ARXIV:2603.10950 · MOLECULAR STRUCTURE RETRIEVAL · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

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

arXiv

A framework for selectively predicting trusted molecular structures from mass spectra to reduce annotation errors in critical applications.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain A framework for selectively predicting trusted molecular structures from mass spectra to reduce annotation errors in critical applications.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

A framework for selectively predicting trusted molecular structures from mass spectra to reduce annotation errors in critical applications. In high-stakes applications such as clinical metabolomics and environmental screening, incorrect annotations can have serious consequences,…

METHOD

Full abstract

Machine learning methods for identifying molecular structures from tandem mass spectra (MS/MS) have advanced rapidly, yet current approaches still exhibit significant error rates. In high-stakes applications such as clinical metabolomics and environmental screening, incorrect annotations can have serious consequences, making it essential to determine when a prediction can be trusted. We introduce a selective prediction framework for molecular structure retrieval from MS/MS spectra, enabling models to abstain from predictions when uncertainty is too high. We formulate the problem within the risk-coverage tradeoff framework and comprehensively evaluate uncertainty quantification strategies at two levels of granularity: fingerprint-level uncertainty over predicted molecular fingerprint bits, and retrieval-level uncertainty over candidate rankings. We compare scoring functions including first-order confidence measures, aleatoric and epistemic uncertainty estimates from second-order distributions, as well as distance-based measures in the latent space. All experiments are conducted on the MassSpecGym benchmark. Our analysis reveals that while fingerprint-level uncertainty scores are poor proxies for retrieval success, computationally inexpensive first-order confidence measures and retrieval-level aleatoric uncertainty achieve strong risk-coverage tradeoffs across evaluation settings. We demonstrate that by applying distribution-free risk control via generalization bounds, practitioners can specify a tolerable error rate and obtain a subset of annotations satisfying that constraint with high probability.

RESULT

ScienceToStartup currently rates this 5.0/10 on the public viability pass. Our analysis reveals that while fingerprint-level uncertainty scores are poor proxies for retrieval success, computationally inexpensive first-order confidence measures and retrieval-level aleatoric uncertainty achieve…

WHY NOW

Molecular Structure Retrieval 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

PainA framework for selectively predicting trusted molecular structures from mass spectra to reduce annotation errors in critical applications.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

A framework for selectively predicting trusted molecular structures from mass spectra to reduce annotation errors in critical applications.

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

ARXIV:2603.10950 · MOLECULAR STRUCTURE RETRIEVAL · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

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

arXiv

A framework for selectively predicting trusted molecular structures from mass spectra to reduce annotation errors in critical applications.

Blocked on Code›Score5.0Evidence unverified

Opportunity summary

Pain A framework for selectively predicting trusted molecular structures from mass spectra to reduce annotation errors in critical applications.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Molecular Structure Retrieval 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

PainA framework for selectively predicting trusted molecular structures from mass spectra to reduce annotation errors in critical applications.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

A framework for selectively predicting trusted molecular structures from mass spectra to reduce annotation errors in critical applications.

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

Paper Pack

10.48550/arXiv.2603.10950

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

A framework for selectively predicting trusted molecular structures from mass spectra to reduce annotation errors in critical applications.

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 fallback54 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

Molecular Structure Retrieval 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
A framework for selectively predicting trusted molecular structures from mass spectra to reduce annotation errors in critical applications. In high-stakes applications such as clinical metabolomics and environmental screening, incorrect annotations can have serious consequences, making it essential to determine when a prediction can be trusted.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Machine learning methods for identifying molecular structures from tandem mass spectra (MS/MS) have advanced rapidly, yet current approaches still exhibit significant error rates. In high-stakes applications such as clinical metabolomics and environmental screening, incorrect annotations can have serious consequences, making it essential to determine when a prediction can be trusted.
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. Our analysis reveals that while fingerprint-level uncertainty scores are poor proxies for retrieval success, computationally inexpensive first-order confidence measures and retrieval-level aleatoric uncertainty achieve strong risk-coverage tradeoffs across evaluation settings.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Molecular Structure Retrieval 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

Molecular Structure Retrieval

Competitors

not indexed

Competitive landscape

A framework for selectively predicting trusted molecular structures from mass spectra to reduce annotation errors in critical applications.

Segment

Molecular Structure Retrieval

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 2603.10950 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

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PDF

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

References(54)

Small molecule retrieval from tandem mass spectrometry: what are we optimizing for?

2026G. D. Waele, Marek Wydmuch et al.

Uncertainty Quantification for Machine Learning: One Size Does Not Fit All

2025Paul Hofman, Yusuf Sale et al.

Breaking the Modality Barrier: Generative Modeling for Accurate Molecule Retrieval from Mass Spectra

2025Yiwen Zhang, Keyan Ding et al.

MS-BART: Unified Modeling of Mass Spectra and Molecules for Structure Elucidation

2025Yang Han, Pengyu Wang et al.

Out-of-Distribution Detection Methods Answer the Wrong Questions

2025Yucen Lily Li, Daohan Lu et al.

CSU-MS2: A Contrastive Learning Framework for Cross-Modal Compound Identification from MS/MS Spectra to Molecular Structures.

2025Ting Xie, Hailiang Zhang et al.

DiffMS: Diffusion Generation of Molecules Conditioned on Mass Spectra

2025Montgomery Bohde, Mrunali Manjrekar et al.

JESTR: Joint Embedding Space Technique for Ranking candidate molecules for the annotation of untargeted metabolomics data

2024Apurva Kalia, Dilip Krishnan et al.

MassSpecGym: A benchmark for the discovery and identification of molecules

2024Roman Bushuiev, Anton Bushuiev et al.

Label-wise Aleatoric and Epistemic Uncertainty Quantification

2024Yusuf Sale, Paul Hofman et al.

MS2Mol: A transformer model for illuminating dark chemical space from mass spectra

2023Thomas Butler, Abe Frandsen et al.

Annotating metabolite mass spectra with domain-inspired chemical formula transformers

2022Samuel Goldman, Jeremy Wohlwend et al.

The critical role that spectral libraries play in capturing the metabolomics community knowledge

2022Wout Bittremieux, Mingxun Wang et al.

Selection by Prediction with Conformal p-values

2022Ying Jin, E. Candès

Out-of-distribution Detection with Deep Nearest Neighbors

2022Yiyou Sun, Yifei Ming et al.

The emerging role of mass spectrometry-based proteomics in drug discovery

2022F. Meissner, Jennifer Geddes-McAlister et al.

Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation

2022Viktor Bengs, Eyke Hüllermeier et al.

Generalized Out-of-Distribution Detection: A Survey

2021Jingkang Yang, Kaiyang Zhou et al.

Chemformer: a pre-trained transformer for computational chemistry

2021Ross Irwin, Spyridon Dimitriadis et al.

MSNovelist: de novo structure generation from mass spectra

2021Michael A. Stravs, Kai Dührkop et al.

Showing 20 of 54 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 WorkFlexMS is a flexible framework for benchmarking deep learning-based mass spectrum prediction tools in metabolomics

5.0

Prior WorkSpecTM: Spectral Targeted Masking for Trustworthy Foundation Models

5.0

Extension

Builds On ThisPhysical probes expose and alleviate chemical-environment collapse in molecular representations

3.0

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

2.0

Builds On ThisAgentic Molecular Recovery via Molecule-Aware Exploration

0.0

Commercially relevant

Higher ViabilityCoRe-Gen: Robust Spectrum-to-Structure Generation under Imperfect Fingerprint Conditions

8.0

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

7.0

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

8.0

Higher ViabilityDelving Aleatoric Uncertainty in Medical Image Segmentation via Vision Foundation Models

7.0

Higher ViabilityConditional Generative Framework with Peak-Aware Attention for Robust Chemical Detection under Interferences

8.0

Conflicting

none indexed

Owned Distribution

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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: 2603.10950
Route: /paper/when-should-we-trust-the-annotation-selective-prediction-for-molecular-structure-retrieval-from-mass-spectra
Active tab: read
Artifact: when-should-we-trust-the-annotation-selective-prediction-for-molecular-structure-retrieval-from-mass-spectra

Available agents

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

API/MCP endpoints

REST paper pack API/api/v1/paper/when-should-we-trust-the-annotation-selective-prediction-for-molecular-structure-retrieval-from-mass-spectra/paper-pack
REST build passport API/api/v1/paper/when-should-we-trust-the-annotation-selective-prediction-for-molecular-structure-retrieval-from-mass-spectra/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/when-should-we-trust-the-annotation-selective-prediction-for-molecular-structure-retrieval-from-mass-spectra

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

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

Canonical ID when-should-we-trust-the-annotation-selective-prediction-for-molecular-structure-retrieval-from-mass-spectra | Route /paper/when-should-we-trust-the-annotation-selective-prediction-for-molecular-structure-retrieval-from-mass-spectra

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/when-should-we-trust-the-annotation-selective-prediction-for-molecular-structure-retrieval-from-mass-spectra

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: When should we trust the annotation? Selective prediction for molecular structure retrieval from mass spectra

/buildability/when-should-we-trust-the-annotation-selective-prediction-for-molecular-structure-retrieval-from-mass-spectra

Watchwatch

Subject: When should we trust the annotation? Selective prediction for molecular structure retrieval from mass spectra

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/when-should-we-trust-the-annotation-selective-prediction-for-molecular-structure-retrieval-from-mass-spectra

Paper ref

when-should-we-trust-the-annotation-selective-prediction-for-molecular-structure-retrieval-from-mass-spectra

arXiv id

2603.10950

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

d2019bdecd411ddf32abab54294695f9e8917fe1e5d0565a685c9c76761ac343

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

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

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