MS2MetGAN: Latent-space adversarial training for metabolite-spectrum matching in MS/MS database search

Meng Tsai; Alexzander Dwyer; Estelle Nuckels; Yingfeng Wang

cs.LGMar 7, 2026

MS2MetGAN: Latent-space adversarial training for metabolite-spectrum matching in MS/MS database search

Authors: Meng Tsai, Alexzander Dwyer, Estelle Nuckels, Yingfeng Wang

Organizations: Department of Computer Science and Engineering, University of Tennessee at Chattanooga, 615 McCallie Ave, Chattanooga, 37403, Tennessee, United States. · Department of Natural Sciences, Middle Georgia State University, 100 University Pkwy, Macon, 31206, Georgia, United States.

Abstract

Database search is a widely used approach for identifying metabolites from tandem mass spectra (MS/MS). In this strategy, an experimental spectrum is matched against a user-specified database of candidate metabolites, and candidates are ranked such that true metabolite-spectrum matches receive the highest scores. Machine-learning methods have been widely incorporated into database-search-based identification tools and have substantially improved performance. To further improve identification accuracy, we propose a new framework for generating negative training samples. The framework first uses autoencoders to learn latent representations of metabolite structures and MS/MS spectra, thereby recasting metabolite-spectrum matching as matching between latent vectors. It then uses a GAN to generate latent vectors of decoy metabolites and constructs decoy metabolite-spectrum matches as negative samples for training. Experimental results show that our tool, MS2MetGAN, achieves better overall performance than existing metabolite identification methods.

Figures & tables

Figure 1: Overview of the method. MS/MS spectra and metabolite structures are encoded into latent spectrum vectors and latent metabolite vectors by a spectrum autoencoder and a structure autoencoder, respectively. The generative adversarial network (GAN) comprises a generator and a discriminator. The generator produces synthetic latent metabolite vectors conditioned on latent spectrum vectors, whereas the discriminator assigns a true-likeness score to each latent metabolite–spectrum match (MSM), distinguishing true MSMs from decoy MSMs formed by pairing synthetic metabolite latents with the corresponding spectrum latents. These scores are then used to rank metabolite candidates.

	MIDAS	SF-Matching	MAGMa+	CSI:FingerID	CFM-ID
CASMI2016SP	90.98%	99.18%	54.92%	59.84%	69.67%
CASMI2017SP	88.10%	83.33%	59.52%	64.29%	61.90%
CASMI2016FP	85.04%	98.16%	57.22%	86.88%	66.40%
CASMI2017FP	67.12%	61.64%	51.37%	30.82%	42.47%
CASMI2022P	68.94%	58.39%	54.04%	77.64%	65.84%
GNPS-S	54.92%	44.04%	30.57%	34.72%	8.29%

Table 1: Identification accuracies of baseline tools and MS2MetGAN for database searching against MetaCyc (bold indicates the highest accuracy)

	MIDAS	SF-Matching	MAGMa+	CSI:FingerID	CFM-ID
CASMI2016SP	54.92%	98.36%	32.79%	58.20%	61.48%
CASMI2017SP	57.14%	80.95%	30.95%	69.05%	45.24%
CASMI2016FP	54.59%	95.28%	33.07%	83.99%	57.48%
CASMI2017FP	39.73%	60.27%	27.40%	31.51%	42.47%
CASMI2022P	42.86%	34.16%	29.81%	29.19%	34.78%
GNPS-S	40.42%	64.77%	16.58%	34.72%	72.02%

Table 2: Identification accuracies of baseline tools and MS2MetGAN for database searching against isomer decoys (bold indicates the highest accuracy)

	MIDAS	SF-Matching	MAGMa+	CSI:FingerID
MetaCyc Database	66.67%	55.56%	88.89%	77.78%
Isomer Decoys	88.89%	66.67%	100.00%	100.00%
	CFM-ID	MetFrag	CMSSP	CSU-MS2
MetaCyc Database	88.89%	88.89%	55.56%	88.89%
Isomer Decoys	100.00%	88.89%	77.78%	100.00%

Table 3: Percentage of test datasets on which MS2MetGAN outperforms baseline tools in pairwise comparisons

	GAN-0	GAN-1	GAN-2	GAN-3	GAN-4
CASMI2016SP	80.33%	49.18%	65.57%	61.48%	67.21%
CASMI2017SP	52.38%	33.33%	61.90%	61.90%	57.14%
CASMI2016FP	91.08%	43.04%	72.97%	65.35%	72.44%
CASMI2017FP	100.00%	52.74%	72.60%	69.18%	67.12%
CASMI2022P	33.54%	47.83%	33.54%	35.40%	34.78%
GNPS-S	98.96%	43.01%	67.36%	55.44%	62.18%

Table 4: Identification accuracies of discriminators during training for database searching against MetaCyc (GAN-0 is trained using structural isomers from PubChem as negative samples, and GAN-9 corresponds to MS2MetGAN)

	GAN-0	GAN-1	GAN-2	GAN-3	GAN-4
CASMI2016SP	89.34%	55.74%	57.38%	68.85%	77.05%
CASMI2017SP	76.19%	16.67%	19.05%	64.29%	59.52%
CASMI2016FP	92.91%	60.10%	62.99%	67.19%	78.74%
CASMI2017FP	100.00%	45.21%	45.89%	72.60%	76.71%
CASMI2022P	19.88%	34.16%	22.36%	27.33%	32.30%
GNPS-S	82.90%	37.82%	39.90%	52.85%	63.73%

Table 5: Identification accuracies of discriminators during training for database searching against isomer decoys (GAN-0 is trained using structural isomers from PubChem as negative samples, and GAN-9 corresponds to MS2MetGAN)

Appendix figures & tables36 assets

Supplementary material from the paper’s appendix.

Appendix

	CASMI2016SP	CASMI2017SP	CASMI2016FP	CASMI2017FP	CASMI2022P
total	122	42	381	146	161
rank 1	111	37	324	98	111
rank 2	5	1	27	7	24
rank 3	3	0	12	9	9
rank 4	0	1	3	9	6
rank 5	0	1	3	6	1

Appendix

Table S1: Distribution of the true-compound rank in MIDAS database searches against MetaCyc across benchmark spectra datasets

	CASMI2016SP	CASMI2017SP	CASMI2016FP	CASMI2017FP	CASMI2022P
total	122	42	381	146	161
rank 1	121	35	374	90	94
rank 2	0	4	1	20	30
rank 3	0	0	3	8	18
rank 4	0	1	0	5	5
rank 5	0	2	1	6	4

Appendix

Table S2: Distribution of the true-compound rank in SF-Matching database searches against MetaCyc across benchmark spectra datasets

	CASMI2016SP	CASMI2017SP	CASMI2016FP	CASMI2017FP	CASMI2022P
total	122	42	381	146	161
rank 1	67	25	218	75	87
rank 2	10	5	40	17	25
rank 3	2	2	12	7	14
rank 4	1	0	2	4	8
rank 5	1	0	13	3	3

Appendix

Table S3: Distribution of the true-compound rank in MAGMA+ database searches against MetaCyc across benchmark spectra datasets

	CASMI2016SP	CASMI2017SP	CASMI2016FP	CASMI2017FP	CASMI2022P
total	122	42	381	146	161
rank 1	73	27	331	45	125
rank 2	0	2	32	14	11
rank 3	0	2	3	17	4
rank 4	0	0	3	4	3
rank 5	0	1	1	5	1

Appendix

Table S4: Distribution of the true-compound rank in CSI:FingerID database searches against MetaCyc across benchmark spectra datasets

	CASMI2016SP	CASMI2017SP	CASMI2016FP	CASMI2017FP	CASMI2022P
total	122	42	381	146	161
rank 1	85	26	253	62	106
rank 2	14	5	53	28	28
rank 3	6	3	27	12	6
rank 4	6	2	17	10	6
rank 5	4	1	8	9	5

Appendix

Table S5: Distribution of the true-compound rank in CFM-ID database searches against MetaCyc across benchmark spectra datasets

	CASMI2016SP	CASMI2017SP	CASMI2016FP	CASMI2017FP	CASMI2022P
total	122	42	381	146	161
rank 1	52	11	129	26	105
rank 2	14	2	44	7	25
rank 3	4	5	18	10	12
rank 4	4	4	12	8	6
rank 5	5	2	11	4	2

Appendix

Table S6: Distribution of the true-compound rank in MetFrag database searches against MetaCyc across benchmark spectra datasets

	CASMI2016SP	CASMI2017SP	CASMI2016FP	CASMI2017FP	CASMI2022P
total	122	42	381	146	161
rank 1	108	39	324	110	72
rank 2	10	0	29	10	25
rank 3	0	1	5	6	8
rank 4	0	1	7	0	9
rank 5	1	0	5	1	4

Appendix

Table S7: Distribution of the true-compound rank in CMSSP database searches against MetaCyc across benchmark spectra datasets

	CASMI2016SP	CASMI2017SP	CASMI2016FP	CASMI2017FP	CASMI2022P
total	122	42	381	146	161
rank 1	32	23	93	44	67
rank 2	20	7	56	31	27
rank 3	10	4	43	12	15
rank 4	11	3	32	10	11
rank 5	4	0	27	15	11

Appendix

Table S8: Distribution of the true-compound rank in CSU-MS2 database searches against MetaCyc across benchmark spectra datasets

	CASMI2016SP	CASMI2017SP	CASMI2016FP	CASMI2017FP	CASMI2022P
total	122	42	381	146	161
rank 1	100	37	326	120	61
rank 2	0	0	0	1	21
rank 3	0	0	0	0	24
rank 4	0	0	0	0	8
rank 5	0	0	0	0	13

Appendix

Table S9: Distribution of the true-compound rank in MS2MetGAN database searches against MetaCyc across benchmark spectra datasets

	CASMI2016SP	CASMI2017SP	CASMI2016FP	CASMI2017FP	CASMI2022P
total	122	42	381	146	161
rank 1	67	24	208	58	69
rank 2	16	4	39	17	12
rank 3	8	5	28	12	8
rank 4	3	1	13	10	10
rank 5	6	0	23	6	9

Appendix

Table S10: Distribution of the true-compound rank in MIDAS database searches against isomer decoys across benchmark spectra datasets

	CASMI2016SP	CASMI2017SP	CASMI2016FP	CASMI2017FP	CASMI2022P
total	122	42	381	146	161
rank 1	120	34	363	88	55
rank 2	0	4	9	12	15
rank 3	0	1	3	7	12
rank 4	1	1	2	4	6
rank 5	0	1	1	4	6

Appendix

Table S11: Distribution of the true-compound rank in SF-Matching database searches against isomer decoys across benchmark spectra datasets

	CASMI2016SP	CASMI2017SP	CASMI2016FP	CASMI2017FP	CASMI2022P
total	122	42	381	146	161
rank 1	40	13	126	40	48
rank 2	14	4	46	12	12
rank 3	6	5	25	15	10
rank 4	6	0	18	7	9
rank 5	1	1	18	6	7

Appendix

Table S12: Distribution of the true-compound rank in MAGMA+ database searches against isomer decoys across benchmark spectra datasets

	CASMI2016SP	CASMI2017SP	CASMI2016FP	CASMI2017FP	CASMI2022P
total	122	42	381	146	161
rank 1	71	29	320	46	47
rank 2	1	0	13	4	12
rank 3	0	0	3	1	5
rank 4	0	0	3	1	5
rank 5	0	0	0	2	4

Appendix

Table S13: Distribution of the true-compound rank in CSI:FingerID database searches against isomer decoys across benchmark spectra datasets

	CASMI2016SP	CASMI2017SP	CASMI2016FP	CASMI2017FP	CASMI2022P
total	122	42	381	146	161
rank 1	75	19	219	62	56
rank 2	10	7	33	20	14
rank 3	8	2	25	6	6
rank 4	4	0	17	3	6
rank 5	7	1	11	6	5

Appendix

Table S14: Distribution of the true-compound rank in CFM-ID database searches against isomer decoys across benchmark spectra datasets

	CASMI2016SP	CASMI2017SP	CASMI2016FP	CASMI2017FP	CASMI2022P
total	122	42	381	146	161
rank 1	63	13	194	71	62
rank 2	13	0	49	16	21
rank 3	11	2	32	12	13
rank 4	1	2	16	10	11
rank 5	2	1	17	7	5

Appendix

Table S15: Distribution of the true-compound rank in MetFrag database searches against isomer decoys across benchmark spectra datasets

	CASMI2016SP	CASMI2017SP	CASMI2016FP	CASMI2017FP	CASMI2022P
total	122	42	381	146	161
rank 1	109	25	342	117	39
rank 2	7	15	18	6	27
rank 3	0	2	7	7	11
rank 4	1	0	2	2	8
rank 5	1	0	2	3	3

Appendix

Table S16: Distribution of the true-compound rank in CMSSP database searches against isomer decoys across benchmark spectra datasets

	CASMI2016SP	CASMI2017SP	CASMI2016FP	CASMI2017FP	CASMI2022P
total	122	42	381	146	161
rank 1	9	7	29	30	19
rank 2	13	2	21	7	7
rank 3	7	4	21	7	14
rank 4	7	5	21	12	6
rank 5	3	3	19	8	12

Appendix

Table S17: Distribution of the true-compound rank in CSU-MS2 database searches against isomer decoys across benchmark spectra datasets

	CASMI2016SP	CASMI2017SP	CASMI2016FP	CASMI2017FP	CASMI2022P
total	122	42	381	146	161
rank 1	105	38	333	126	61
rank 2	1	1	1	2	13
rank 3	2	0	2	0	9
rank 4	0	0	1	0	8
rank 5	2	0	5	0	4

Appendix

Table S18: Distribution of the true-compound rank in MS2MetGAN database searches against isomer decoys across benchmark spectra datasets

	CASMI2016SP	CASMI2017SP	CASMI2016FP	CASMI2017FP	CASMI2022P
total	122	42	381	146	161
rank 1	98	22	347	146	54
rank 2	0	0	0	0	30
rank 3	0	0	1	0	18
rank 4	0	0	3	0	10
rank 5	1	0	1	0	15

Appendix

Table S19: Distribution of the true-compound rank in GAN-0 database searches against MetaCyc across benchmark spectral datasets (GAN-0 is trained using structural isomers from PubChem as negative samples)

	CASMI2016SP	CASMI2017SP	CASMI2016FP	CASMI2017FP	CASMI2022P
total	122	42	381	146	161
rank 1	60	14	164	77	77
rank 2	2	0	11	2	22
rank 3	0	0	2	1	21
rank 4	2	0	2	0	5
rank 5	0	0	0	1	7

Appendix

Table S20: Distribution of the true-compound rank in GAN-1 database searches against MetaCyc across benchmark spectral datasets

	CASMI2016SP	CASMI2017SP	CASMI2016FP	CASMI2017FP	CASMI2022P
total	122	42	381	146	161
rank 1	80	26	278	106	54
rank 2	0	0	3	0	29
rank 3	0	0	1	0	18
rank 4	0	1	1	1	10
rank 5	0	0	1	0	19

Appendix

Table S21: Distribution of the true-compound rank in GAN-2 database searches against MetaCyc across benchmark spectral datasets

	CASMI2016SP	CASMI2017SP	CASMI2016FP	CASMI2017FP	CASMI2022P
total	122	42	381	146	161
rank 1	75	26	249	101	57
rank 2	1	0	1	0	28
rank 3	0	0	1	0	21
rank 4	0	0	1	1	17
rank 5	0	0	1	0	6

Appendix

Table S22: Distribution of the true-compound rank in GAN-3 database searches against MetaCyc across benchmark spectral datasets

	CASMI2016SP	CASMI2017SP	CASMI2016FP	CASMI2017FP	CASMI2022P
total	122	42	381	146	161
rank 1	82	24	276	98	56
rank 2	1	0	1	0	28
rank 3	0	0	0	0	21
rank 4	0	0	1	1	18
rank 5	0	0	2	0	7

Appendix

Table S23: Distribution of the true-compound rank in GAN-4 database searches against MetaCyc across benchmark spectral datasets

	CASMI2016SP	CASMI2017SP	CASMI2016FP	CASMI2017FP	CASMI2022P
total	122	42	381	146	161
rank 1	91	35	308	107	68
rank 2	0	0	1	0	28
rank 3	0	0	0	1	25
rank 4	0	0	0	0	5
rank 5	0	0	0	1	7

Appendix

Table S24: Distribution of the true-compound rank in GAN-5 database searches against MetaCyc across benchmark spectral datasets

	CASMI2016SP	CASMI2017SP	CASMI2016FP	CASMI2017FP	CASMI2022P
total	122	42	381	146	161
rank 1	91	29	313	111	56
rank 2	0	0	3	1	27
rank 3	1	0	2	0	22
rank 4	1	0	1	0	13
rank 5	1	0	2	1	12

Appendix

Table S25: Distribution of the true-compound rank in GAN-6 database searches against MetaCyc across benchmark spectral datasets

	CASMI2016SP	CASMI2017SP	CASMI2016FP	CASMI2017FP	CASMI2022P
total	122	42	381	146	161
rank 1	99	37	327	119	60
rank 2	0	0	0	0	24
rank 3	0	0	0	0	25
rank 4	0	0	0	0	6
rank 5	0	0	0	0	12

Appendix

Table S26: Distribution of the true-compound rank in GAN-7 database searches against MetaCyc across benchmark spectral datasets

	CASMI2016SP	CASMI2017SP	CASMI2016FP	CASMI2017FP	CASMI2022P
total	122	42	381	146	161
rank 1	91	34	312	117	58
rank 2	0	0	0	2	24
rank 3	2	0	6	0	23
rank 4	1	0	0	0	9
rank 5	2	0	2	0	1

Appendix

Table S27: Distribution of the true-compound rank in GAN-8 database searches against MetaCyc across benchmark spectral datasets

	CASMI2016SP	CASMI2017SP	CASMI2016FP	CASMI2017FP	CASMI2022P
total	122	42	381	146	161
rank 1	109	32	354	146	32
rank 2	1	1	5	0	11
rank 3	1	1	3	0	8
rank 4	0	1	2	0	6
rank 5	2	1	2	0	5

Appendix

Table S28: Distribution of the true-compound rank in GAN-0 database searches against isomer decoys across benchmark spectral datasets (GAN-0 is trained using structural isomers from PubChem as negative samples)

	CASMI2016SP	CASMI2017SP	CASMI2016FP	CASMI2017FP	CASMI2022P
total	122	42	381	146	161
rank 1	68	7	229	66	55
rank 2	4	1	14	7	4
rank 3	3	4	1	6	5
rank 4	3	2	1	5	5
rank 5	3	0	6	1	3

Appendix

Table S29: Distribution of the true-compound rank in GAN-1 database searches against isomer decoys across benchmark spectral datasets

	CASMI2016SP	CASMI2017SP	CASMI2016FP	CASMI2017FP	CASMI2022P
total	122	42	381	146	161
rank 1	70	8	240	67	36
rank 2	3	1	6	7	1
rank 3	2	1	6	1	10
rank 4	1	1	5	3	6
rank 5	0	0	6	0	4

Appendix

Table S30: Distribution of the true-compound rank in GAN-2 database searches against isomer decoys across benchmark spectral datasets

	CASMI2016SP	CASMI2017SP	CASMI2016FP	CASMI2017FP	CASMI2022P
total	122	42	381	146	161
rank 1	84	27	256	106	44
rank 2	1	0	7	6	11
rank 3	1	1	3	2	5
rank 4	1	0	4	1	7
rank 5	2	0	7	0	6

Appendix

Table S31: Distribution of the true-compound rank in GAN-3 database searches against isomer decoys across benchmark spectral datasets

	CASMI2016SP	CASMI2017SP	CASMI2016FP	CASMI2017FP	CASMI2022P
total	122	42	381	146	161
rank 1	94	25	300	112	52
rank 2	3	2	9	2	16
rank 3	2	0	4	1	10
rank 4	1	0	6	3	5
rank 5	2	1	8	1	6

Appendix

Table S32: Distribution of the true-compound rank in GAN-4 database searches against isomer decoys across benchmark spectral datasets

	CASMI2016SP	CASMI2017SP	CASMI2016FP	CASMI2017FP	CASMI2022P
total	122	42	381	146	161
rank 1	96	35	312	114	50
rank 2	3	2	3	3	8
rank 3	0	0	1	3	5
rank 4	1	0	3	0	10
rank 5	1	0	2	2	4

Appendix

Table S33: Distribution of the true-compound rank in GAN-5 database searches against isomer decoys across benchmark spectral datasets

	CASMI2016SP	CASMI2017SP	CASMI2016FP	CASMI2017FP	CASMI2022P
total	122	42	381	146	161
rank 1	114	40	313	135	58
rank 2	0	0	2	2	16
rank 3	0	0	2	1	4
rank 4	0	0	2	0	6
rank 5	1	0	4	0	8

Appendix

Table S34: Distribution of the true-compound rank in GAN-6 database searches against isomer decoys across benchmark spectral datasets

	CASMI2016SP	CASMI2017SP	CASMI2016FP	CASMI2017FP	CASMI2022P
total	122	42	381	146	161
rank 1	105	39	336	125	63
rank 2	0	0	0	1	15
rank 3	0	0	0	2	12
rank 4	0	0	0	0	7
rank 5	0	0	0	0	2

Appendix

Table S35: Distribution of the true-compound rank in GAN-7 database searches against isomer decoys across benchmark spectral datasets

	CASMI2016SP	CASMI2017SP	CASMI2016FP	CASMI2017FP	CASMI2022P
total	122	42	381	146	161
rank 1	103	36	333	129	60
rank 2	2	1	4	4	17
rank 3	1	0	2	1	8
rank 4	0	0	0	0	5
rank 5	1	0	4	0	4

Appendix

Table S36: Distribution of the true-compound rank in GAN-8 database searches against isomer decoys across benchmark spectral datasets

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Abstract

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