
95.79K8
0.1608
0.2691
7 7,8891
0,5168
0,3735
0.2811
severe
0,2611
0.3719
mild 98.90%
0.4076
0,5302
4,4417
0,8744
0.6746
0.4624
I+R+U*
0.4554
severe 99.08%
0.3901
0,5153
4,5018
0.8547
0.6530
0.4478
96.34%
0,4592
P+R++U
moderate 89,56%
0.1546
0.2607
8.4029
0.4828
0,3653
0.2655
severe
10.1472
mild 98.53%
0,2550
0,3824
6.4526
0.6704
0,5060
0.3494
I+R"AU"
moderate
15%
7,9476
0.2787
severe 95.97%
0.0850
0.1535
9.5544
0,2074
0,2612
0.3704
97.07%
0,3431
0.4719
5,2047
0.8098
0.6091
0.4124
P+R+U*
moderate 98.35%
0.2796
0.40448
1825
0.6789
0.5240
0.3682
severe
mild
96, 70%
0.2205
0,3473
6.9820
0.6272
0.4676
0.3154
0.3505
0.2548
severe
39.01%
0.0792
0,1427
10,2981
0.3342
0,2508
0.1718
Table 17: Noise configurations for simulated IR, Raman, and UV-Vis spectra at three severity levels.
IR
Raman
UV-Vis
rel, moise sigma=0.04
noise sipma=0.02
baseline amp=0.03
rel noise sigma=0.01 huseline
sameinne_mmp00011
peorescence_yppeeeee
Mild
sigma=0.5
fluorescence strength=100
sigmu=3.0
spike prot=0.001
conv sigma=1 O
spike prob=0.001
Ireq jilterr00
spike fikkee.rob=00022
ftee.iiter=:0111
freq jitter=0.2
noise sigma=0007
mosse xigmu=00.05
baseline amp=0.08
rel moise sigmu=0.0M
baseline amp=0.03
buseline amp=0.0 sis.mmmmm04000 spike -000 freg, jitt__itte=-.3
fluorescence strength=13
Moderate
www.ssigaa...55 1.5 spike 0000 freq, fitq.iitterr..3
conv sigma=2.00
prokepprob=0000
fiter-1
noise sigma=00.10
nolioi sigma=0..08
rel noise sigma=0.07
baseline amp=0.15
buseline.ap=-0.08
sigma=330030
fluorescence 1trrsscencc_stennghe111...
buseline
sigma=5.0
spike_prob=0.01
conv.sigma=2.5
spike_prohw0h
spike_prob=0.01
frte_jiter=00.6
froq,jitter=0.6
freg_jiter=0.66
We add noise to multi-spectral prompts that jointly contain IR, Raman, and UV-Vis. In total, there are 21 possible constructions: (i) adding one of the three noise levels to exactly one spectrum (e.g., noisy IR, clean Raman/UV); (ii) keeping exactly one spectrum clean while adding the same noise level to the remaining two; and (iii) adding the same noise level to all three spectra. For each setting, we re-construct the noisy test using dynamic filtering threshold, set 1% of the maximum spectral intensity. Table 18 reports a representative subset configurations, where a superscript denotes the spectrum to which noise applied.
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Overall, the results show a clear monotonic relationship between performance degradation. noise intensity, and the number of corrupted spectra. When only mild noise is injected into single modality (e.g., noisy IR with clean Raman/UV), the drop in all metrics is negligible. For instance, the Tanimoto score changes only slightly from 0.3355 (clean I+R+U) to 0.3365, 0.2205, or 0.2487 under different single-modality noise configurations, indicating that the model does not catastrophically fail when one spectrum deteriorates, but instead degrades gracefully. Moreover, even when moderate or severe noise (corresponding to heavily dis- torted spectra rarely observed in practice) is applied to a single modality, the model still maintains high validity and non-trivial Tanimoto scores. This suggests that SpectraLLM effectively exploits the remaining clean, orthogonal spectral information (e.g., Raman and UVsV to compensate for the corrupted channel and continue performing meaningful structural inference.
At the same time, although Tanimoto similarity decreases under stronger noise, the corresponding increase in MCES (Maximum Common Edge Subgraph) distance is relatively mild. In other words, when the model fails, it tends to predict molecules that preserve a similar core scaffold to the ground truth rather than produc- ing random structures. This structural conservatism is an important form of predictable failure and provides useful interpretability for chemists.
As the noise level increases from mild to moderate to severe, or as the number of corrupted modalities grows from one to two to three, all metrics continue to decline. For example, when all three spectra are severely corrupted, validity drops 57.88%, the Tanimoto score falls to 0.0749, and functional group accuracy decreases to 0.3348. This pattern is fully consistent with intuition: the mapping & from spectra to textual peak representations the sole information interface between the raw data and the language model, so stronger perturbations or loss of peak fidelity directly weaken the chemical constraints available to the LLM.
These observations support three key conclusions:
1. The peak-extraction eetracctonnbbsed mapping & is robust under realistic noise conditions.
2. As spectra become increasingly corrupted, the model degrades in a predictable and graceful manner rather than collapsing abruptly.
3. The practical performance boundary of SpectraLLM determined by simultancous severe disrup- tion of multiple modalities, rather than by a single low-quality spectrum.
A.9 MODALITY CONTRIBUTIONS AND MULTISPECTRAL SYNERGY
In Table 4. we have already conducted a preliminary ablation from single-spectrum inputs to multi-spectral joint inputs the test data, and observed that joint inference with multiple spectra indeed leads to better performance. To perform a more systematic spectral-modality ablation, we further train a separate model for every modality combination in the QM9s dataset, so as to avoid the distribution shift that may arise from simply masking inputs. Concretely, QM9s contains three spectra, IR, Raman, and UV-Vis; we train one model for each single-spectrum setting, one for each pairwise combination among IR. Raman, and UV-Vis, and one for the threc-spectrum combination, yielding in total 7 models, which are evaluated independently. The results are shown in Table 19 The performance differences among single-modalls the baseline contribution strength of each spectrum to the structure recovery task. Among them, the Raman- Only model performs the best (Tanimoto = 0,3251), far exceeding IR-Only and UV-Vis-Only, which is consistent with the strong structural constraints Raman provides on carbon skeleton vibrations and symmetry modes. In contrast, UV- Vis used alone carries limited information, making it difficult to provide stable bond- order or functional group constraints; consequently, it performs the weakest on almost all structural metrics.
models first reveal
When we further examine the bimodal combinations, the performance gain of IR+Raman is the most sig- nificant (Tanimoto 0.5320), achieving more than 60% relative improvement over the best single-modalit Raman model. This strong synergy indicates that the two spectra offer highly complementary structural information: Raman mainly encodes backbone and symmetry, while IR is particularly sensitive to polar
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functional groups and local environments. When both are provided as input, the model can jointly lock down the global molecular framework and the positions of key functional groups, substantially shrinking the uncertainty of the chemical space. Other bimodal combinations (such as Raman+UV- or IR+UV-Vis) Vis) also yield consistent gains, but the magnitude is smaller than that of RRmanm further indicating that the marginal contribution of UV-Vis to structure recovery is weaker than that of the two vibrational spectra.
Interestingly, although the three-spectra input (IR UR-=Raman-UVVVis provides the most complete coverage, its performance slightly worse than that of the RR+Raman combination. For example, its Tanimoto score reaches only 0.4053, clearly below the 0.5320 achieved by IR+Raman. This phenomenon suggests that, on clean synthetic spectra, UV-Vis information does not always yield additional benefits and can in some cases introduce noise redundant cues, increasing the burden of aligning multiple spectroscopic signals. In other words, incorporating more spectra does not guarantee monotonically improved performance; rather, the effectiveness of fusion depends on the complementarity and information correlation among the spectra being combined.
Table 19: Effect of IR, Raman, and UV-Vis modality combinations on spectrum-to--trratuur performance on QM9s.
SpectraLLLM Q9MS retrained
Validity
Tanimoto
Tanimolo (MACCS)
Cosine
MCES
Functional Group
Fraggle
IR-Only
100.00%
0,1474
0.3436
0.2462
7.1245
0,5309
0.2685
Raman- Only
98.90%
0.4290
0.7986
0.3958
IVV-Vi-Onll
100.00%
0.0777
0.3081
0.1407
10:3874
0.2137
IR-UVANS
633
0,2088
0.4471
0.3280
6.0551
0.6791
0.3333
RamaneU VV-is
99.45%
0.4594
0.6718
0.5699
4,0405
0.8431
0.4561
IR IR-Rantan UV-N0i
98. 15.
0.4053
0.6501
0.5202
3.9926
0.8370
0.4479
On the other hand, we summarize the train-test transfer performance across different spectral combinations in Table to further analyze the generalization patterns and spectrum robustness of multi-spectral models. Overall, when the test spectra match the spectra seen during training, the model typically achieves the highest Tanimoto similarity; once the input spectra differ from those observed during training, performance drops sharply and can in some cases approach random. This indicates that the inverse mapping from spectra structures does not naturally share a modality-invarian latent space; instead, the mapping heavily depends on spectrum-specific structural constraints, which also explains why single-spectrum models generally fail under cross-spectrum transfer.
At the same time, training with multiple spectra yields substantially stronger robustness under cross- spectrum testing. For example, the model trained on IR+Raman not only reaches the highest Tanimoto score (0.532) when evaluated on the same spectral pair, but also remains significantly better than single-spectrum models when tested with only IR or only Raman as input. This suggests that multi multi-spectral training helps the model acquire a more unified and adaptable structural prior, enabling it to maintain stable prediction capability even when certain spectra are absent. A similar pattern is observed for the IR RR+Raman+UV-- model: joint training across all spectra provides a more complete representational basis, allowing the model partially recover structural information under
mismatched-speetrum conditions.
However, this robustness is clearly directional. When evaluated on UV-Vis alone, all regells-egarrlless of training spectra- exhibit limited performance, indicating that UV-Vis inherently provides weaker con- straints for structure recovery and that its predictive utility cannot be substantially improved via transfer from other spectra. This again reflects the unequal information density across spectroscopic signals and explains why most multi-spectral gains originate from IR and Raman rather than from simply increasing the number of spectra.
Overall, the transfer matrix supports two key conclusions: (i) single-spectrum models rely strongly on spectrum-specifcc cues and struggle with cross-spectrum generalization; (ii) multi-spectral training substan-
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tially improves robustness, allowing the model retain structure recovery capability under diverse spectral combinations and missing- conditions. These observations align with the eeependent-training ab- lations discussed above and further highlight the necessity and practical advantages of building multi- multi-spectral models that remain functional even under incomplete spectroscopic inputs.
Table 20: Train-test modality transfer matrix QM9s (Tanimoto similarity).
Test train
IR
Raman
UV.Vis
IR+Raman
IR+UV-Vis
Raman+UVV-Vs IR+Raman+UVVVis
IR
0.1474
0.0674
0.0605
0.2012
0.2012
0.0629
0.1921
Raman
0.0669
0.3251
0.0721
0.3951
0.0639
0.4112
0.2977
UV-Vis
0.0592
0.0632
0.0777
0,0579
0,086
0.0877
0,0812
IR+Raman
0.1372
0.1209
0.0671
0.532
0,1468
0.1319
0.4231
IR+UV-Vis Vis
0.1379
0.0642
0.0704
0,1803
0,2088
0.0645
0.1962
Raman+UV-Vis
0,0671
0,2746
0.0741
0.3687
0,0786
0,4594
0,3353
IR+Raman+UV-VV UV-Vis
0.1373
0.1161
0.0706
0,4922
0.17
0.1305
0.4213
A.10 IMPACT OF METADATA AND EXPERIMENTAL CONDITIONS ON STRUCTURE PREDICTION
Experimental metadata, such as spectral acquisition conditions, sample descriptions, or collision energies. often play critical role in both spectral patterns and molecular structure prediction. To quantitatively assess the model's 's dependence on such metadata, we design an ablation setting denoted as "-wo exper r i iment" " Specifically, we remove all experimental metadata from the prompts, as shown in Table and then retrain the model on this new prompt corpus, and then evaluate it on each single-spectrum and multi-spectral test set. The results are reported in Table Overall, the full model with experimental conditions consistently outperforms the variant without them, with even larger gains observed under cross-spectrum combined in- puts.
This effect is particularly pronounced for mass spectrometry tasks. For example, on the MassBank dataset, the full model achieves an average Tanimoto similarity 1306, which drops to 0. 1259 when experimental metadata are removed. The underlying reason is that the core of mass spectrometry lies in fragmentation patterns, while experimental parameters such as collision energy directly influence the formation of frag- ment peaks and their intensity distributions. SpectraL.LM can encode these experimental conditions into the prompt and, during autoregressive generation, jointly condition on both the spectral peaks and the ex- perimental settings. For instance, when the prompt indicates low collision energy, the model expects weaker, low-mass fragment peaks, whereas higher collision energy corresponds to more intense, deeper fragmentation peaks.
For multi-spectral joint training tasks, experimental metadata similarly act as a bridge, enabling the model share contextual information across different spectral types. When IR and Raman spectra are provided jointly, for example, the model can exploit their respective characteristic peaks while using metadata to per- form conditional reasoning over spectral patterns, thus improving the accuracy of the joint prediction. This suggests that iaguage-bbsed metadata prompts not only compensate for potential precision loss introduced by discretizing spectral peaks but also substantially enhance the model's reasoning ability across spectra and experimental conditions.
Taken together, this series of ablations demonstrates that SpectraL.LM successfully integrates non-numerical context into the structure prediction pipeline. By expressing experimental conditions in natural language, the model can map diverse spectra and measurement parameters into a shared semantic space, offering a new and general computational paradigm for complex spectral data analysis. This highlights the potential of multi-spectral large language models for scientific data reasoning, establishing a powerful framework for cross- cross-spectral analysis and complex data interpretation.
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Table 21: Prompt Design Examples (Conditional vs. Unconditional)
Conditional Prompt
"prompt": "Mass spectrum data: (mmzs\\:
1991.05,
29,..2..........2244..... 11\", \iintesiities type: [M+H
\":
0.142, (.
[+AnInstrument. orbitrapphnooliisionn energy: 30.0 eV\nPlease predict the compound's SMILES representation with LESS THAN 1000 characters thinking. The final output must strictly begin with "#MMILES:", "response": "ASMILES: cC(=0) (CC1=CC=CC=CI) (=CCC 02) "system": "You are professional mass spectrometry analysis model. Your task is to predict the compound's SMILES based the given mass spectrum data. The input contains m/z and intensity 1ist, well experimental conditions (such as adduct type, instrument type, collision energy, and precursor m/z). Please predict the compound's SMI LES representation.
Unconditional Prompt
"prompt": "Mass spectrum data: (\ (\"mzs\"; \"91.05, 125.02, 25..
\"intensities
\": 1"0.245, 1.0, 0.00,,00800000 0.35,,,05,,,,000.. 0.04,, 0.95,0.10 0.142,0.733 0.735>") AnPlease predict the
compound' SMILES representation with LESS THAN 1000 characters thinking The final output must strictly begin with 'ASMILES:",
"response"; "#SMILES: (=())N[[C)))] "E (=0))) 02)
"system": "You are professional mass spectrometzy analysis model. Your task is to predict the compound's SMILES based the given mass spectrum
data. The input contains m/z and intensity 1ist. Please predict the compound's s SMILES representationn"
Table 22: Effect of removing experimental metadata from the prompt on spectrum-to-structuree prediction across datasets.
Spectral.IMM Qwen3-32B
Tanimoto (MACCS)
Functional Group
Validity
MCES
Cosine
Fragele 1
QM9s Jointly
99.45%
0.4586
0.5639
3.7164
0.8659
0.6785
0.4621
-wo experiment
99.82%
0.4408
0.5477
3.8339
0.8459
0.6608
0.4607
Multi Jointly
99.57%
0.5750
0.6638
5.4829
0.8448
0.7700
0.6740
-wo experiment
98.94%
0.5527
0.6446
5.7462
0,8279
0.7524
0.6658
MasaBank
98.46%
0.1306
0.2264
0.4540
0.3848
0.3201
-wo experiment
98.62%
0.1259
0.2215
0.4336
0.3791
0.3092
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A.11 ZERO-SHOT LIMITATIONS OF PRETRAINED LLMS ON SPECTRAL INTERPRETATION
To rule out the possibility that the base large language model may have indirectly acquired chemical-structure knowledge during poltetial causing knowledge leakage or spurious performance gains-we conduct a strict zero-shot evaluation on the pretrained Qwen3-32B model without any spectrum-related fine-tuning. Specifically, we directly feed spectral text prompts into the base model and evaluate its structure generation ability on the QM9s Jointly and Multi Jointly test sets. The results are presented in Table 23
Overall, the unfine-tuned model is unable recover any meaningful structural information from spectra. On QM9s Jointly, the Tanimoto similarity is only 0.0308, and all structural metrics remain near random. On the more heterogeneous Multi Jointly set, performance degrades even further: all structure-recovery metrics (Tanimoto, MACCS, Fraggle) collapse to O, and distance-based metrics such as MCES indicate severe deviation from the ground truth. This shows that the base LLM essentially lacks the ability to map spectral signals to molecular structures, yielding chemically inconsistent outputs.
These findings indicate that although a pretrained LLM may have encountered chemical text or isolated molecular representations, such knowledge does not transfer to the peecrum-to--strucuu inverse problem. other words, the base model does not understand spectra and cannot exploit pretraining statistics for struc- ture recovery. The performance gains of SpectraLLM therefore stem almost entirely from opectrum-tex: alignment during fine-tuning rather than retrieval or memorization pretraining knowledge, Consequently, ppectrum-to-struutuu generation is not a conventional language modeling task that benefits from corpus- level generalization, but a cross-modal inverse problem that must be learned through dedicated training.
Table 23: Zero-shot performance of the pretrained Qwen3-32B model on spectrum tpectrum-to-struutuue prediction.
Spectral.LM Qwen3-32B
Tanimoto (MACCS)
Functional, Group
Validity
Tanimoto
Cosine
MCES
Fraggle
QM% Joindl
0.92% 0.43%
0,0308 0.0000
0.1963 0.0000
0.0595 0.0000
7,6000
0,2567 0.0000
0.0157
Multi Jointly
29.0000
0.0800
A.12 TRAINING AND INFERENCE EFFICIENCY OF SPECTRALLM
To comprehensively evaluate computational efficiency, we compare lightweight spectrum-specific mod- els (IR-to-Structure, Spectra2Structure, NMR2Struct, Spec2Mol) with the large-scale multi-spectral Spec- traLLM. As shown in Table 24 we report training compute in FLOPs (1025), using Spectra2Structure as the 1.0x baseline. Lightweight models incur relatively modest training costs; for example, IR-to- Structure requires 2.81x the baseline FLOPs, while NMR2Struct requires 53.48x. In contrast, fine-tuning Spec- traLLM on multi-spectral data demands 429,655.22 x 1 FLOPs=approxine approximatay 4.3 x 10 the base- line line-primarily due to its 32B parameters and long multi-spectral input sequences.
IR-to-Structure
For inference (Table 25). we measure per-sample latency (ms/sample) and throughput (samples/s), using Spec2Mol (6.02 ms/sample) as the 1.0x baseline. Spectruu-speciffic models achieve millisecond-level la- tency suitable for high-throughput use. In contrast, SpectraLLM exhibits an average latency of 5.78 s/sam- ple, corresponding to a 959x overhead. This slowdown stems mainly from autoregressive decoding and the large parameter count. We also observe tra typically require more decoding steps than IR, reflecting differing spectral complexity. Nevertheless, Spectral.L.M delivers unified multi-spectral reasoning and significantly higher accuracy than its lightweight counterparts- it suitable for low-throughput, high-precision analytical scenarios.
differences; for example, Raman spec-
Overall, while SpectraLLMM incurs substantially higher computational costs compared to spectrum-specific models, these costs offset by improvements in cross cross-spectral generality and structural prediction accu-
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racy. For large-scale spectral analysis, practitioners can balance efficiency and accuracy based on application needs, with lightweight models remaining preferable for high-throughput settings.
Table 24: Training compute and wall-clock cost for SpectraLLM and modality specific baselines.
Model
(train/val)
#Samples
used
epoch
FLOPs (10")
Wall-ctock Time (h)
Training Cost (relative)
QMPs (IR unly.
IR-4o Structure
105,019 11,876
3050
for IR
9:25 h for
Kamin-only.
Spectau2Stmctuee
105.019 / 11,676
1x 100 80G38
800
334 for Raman
0.0914
22.71 h for Kamna
100x (haselinc)
UV-only)
157 for UV
10.68 h for UV
Multasedal Spectrossopic
(NMR-unly)
NMR2Strmer
643,604 / 71,512
Ix RTX 3090 34C
256
500
4888
h4
53.48x
Combined
SpectraLLM
2,367,865
H100 80G
429,655.22
100h
4. 4.70
Table 25: Inference latency, throughput, and relative cost across LL and baseline models.
Datasets
Model
#Sample
Inference lateney
Inference Cost
Avg Lateney (ms//ample)
Throughput (sampics/s)
Params (B)
QM9s
IR to Structrre
13,062
16.06
62.27
0.069
2.67x
(IR-only, Raman-only, UV-only)
Spoctra2Structure
13,062
133.46
0.024
1.24x
Multimodal Spectroscopic
NMR-only
NMR2Struct
79287
119.05
8.4
0.0136
19.78x
SyesSye
Spec2Mol
16264
73.03
13.15
0,0518
12.13x
Mass-only
Diffmss
16264
2700.97
0.37
0.0849
448.67x
MassBank
Spec2Mol
7905
46.32
21.59
0.0518
7.69x
Mass-only
Diffins
7905
873.97
1.14
0.0849
145. 18x
Multimodal Spectroscopic
Spec2Mol
79287
6.02
166 20
0.0518
L.0K (baselinc)
Mass-only
Diffms
79287
2800.72
0.36
0.0849
465.24x
depends subects
Combined
SpectraLLM
5778.39
0.17
32
959,86x
A.13 BAD CASE STUDY
In addition to the quantitative ablations, we performed qualitative stress tests by manually deleting chemi- cally meaningful fragment peaks from single-spectrum mass spectrometry (MS) examples. This approach allowed to assess how the model performs under conditions where key spectral features missing, test- ing its reliance on spectral information versus its ability to infer molecular structures. Two representative cases are shown in Fig 5 each illustrating situation where important fragment peaks removed, leading to significant changes in the model's 's predictions. In the first case, the ground-truth molecule is the branched ester CC(C))C(-O)CCC), which exhibits a prominent fragment at m/z 43, corresponding to an isopropyl cation. This fragment is a hallmark of esters containing isopropyl groups When this peak is removed, the model fails to predict an ester structure, instead generating straight-chaie aldehyde CCCCCC=O, which retains a terminal carbonyl group but lacks the ester motif and the branched isopropyl substituent. This demonstrates that the model is heavily reliant on this particular fragment for its predictions, underscoring the importance of specific fragment peaks in its reasoning process. In the second case, the ground-truth molecule C (N. w...-dimmthyllaetamiid also produces strong fragment at m/z = 43, indicative of the acetyl cation [CH, After removing this peak, the model predicts [N=]=[N"]=N[[C" @ @ @HJINI, nitrogen-rice heterocycle that fits the overall mass of the molecule but loses the oxygen-containing fragment characteristic of m/z 43. This suggests that the model has learned associate certain fragment peaks with specific functional groups and substructures, and when key peaks are missing, its predictions shift accordingly.
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Case 1: Ester-Diagnostic Peak (m/2 2 43) Removed
Case 2: Acetyl-Diagnostic Peak (m/2 = 43) Removed
CCCCOC--DXICCC
CCCCCC=
CNJCCCC)=
[M/]-NN)-NC[CeeeHGGG
Ground Truth: CC(C)CC(=))(CC
Ground Truth: CN(C)C(C)
Predicted: CCCCC-=0
Predicted: [N-H:N+]=NC[C@HHII HJICNI
Figure 5: Qualitative counterfactual examples obtained by deleting diagnostic MS fragment peaks. Left group: Ground -rrnth molecule CC(C)OC(=O)CCCCC (left) and SpectraLLMM prediction CCCCCC=O (right) after removing the ester-diagnottic m/z = 43 isopropyl cation peak from the input spectrum. The model preserves terminal carbonyl but no longer predicts an ester or branched isopropyl substituent. Right group: Ground truth C (left) and prediction [N=]=NN"= HJICN1 (right) after removing the m/z 43 [CH,CO]* acetyl fragment. The predicted structure matches the overall mass but eliminates oxygen-containing fragments consistent with m/z 43.
These controlled failures provide important insights into how SpectraLLM processes spectral data. The model's "incorrect" predictions are random- they systematically avoid the specific functional motifs (isopropyl ester, acetyl carbonyl) that correspond to the fragment peaks we have artificially removed, while retaining other spectral aspects such as mass and heteroatom composition. This behavior indicates that Sp relies chemically interpretable spectral features rather than merely exploiting superficial dataset biases.
In the next case study (Fig6)) we performed systematic analysis of failure modes across QM9S, MassSpec- Gym, MassBank and the Multimodal Spectroscopic dataset to understand where SpectraLLMM still struggles and why these failures depress structure-Ievel metrics (c.g. Tanimoto). The empirical bad cases cluster into three dominant categories that reflect fundamentally distinct sources of uncertainty: intrinsic spectral ambiguity among isomers, high structural complexity with rare congested fragmentation patterns, and small fraction of output errors traceable to SMILES formatting or decoding failures. Below we examine each category in turn and explain how the spectroscopic physics, dataset statistics, and sequence-generaaion mechanics conspire to produce the observed prediction errors.
The largest class of failures originates from intrinsic ambiguity: many constitutional isomers and positional isomers produce highly similar spectral fingerprints, and mass fragmentation in particular cannot uniquely disambiguate certain rearrangements or alkyl-chain permutations. Examples from Casel-Cases illustrate this: spectra from structural isomers (c.g., C2H14 isomers, positional alcohol isomers, or different substi- tution patterns on an aromatic ring) frequently share almost identical sets of fragment masses and vibra- tional features, so multiple chemically plausible SMILES strings yield near-identical spectral likelihoods. these situations the model often outputs a chemically valid isomer that spectrally consistent with the measurement but not the reference structure, which directly lowers Tanimoto despite generating reasonable chemistry. This limitation is not a model bug per se but a property the inverse problem: the measurements lack the information necessary for unique reconstruction. As result, performance metrics that require exact structural agreement are conservative estimates of practical utility in these regimes.
sicecules-polycyeer frame- works, heavily functionalized or heteroatom-richh species, and longer fused-ring or highly polar scaffolds (Cases 4-6). These compounds incur two compounding difficulties. First, their spectra intrinsically more
A second, qualitatively different failure mode arises for high-complexity
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Intrinsic Isomer Ambiguity (QM9s Dataset)
Case:: Tautomerism
Case:: Ring-Chain Isomerism
Casc3: Functional Group Isomerism
NHz
CNTCI-OCCCC1-
N-CIOjCPCCI-NIO
c=cloccc(nH]1
Cctcoa[Ojc1N
CcInan/Cx10
NO
Nol
NFCC1O)CNCC10
NeCCICN-CHI
coccocNNi110
Cnlemettc
Tash: CXCCC- Colo Mio
Trob: N=cloma/ulij
Geound Torh: CeleastOyoN Predicted Ccloo
Groin Tauhh Co1 Predicted Culonee1CO
Predictel,
Predicted: NeCCICN
High-Complexity Structures (QM9s MassSpeeGym)
Case4; Complex Multi-Loop
Cuse5: Highly Polar, Heteroaton- Rich Straetures
Casoli: Highly Complex Polycyelic/ Poly Polytonctional Structures
CC ERERE POTTEEEEEEEEEEEEC BOCTDGEOCION ENCEEICCEER OHKCII
D+C1CIOCaC1C23
CC1COCC01
O-C1CICBCSS12
INC C--------------------
CROBO
COOOCROXOOOOO
GINICI10001
saj2001 Praficted:
Predicte: CICXC0CCC12
Predicied: CUOCTOCIC
Predicted:
neecosct--lEj-aee 201-00
CI-0C--0--IA CIRCRSHHIIIIOOOO
Predited:
DIC-CO DC--0-00- -10000
Figure 6: Bad Case Analysis of Spectral.LM Predictions. The upper panel shows had cases from intrinsic ambiguity: Isomers (e.g., CoH14 isomers, alcohol positional isomers, aromatic substitution patterns) have nearly identical spectral features, leading the model to predict chemically reasonable but non-unique isomers (all cases from QM9s). The lower panel shows bad cases from high-complexity structures: Errors are more frequent for fused polycyclic molecules, long conjugated systems, or heteroatom-rich structures (case4 from QM9s; case5 6 from MassSpeeGym),, which are underrepresented in training data and suffer from peak congestion/shift.
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complex: dense peak forests, overlapping isotopic or adduct signals, strong neutral losses (e.g. phosphate, sugar moieties), and multiple low-abundance fragments that vary strongly with ionization and collision- energy conditions. Under typical MS/IR acquisition the diagnostic peaks that would uniquely indicate a particular scaffold are often missing, suppressed, or convolved with neighboring signals. Second, these chemotypes are under-represented in training corpora, so the model has limited prior experience to draw upon; rare stereochemical motifs and subtle regiochemical differences are therefore not strongly encoded in the learned latent space. Together these factors cause systematic misplacement or omission of key substruc- tures (missed phosphate/sugar groups, incorrect ring fusion patterns, wrong stereochemical assignments), leading to large topological discrepancies captured by MCES and low fragment-level metrics like Fraggle and functional-group recall.
third category consists of truncated outputs. These failures typically occur for long or highly branched predictions where ring indices or parentheses are misaligned during autoregressive decoding. While such errors are largely orthogonal the model's spectroscopic reasoning capabilities, they still impact aggregate statistics because invalid or malformed SMILES are excluded from fingerprint comparisons and therefore reduce measured Tanimoto and MACCS rates. In practice, improving decoding robustness (e.g., constrained decoding, grammar-awant postprocessing, or validity-guider reranking) mitigates portion of these cases without changing the under- lying spectroscopic model.
or decoding errors that produce syntactically invalid or
Taken together, these three categories highlight where future work should focus to close the gap between spectrally plausible predictions and exact-structure recovery. Intrinsic ambiguity implies hard information- theoretic ceiling that can only be relaxed by adding orthogonal measurements or experimental metadata; high-complexity failures call for targeted data augmentation, domain- -wwre pretraining on rare scaffolds, and catrument-conditionn modeling to account for acquisition-dependent fragmentation; and decoding errors can be reduced through constrained generation techniques. Understanding these distinct failure modes helps explain why single-number metrics such Tanimoto remain modest in some regimes: the bottlenecks mixture of irreducible measurement ambiguity, dataset coverage, and generation mechanics.
A.14 GENERALIZATION TO REAL EXPERIMENTAL SPECTRA
To rigorously assess the model's ability to generalize beyond simulated spectra, evaluate SpectraLLM under two complementary settings involving real experimental data: (1) out-of-distribution reconstruction MassSpeeGym and MassBank after removing these datasets entirely from training, and (2) direct zero- shot transfer to NIST IR and UV-Vis spectra. Together, these experiments reveal the extent to which multi-spectral language modeling enables robust molecular structure prediction from real-world measure- ments, where spectral noise, instrument variability, and distributional shifts significantly challenge purely iouulation-trained models.
We first re-train SpectraLLM using only simulated spectra from QM9s and the Multimodal Spectroscopic Dataset, explicitly excluding all MassSpecGym and MassBank molecules. The retrained model is then evaluated on the original real-world test sets. Table 26 reports detailed comparisons against DiffMS and Spec2Mol under both trained and untrained states. Across both real-world datasets, SpectraLLM consis- tently outperforms prior methods in all structure-sensitive similarity metrics, particularly Tanimoto simi- larity, MACCS-based similarity, Fraggle similarity, and functional-grouy accuracy. The performance gap most pronounced in the untrained setting, where SpectraLLMM-a despite never observing these datasets reconstructs molccular structures with significantly higher fidelity than both DIfIMS and Spec2Mol. These results indicate that SpectraLLMM learns a spectra-to-structte mapping that is not tightly bound to the statisti- cal properties of any specific dataset, enabling effective generalization to experimental spectra with unknown acquisition settings and noise characteristics.
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Table 26: Model Performance on masbannkmmssspecgynn Experimental Spectra.
Functional Group
Tanimoto (MACCS)
Setting
State
Model
Validity
Tanimoto
Cosine
Fraggle 7
Diffims
57. 16%
0.139705
0.242187
0,489004
0,430529
0,353878
MassSpecGym
trained
Spectral I.M IM
Spec2Mol
99. 74%
62.86%
01884861
0.1537
0.15III5
0,2565
0.31T157
0.5016
0,270927
0.4735
0,206588
0.362
untrained
Spectral
92.97%
0.1139
0,2039
0.4354
0.4005
0.2988
trained
Lp
Dittmms
98.46%
23.63%
0.074269
0.1306
0. 0,103735
0,2264
0.179588
0.454
0,136596
0,3848
0,153949
0,3201
MassBank
Spec2Mol
71.63%
0.085741
0,138661
0.299915
0.241751
0,214962
untrained
Sp
92.79%
0.1125
0,2019
0.187
0,3612
0,2922
To further evaluate zero-shot transferability, collected all molecules from NIST toolkit Fl and applied the same QM9-style filtering protocol used in prior literature Kanakala et al. 2024). Molecules containing elements outside C. N, O, H. were removed, resulting in a pool of 73,354 unique QM9-like structures. Among these, 7,177 had downloadable IR spectra and 1,568 had UV-Vis spectra. After removing molecules overlapping with our training set-either exactly or via high MCES similarity-we obtained two challenging, strictly out-of-distribute test sets: 4,024 molecules- IR pairs and 91 molecules- UV-Vis pairs. Statistics are summarized in Table 27
using the nistchempy
Table 27: Statistics of NIST Dataset (SMILES and Spectral Availability).
Total Molecule
QM9s-like Molecule
Download Spectral
NIST
Available
SMILES
Spectral
IR
144795
73354 73354
7177
4024
4024
UV-Vis
144795
1568
911
911
Across both IR and UV-Vis regimes (Table 28 chemically faithful structures than all baseline systems, even under the strict zero-shot setting. Although validity remains uniformly high across methods, the structure-ad aaweee and functional-group
SpectraLLM exhibits a markedly stronger ability to recover
MACCS, Fraggle, cecturacc--ossiteennttte distinguish SpectraLLLM from unheedding-drived baselines. On the NIST-IR benchmark, SpectraLLLM achieves Tanimoto similarity of 0.0727. representing improve- ment of more than 5x over the strongest baseline. This gain is mirrored in MACCS and Fraggle similarity, each reflecting substantially closer fingerprint- and fragment-level agreement with ground-truth structures. A similar pattern holds for the NIST-UV subset. While cosine similarity among all models remains relatively close- likely reflecting the smoother, low-frequency nature of UV-Vis spectra-Spectraale continues deliver the highest structure-level fidelity across all fingerprint- and fragment-basee metrics.
Table 28: Model Performance NIST-IR/UV Experimental Spectra.
Functional Group
Tanimoto (MACCS)
Datasets
Model
Validity
Tanimoto
Cosine
MCES
Fraggle
IR-to-Structure
98
0.0095
0.0657
33,4585
0.1309
0.0699
0.0720
NIST-ir
Sprctr2Structure
99.31%
0.0128
0,0769
29.5700
0.2104
0.0923
0.1267
Spectral I.M
99.43%
0.0727
0.1368
22.3229
0.3191
0.1964
0.2176
IR-to- ti-to-SStuctree
99.12%
0.0673
0.1147
25.9537
0.2799
0.1529
0.1101
NIST-av
SpectraZStructure
99.37%
0.0726
23.3650
0.3058
0.1407
SpectraLLM
99.36%
0.0744
0.1381
23.0612
0.3231
0.2084
0,1411
hhttps//webbookk.isttt. wist. gww..cccttts.oo///ssssssrrr//
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Across both evaluation settings, SpectraLLM demonstrates strong generalization, robustness to spectral noise, and cross-dataset transferabilityy. We attribute this to its unified language-basee based representation, which jointly encodes multi-spectral information and molecular structural priors. Compared architectures op- timized for specific spectral modality or dataset, our model is better equipped to handle previously un- seen experimental distributions, distortions absent from training data, and novel molecular scaffolds not covered in synthetic datasets. Overall, these results indicate that SpectraLLMM serves highly versatile spectrum-to-strrutun model, effectively transferring the knowledge learned from synthetic spectra to real experimental measurements.
Table 29: Example prompts for multi-spectral reasoning.
Multi-spectral Prompts
Given multiple spectra, they Infrared Spectrum (Wavemumbers:3599.8.5555
3314,97,3202. 11,.3114.11441441111 1998.51,1444.,,7220 int..//ie.ttttttmsiiieess::s
0.65,0.76, Widths:98. 69, 16.,,,7.31,,,,,,, 10.,,,
42.99,142..11, 13,67.04), .04). Raman spectroscopy
(Wavenumbers:2913 2,1000,2926.. 0,298 10.3688.00 0. Intensities: 0.75,1.0, 1.0,0.66 Widths:4.39, 02.34.24.45.10.08 Ultraviole- spectroscopy (Energies:6.54.7.35 Intensities:1.0, 0.34, Widths:0.75.0.377 All these spectra are determined by the same compound, with the wavenumber postions in reciprocal centimeters as Wavenumbers, the energy postions in eV as Energies and corresponding intensitics as Intensities. Based on the information provided by these spectra, predict which compound the spectra corres- pond to and give the SMILES of that compound. Please answer strictly the format #SMILES:
Human:
GPT:
# # ON=C1CCCC1
Given multiple spectra, they are Carbon- 13 Nuclcar Magnetic Resonance (C-shifts: 146. 1..709....7..9442222 Intensities:
0,0.37,00.9.00.77. Intennitiess1.000037......2
Proton Nuclear Magnetic
Resonance 4.57,4.56, Intensities::05500411 0...4.......14..2..22260.22222.0 20t.:........00220.0000011110110
0.33.0.89, 0.91,0.34), Heteronuclear Single Quantum Coherence 1B=shiffs:4433..5744444.. C-
shifts:68.33 39..9.92..99999 (....99....14...... 146.03, Intensities: 1.0.1.0.1.0.10/// Infrared Spectrum (Wave-
numbers:3961.9e (11.9619988999111.
h42.4499944222202222222222222222 3041.47,2999..4
Human:
1414.56,1404.566
0. 110.113.0...11111111110011110220
Mass
spectrum data (mzs:39.00m wppp::39.02.41..0 212.44..4444500,.555 spectra are determined by same compound, with the wavenumber postions reciprocal centimeters as Wavenumbers, the energy postions in eV as Energies and corresponding intensities as Intensities. Based on the information provided by these spectra, prediet which compound the spectra correspond to and give the SMILES of that compound. Please answer strictly in the format # SMILES:
Intensities:0.28, 1.0.0.22,0.23). All of these
GPT:
#MILES:: C1 C1=C0CC1
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Table 30: Representative prompts for single-modality spectroscopic inference.
Single-spectrum Prompts
Given Infrared Spectrum (Wavenumbers: 3596.8, 3,459 23314497,3202.666 1444.0.724.0, Intensities:
1498.51,
2...0.4...12...38.... 5..60.5.,.666...666 Widths: 1.2239914,15.3,111
15.47.17.52.255.8..
the spectra data includes the wavenumber positions in reciprocal centimeters Wavenumbers and corresponding intensities Intensities, corresponding width as Widths. Based on information provided, predict which compound the spectra correspond to and give the SMILES of that compound. Please answer strictly in the format ASMILS::
Human:
GPT:
#SMMLES:: ON=C1CCCC1
Given Raman spectroscopy (Wavenumbers: 2913.0. 2913.0,2922 www.00.22...29000 299.1.1182 0,366850, Intensities: 0.75,100. 1.0,0.61, Widths: .39,22.34.24.45, 10.008, the spectra data includes the wavenumber positions in reciprocal centimeters as Wavenumbers and corresponding intensities as Intensities, corresponding width as Widths. Based on the information provided, predict which compound the spectra corres- pond to and give the SMILES of that compound. Please answer strictly in the format $ 4 SMILES.
Human:
GPT:
#MILES:: ON=C1CC0C1
Given Uttraviole-visible spectroscopy [Energies: 6,54,7.52, Intensitics: 1.0,0.34, Widths: 0.75, 0.37), the spectra data includes the energy positions eV Energies and corresponding inten- sities as Intensities, corresponding width Widths. Based the information provided, predict which compound the spectra correspond to and give the SMILES that compound. Please answer strictly in the format + +*SMILES:
Human:
GPT:
# # #SMLLSS: ON=C1CC0C1
Given Carbon-13 Nuelear Magnetic Resonance (C-shifts: 146. 146.12, 105. 1,3.11,,7.93,422. 03,42.24, Intensities:
the spectra data includes the Chemical Shift positions in ppm as C-shifts and corresponding intensities Intensities. Based the information provided, predict which pound the spectra correspond to and give SMILES of that compound. Please answer strictly the format # #SMILES:
Human:
GPT:
# #*SMIES:: C1=COCC1
Given Proton Nuclcar Magnetic Resonance
6.93.6.31,6.3 1..331,633.,666 ....3.......9.444.. 4.55,44.57,4..5.. Intensities: 0.5,0.47,0.. 2.F........2226222299000........ 16,0..5, 0,,
the spectra data includes the Chemical Shift positions ppm H- shifts corresponding intensities as Intensities. Based on the information provided, predict which compound the spectra correspond to and give the SMILES of that compound. Please answer strictly in the format # #SMILES:
Human:
GPT:
# LES: C1-COCC1
Given Heteronuclear Single Quantum Coherence (II-shifts: 4.3,2.57,4994.66322 C-shifts: 68.39, 27.85,99.66 196.,44,46..... Intensities: 1.0,1.0.100.100/ the spectra data includes the Chemical Shift posi- tions in ppm C-shifis,H--shifts
Human:
corresponding intensities Intensities. Based on the information provided, predict which compound the spectra correspond to and give the SMILES of that compound. Please answer strictly in the format #SMILES:
GPT:
##SMILES: C1=COCC1
Mass spectrum data: {mzs:
21..8..6.6.....4..13.3 13..2244.....6.... 18..44442..34..8.4002..1 19,44402.9,,555266655.....
806.4, intensities: 0tt..00....006..000 20.4,000..0011111111100
wtt......6...1.......... 19.00.355..3 Adduet type: [M+
Human:
Naj+ Collision energy: 35(NCE) Please predict the compound's SMILES representation with LESS
THAN 1000 characters thinking. The final output must strictly begin with SMILES:
##SMILES: CC (C) [céea]1c (=0) [CSH] (C10))000 [сеен] (C (C (=0) (02-00-0--02) -0000000000000000000000000000000 0C300-00-33000001111100 C) cCC4-CC-co-C4cC
([CH)) (=
GPT:
42


Reviews:
[{"review_id": "QAKafOdnsC", "reviewer": "Reviewer_61jb", "summary": "The paper introduces SpectraLLM, a large language model fine-tuned via LoRA to perform end-to-end molecular structure elucidation from diverse spectroscopic inputs (IR, Raman, UV-Vis, NMR, and MS). It reformulates spectral data as natural language prompts to enable symbolic reasoning within a shared semantic space. Using datasets like QM9Spectra, Multimodal Spectroscopic, MassSpecGym, and MassBank, SpectraLLM achieves state-of-the-art unimodal and multimodal performance, outperforming domain-specific baselines across structural and functional similarity metrics.", "questions": "How sensitive is performance to the accuracy of the spectral-to-text mapping (ϕ)?\n\nCould the model generalize to unseen experimental conditions or instruments?\n\nAre there ablations showing which spectral modality contributes most to multimodal gains?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "There is novelty in this work. Specifically, it converts continuous and discrete spectra into text, enabling a unified reasoning space for multiple spectroscopy types.\n\nThere are also strong empirical results in which consistent and large gains over baselines are shown across IR, Raman, NMR, and MS, with near-perfect validity and robustness to missing modalities.\n\nFurthermore, there is comprehensive evaluation where there are benchmarks on multiple datasets with diverse metrics (Tanimoto, MCES, Fraggle, etc.) and insightful qualitative analyses", "weaknesses": "While the model performs well, it remains unclear how linguistic embeddings capture physical–chemical semantics. The authors need to improve the interpretability component of their model. \n\nThere is also heavy reliance on synthetic spectra (QM9s, Multimodal Spectroscopic) may limit real-world generalization. Ultimately, these are simulated data and the authors should show model performance on real world datasets. \n\nThere is also no discussion of common failure modes, ambiguity cases, or invalid SMILES beyond aggregate metrics. The authors should provide more error analysis and better discuss the limitations of their work. \n\nSeveral critical recent works are also omitted from reference in this paper:\n\n- Le, Khiem, et al. “MolX: Enhancing Large Language Models for Molecular Learning with a Multi-Modal Extension.” KDD MLoG-GenAI 2025. (Include as a key related multimodal-LLM baseline/approach.)\n\n- Fang, Junfeng, et al. “MolTC: Towards Molecular Relational Modeling in Language Models.” Findings of the Association for Computational Linguistics: ACL 2024, Association for Computational Linguistics, 2024, pp. 1943–1958. \nACL Anthology\n\n- Ju, Jiaxin, et al. “Uni-MRL: Unified Multimodal Molecular Representation Learning with Large Language Models and Graph Neural Networks.” Advances in Knowledge Discovery and Data Mining (PAKDD 2025), LNCS 15874, Springer, 2025, pp. 275–287. \nSpringerLink", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "jQ8ZG3I6Z7", "reviewer": "Reviewer_MWCJ", "summary": "The paper presents SpectraLLM, an LLM fine-tuned to predict molecular structures given spectra of one or more spectroscopic modalities. The authors compile a new multi-modal dataset of different types of spectra and use it to fine-tune Qwen3 LLM using LoRA. The method is shown to outperform existing methods in both uni-modal and multi-modal settings.", "questions": "- Can the authors explain the practical use cases where their model could be readily applied?\n- Can the authors better evaluate data leakage and elaborate on the data used to retrain the baselines?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- The paper addresses an important problem of molecular structure annotation from spectroscopic data modalities which remains very challenging to solve\n- The authors compile a large new dataset from multiple sources\n- The paper is well written and structured\n- The authors leverage Fraggle similarity from RDKit, which to the best of my knowledge was not explored in related work before", "weaknesses": "Major concerns\n\n- Most of the collected dataset appears to contain relatively small molecules (as shown in Figure 2). Could the authors provide examples of practical use cases where annotation of such small molecules, as opposed to larger natural products or drug-like molecules, is of interest? Without some EDA of the molecular composition and an explanation of when this setting is relevant, it is difficult to assess the utility of the work, especially considering that the methodological novelty is limited (standard LLM fine-tuning). Please also elaborate in what practical scenarios the annotation of molecules simultaneously from multiple types of spectra is relevant.\n- The baselines (e.g., DiffMS) do not seem to be retrained and appear to be used out of the box. Please clarify this in the paper if is not true, or retrain the baselines on the same data splits if is true. Otherwise, the results do not seem to be valid.\n- The results on MassSpecGym do not seem consistent with the MassSpecGym benchmark: the reported metrics differ, and the Tanimoto score for DiffMS does not match the published result. Please retrain the model using the official MassSpecGym data split. Without such an experiment, it is unclear whether the proposed method actually outperforms DiffMS.\n- The evaluation may suffer from data leakage. Please elaborate on the splitting strategy and the degree of similarity between molecules in the training and testing sets. The paper currently mentions a “molecular identity”-based data split, which is known to result in leakage even when stereochemistry is ignored (see, for example, Figure 2 in the MassSpecGym paper https://arxiv.org/abs/2410.23326).\n- The goal of annotating molecules from spectra is to discover novel molecules that have not been characterized before. The LLM-based approach may be prone to data leakage if it is already familiar with test-set molecules. Could the authors evaluate to what extent the model is familiar with these molecules before fine-tuning? Please note that non-LLM models (e.g., DiffMS) are not prone to this potential problem.\n\nMinor concerns\n\n- Table 1: Please note that isotopic distributions are typically present in MS1 spectra, not in MS/MS spectra, which this paper does not work with\n- Lines 066–071: These statements are not well justified. One could argue the opposite: working with precise continuous values is more important than discrete tokenised “low-resolution” values, as spectroscopic signals often contain information in decimal places, which may be hard to model with a tokenized LLM approach\n- Line 152: Please note that MassBank is part of, or substantially overlaps with, MassSpecGym\n- Equation 3: Please define phi\n- Lines 176–179: Please elaborate on how experimental conditions and metadata are incorporated\n- Figure 2: It is not clear what the checkmarks and boxes indicate\n- Lines 879–884: It is unclear what A and B represent", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "dbdIBz6I6b", "reviewer": "Reviewer_myQs", "summary": "The paper presents SpectraLLM, a language-based framework for molecular structure prediction from spectroscopy. The key design is to transform spectral peaks (IR, Raman, UV-Vis, NMR, MS) into compact textual tokens that encode physical attributes (e.g., position, intensity, annotations). The LoRA-tuned LLM, SpectraLLM, then autoregressively generates SMILES from one or multiple spectra. This unified textual interface allows the model to combine heterogeneous modalities in a shared semantic space and to perform joint reasoning across signals. The authors aggregate several public datasets, enforce molecule-identity splits, and evaluate both unimodal and multimodal settings using chemically meaningful metrics (e.g., ECFP/MACCS Tanimoto, MCES, functional-group overlap). They report consistent gains when fusing modalities and stronger NMR performance compared with prior systems. Qualitative cases illustrate when specific modalities add value.", "questions": "1. How sensitive are results to the design of spectral tokenization (e.g., peak thresholds, binning, ordering, or inclusion of metadata)?\n\n1. Do the authors plan to open-source code, model checkpoints, and prompt templates?\n\n1. Can you provide evaluation on experimental spectra (e.g., MassBank IR/NMR) to confirm real-world robustness?\n\n1. How does the model perform under top-(k) decoding or diverse sampling?\n\n1. Are stereochemical correctness and formula consistency checked after decoding?\n\n1. What are the compute requirements for fine-tuning and inference compared to modality-specific models?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "* By transforming peaks into tokens that capture physical attributes, the method places heterogeneous spectra in a shared semantic space. This is a simple, scalable way to enable joint reasoning over multi-spectral inputs.\n* Fusing modalities improves similarity metrics and structural correctness over the best single-modality baselines; the NMR setting shows clear margins.\n* Parameter-efficient LoRA on a general LLM, near-perfect SMILES validity, and clear data preprocessing make the approach straightforward to reproduce in principle.\n* Multiple datasets and metrics (beyond exact match) provide a more realistic view of structure recovery.", "weaknesses": "* The core idea—transforming peaks into textual tokens and fine-tuning an existing LLM—is incremental relative to existing generative pipelines. The paper does not introduce fundamentally new learning principles or solutions for spectroscopy-to-structure mapping. This is the major concern regarding the work’s originality and contribution to the ICLR community.\n\n* No public code or model weights are provided. This limits reproducibility and the community’s ability to validate and extend the work.\n\n* Results may be sensitive to peak detection criteria, binning, token order, and inclusion of side metadata. The paper does not quantify these effects.\n\n* The dataset mix contains substantial simulated spectra and small molecules. Transfer to challenging experimental conditions (noise, matrix effects, mixture spectra) is not fully assessed.\n\n* Training scale, wall-clock, and inference latency versus specialized models are not reported.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "yd6rjiA5q9", "reviewer": "Reviewer_m8ht", "summary": "This paper introduces SpectraLLM, a large language model designed for molecular structure elucidation using multiple spectra. The model tries to address limitations of single modality approaches by unifying diverse spectroscopic data into a shared language space. The method works by converting spectral peak features into natural language prompts. It then uses a frozen Qwen3 backbone adapted with LoRA to autoregressively generate SMILES strings. The authors evaluate this on four public datasets. The results show state-of-the-art performance and demonstrate that combining spectra improves prediction accuracy.", "questions": "1.  Could the authors please comment on the robustness of SpectraLLM to variations in the peak picking step? For example, how much do errors or changes in peak detection thresholds affect the final SMILES generation?\n\n2.  The Tanimoto scores are strong relative to baselines, but the task is clearly not solved. In the authors' view, what is the primary remaining bottleneck? Is it the spectral representation, the reasoning capacity of the language model, or the inherent ambiguity in the spectra?\n\n3. The paper notes the inclusion of experimental conditions. Could the authors provide a brief analysis of the specific contribution of this metadata, especially collision energy for MS, to the model's accuracy?\n\n4. The model is trained on a combination of simulated and experimental datasets. Was any analysis done on generalization? For instance, how well does a model trained only on simulated data perform on the experimental test sets?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. The experimental evaluation is comprehensive. The model was tested on four large-scale datasets, which include both simulated and experimental data.\n\n2. SpectraLLM shows strong performance. It consistently surpasses specialized baselines even in single modality tasks. The improvement over the NMR2Struct baseline in Table 2 is particularly notable.\n\n\n3. The analysis in Appendix A.4 provides a good justification for the chosen architecture. The comparison against a vision language model pipeline confirms the benefit of the purely language-based representation for this task.", "weaknesses": "1. There is a slight concern about the dependency on the upstream peak extraction process. The model performance seems tied to the quality of this peak list. This step could be an information bottleneck if important signals are missed or noise is included from the raw spectra.\n\n\n2. While the model achieves state-of-the-art results, the absolute performance metrics suggest the task remains very difficult. For instance, the best Tanimoto score in Table 4 is approximately 0.4875. This indicates significant room for future improvement.\n\n3. The paper mentions including experimental metadata in the prompts, such as collision energy. This is a good idea, particularly for mass spectrometry. However, the paper might benefit from a more direct analysis or ablation study showing the specific impact of this metadata on performance", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)