ARXIV:2602.22822 · DEEP LEARNING FRAMEWORKS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

FlexMS is a flexible framework for benchmarking deep learning-based mass spectrum prediction tools in metabolomics

arXiv

FlexMS is a flexible benchmarking framework for deep learning-based mass spectrum prediction in metabolomics.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain FlexMS is a flexible benchmarking framework for deep learning-based mass spectrum prediction in metabolomics.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

FlexMS is a flexible benchmarking framework for deep learning-based mass spectrum prediction in metabolomics. However, the lack of experimental spectra hinders the attachment of each molecular identification, and thus urges the establishment of prediction…

METHOD

Full abstract

The identification and property prediction of chemical molecules is of central importance in the advancement of drug discovery and material science, where the tandem mass spectrometry technology gives valuable fragmentation cues in the form of mass-to-charge ratio peaks. However, the lack of experimental spectra hinders the attachment of each molecular identification, and thus urges the establishment of prediction approaches for computational models. Deep learning models appear promising for predicting molecular structure spectra, but overall assessment remains challenging as a result of the heterogeneity in methods and the lack of well-defined benchmarks. To address this, our contribution is the creation of benchmark framework FlexMS for constructing and evaluating diverse model architectures in mass spectrum prediction. With its easy-to-use flexibility, FlexMS supports the dynamic construction of numerous distinct combinations of model architectures, while assessing their performance on preprocessed public datasets using different metrics. In this paper, we provide insights into factors influencing performance, including the structural diversity of datasets, hyperparameters like learning rate and data sparsity, pretraining effects, metadata ablation settings and cross-domain transfer learning analysis. This provides practical guidance in choosing suitable models. Moreover, retrieval benchmarks simulate practical identification scenarios and score potential matches based on predicted spectra.

RESULT

ScienceToStartup currently rates this 5.0/10 on the public viability pass. Deep learning models appear promising for predicting molecular structure spectra, but overall assessment remains challenging as a result of the heterogeneity in methods and…

WHY NOW

Deep Learning Frameworks moved forward this cycle; last verified April 2026. Public score 5.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score5.0

PainFlexMS is a flexible benchmarking framework for deep learning-based mass spectrum prediction in metabolomics.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

FlexMS is a flexible benchmarking framework for deep learning-based mass spectrum prediction in metabolomics.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2602.22822 · DEEP LEARNING FRAMEWORKS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

FlexMS is a flexible framework for benchmarking deep learning-based mass spectrum prediction tools in metabolomics

arXiv

FlexMS is a flexible benchmarking framework for deep learning-based mass spectrum prediction in metabolomics.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain FlexMS is a flexible benchmarking framework for deep learning-based mass spectrum prediction in metabolomics.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Deep Learning Frameworks moved forward this cycle; last verified April 2026. Public score 5.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score5.0

PainFlexMS is a flexible benchmarking framework for deep learning-based mass spectrum prediction in metabolomics.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

FlexMS is a flexible benchmarking framework for deep learning-based mass spectrum prediction in metabolomics.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2602.22822

FlexMS is a flexible framework for benchmarking deep learning-based mass spectrum prediction tools in metabolomics

FlexMS is a flexible benchmarking framework for deep learning-based mass spectrum prediction in metabolomics.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materializedderived fallback56 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

5.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Deep Learning Frameworks moved forward this cycle; last verified April 2026. Public score 5.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
FlexMS is a flexible benchmarking framework for deep learning-based mass spectrum prediction in metabolomics. However, the lack of experimental spectra hinders the attachment of each molecular identification, and thus urges the establishment of prediction approaches for computational models.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
The identification and property prediction of chemical molecules is of central importance in the advancement of drug discovery and material science, where the tandem mass spectrometry technology gives valuable fragmentation cues in the form of mass-to-charge ratio peaks. However, the lack of experimental spectra hinders the attachment of each molecular identification, and thus urges the establishment of prediction approaches for computational models.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Deep learning models appear promising for predicting molecular structure spectra, but overall assessment remains challenging as a result of the heterogeneity in methods and the lack of well-defined benchmarks.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Deep Learning Frameworks moved forward this cycle; last verified April 2026. Public score 5.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linked

Markets

Deep Learning Frameworks

Competitors

not indexed

Competitive landscape

FlexMS is a flexible benchmarking framework for deep learning-based mass spectrum prediction in metabolomics.

Segment

Deep Learning Frameworks

Adoption evidence

No public code link in the paper record yet

Commercial read

5.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2602.22822 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

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PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(56)

Adduct-Induced Variability in Tandem Mass Spectrometry.

2025Botao Liu, Zhifeng Tang et al.

MassSpecGym: A benchmark for the discovery and identification of molecules

2024Roman Bushuiev, Anton Bushuiev et al.

Tandem mass spectrum prediction for small molecules using graph transformers

2024A. Young, Hannes L. Röst et al.

Generating Molecular Fragmentation Graphs with Autoregressive Neural Networks.

2023Samuel Goldman, Janet X. Li et al.

3DMolMS: prediction of tandem mass spectra from 3D molecular conformations

2023Yuhui Hong, Sujun Li et al.

Prefix-tree Decoding for Predicting Mass Spectra from Molecules

2023Samuel Goldman, John Bradshaw et al.

Advances in Ultra-High-Resolution Mass Spectrometry for Pharmaceutical Analysis

2023Estelle Deschamps, Valentina Calabrese et al.

Mole-BERT: Rethinking Pre-training Graph Neural Networks for Molecules

2023Jun Xia, Chengshuai Zhao et al.

Spectral entropy outperforms MS/MS dot product similarity for small-molecule compound identification

2021Yuanyue Li, T. Kind et al.

Do Transformers Really Perform Badly for Graph Representation?

2021Chengxuan Ying, Tianle Cai et al.

DeepPurpose: a deep learning library for drug–target interaction prediction

2020Kexin Huang, Tianfan Fu et al.

Molecular representation learning with language models and domain-relevant auxiliary tasks

2020Benedek Fabian, T. Edlich et al.

Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra

2020Kai Dührkop, Louis-Félix Nothias et al.

ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction

2020Seyone Chithrananda, Gabriel Grand et al.

Pushing the boundaries of molecular representation for drug discovery with graph attention mechanism.

2020Zhaoping Xiong, Dingyan Wang et al.

Self-Supervised Graph Transformer on Large-Scale Molecular Data

2020Yu Rong, Yatao Bian et al.

An open source chemical structure curation pipeline using RDKit

2020A. P. Bento, A. Hersey et al.

Machine Learning Applications for Mass Spectrometry-Based Metabolomics

2020Ulf W. Liebal, An N T Phan et al.

A Comprehensive Survey on Transfer Learning

2019Fuzhen Zhuang, Zhiyuan Qi et al.

Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks.

2019Minjie Wang, Da Zheng et al.

Showing 20 of 56 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

Prior WorkWhen should we trust the annotation? Selective prediction for molecular structure retrieval from mass spectra

5.0

Prior WorkHyperFitS -- Hypernetwork Fitting Spectra for metabolic quantification of ${}^1$H MR spectroscopic imaging

5.0

Extension

Builds On ThisThe Infinite-Dimensional Nature of Spectroscopy and Why Models Succeed, Fail, and Mislead

2.0

Commercially relevant

Higher ViabilityFlowMS: Flow Matching for De Novo Structure Elucidation from Mass Spectra

7.0

Higher ViabilityRamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy

7.0

Higher ViabilityUnlocking High-Fidelity Molecular Generation from Mass Spectra via Dual-Stream Line Graph Diffusion

8.0

Higher ViabilityMolDeTox: Evaluating Language Model's Stepwise Fragment Editing for Molecular Detoxification

7.0

Higher ViabilityDataFlex: A Unified Framework for Data-Centric Dynamic Training of Large Language Models

7.0

Higher ViabilitySpatial self-supervised Peak Learning and correlation-based Evaluation of peak picking in Mass Spectrometry Imaging

7.0

Higher ViabilityNovelty-Driven Target-Space Discovery in Automated Electron and Scanning Probe Microscopy

6.0

Conflicting

none indexed

Related Resources

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Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2602.22822
Route: /paper/flexms-is-a-flexible-framework-for-benchmarking-deep-learning-based-mass-spectrum-prediction-tools-in-metabolomics
Active tab: read
Artifact: flexms-is-a-flexible-framework-for-benchmarking-deep-learning-based-mass-spectrum-prediction-tools-in-metabolomics

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/flexms-is-a-flexible-framework-for-benchmarking-deep-learning-based-mass-spectrum-prediction-tools-in-metabolomics/paper-pack
REST build passport API/api/v1/paper/flexms-is-a-flexible-framework-for-benchmarking-deep-learning-based-mass-spectrum-prediction-tools-in-metabolomics/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

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Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/flexms-is-a-flexible-framework-for-benchmarking-deep-learning-based-mass-spectrum-prediction-tools-in-metabolomics

stale

Proof freshness: stale
Proof status: unverified
Display score: 5/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

FlexMS is a flexible framework for benchmarking deep learning-based mass spectrum prediction tools in metabolomics

Canonical ID flexms-is-a-flexible-framework-for-benchmarking-deep-learning-based-mass-spectrum-prediction-tools-in-metabolomics | Route /paper/flexms-is-a-flexible-framework-for-benchmarking-deep-learning-based-mass-spectrum-prediction-tools-in-metabolomics

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/flexms-is-a-flexible-framework-for-benchmarking-deep-learning-based-mass-spectrum-prediction-tools-in-metabolomics

MCP example

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source_context

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Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Watch and verify: FlexMS is a flexible framework for benchmarking deep learning-based mass spectrum prediction tools in metabolomics

/buildability/flexms-is-a-flexible-framework-for-benchmarking-deep-learning-based-mass-spectrum-prediction-tools-in-metabolomics

Watchwatch

Subject: FlexMS is a flexible framework for benchmarking deep learning-based mass spectrum prediction tools in metabolomics

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/flexms-is-a-flexible-framework-for-benchmarking-deep-learning-based-mass-spectrum-prediction-tools-in-metabolomics

Paper ref

flexms-is-a-flexible-framework-for-benchmarking-deep-learning-based-mass-spectrum-prediction-tools-in-metabolomics

arXiv id

2602.22822

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

04f65a36a59af8f07a5afa8a10607e0d0625000c7836c6751497d2386da78dac

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

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FlexMS is a flexible framework for benchmarking deep learning-based mass spectrum prediction tools in metabolomics

FlexMS is a flexible framework for benchmarking deep learning-based mass spectrum prediction tools in metabolomics

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