MCP HubMCP Hub
스킬 목록으로 돌아가기

interpret-nmr-spectrum

pjt222
업데이트됨 2 days ago
8 조회
17
2
17
GitHub에서 보기
기타data

정보

이 스킬은 1차원 및 2차원 NMR 데이터(예: 1H, 13C, COSY, HSQC)를 분석하여 화학적 이동을 배정하고, 커플링 패턴을 해석하며, 다차원 상관관계를 통합합니다. 이를 통해 일관된 구조 단편을 제안함으로써 미지의 분자 구조를 규명하거나 합성 생성물을 확인하는 데 사용됩니다. 개발자들은 복잡하고 중첩된 데이터를 다룰 때 체계적인 스펙트럼 해석을 위해 이 스킬을 적용할 수 있습니다.

빠른 설치

Claude Code

추천
기본
npx skills add pjt222/agent-almanac -a claude-code
플러그인 명령대체
/plugin add https://github.com/pjt222/agent-almanac
Git 클론대체
git clone https://github.com/pjt222/agent-almanac.git ~/.claude/skills/interpret-nmr-spectrum

Claude Code에서 이 명령을 복사하여 붙여넣어 스킬을 설치하세요

문서

Interpret NMR Spectrum

Analyze 1D + 2D NMR → assign peaks, determine coupling, propose structural fragments consistent w/ all data.

Use When

  • Structure of unknown organic compound from NMR
  • Confirm identity + purity of synthesized product
  • Assign peaks in complex spectra w/ overlap
  • Correlate multi-exp (1H, 13C, DEPT, COSY, HSQC, HMBC) → unified picture
  • Distinguish regioisomers / stereoisomers / conformational

In

  • Req: NMR data (min 1H w/ shifts, multiplicities, integration)
  • Req: Mol formula / MW (from MS, EA)
  • Opt: 13C + DEPT (shifts + multiplicities)
  • Opt: 2D (COSY, HSQC, HMBC, NOESY/ROESY correlation tables)
  • Opt: Solvent + field strength
  • Opt: Known constraints (rxn starting material, IR confirmed groups)

Do

Step 1: Spectrum Type + Acquisition

Establish what data + quality before interpret:

  1. ID exp types: Catalog which avail (1H, 13C, DEPT-135, DEPT-90, COSY, HSQC, HMBC, NOESY, ROESY, TOCSY). Note nucleus + dimensionality.
  2. Acquisition params: Spectrometer freq (400 MHz, 600 MHz), solvent, temp, ref standard.
  3. Solvent + ref peaks: Locate + exclude.
Solvent1H Residual (ppm)13C Signal (ppm)
CDCl37.2677.16
DMSO-d62.5039.52
D2O4.79--
CD3OD3.3149.00
Acetone-d62.0529.84, 206.26
C6D67.16128.06
  1. Quality: Baseline flatness, multiplet res, S/N. Flag artifacts (spinning sidebands, 13C satellites, solvent impurity H2O ~1.56 ppm CDCl3).

→ Inventory of exps, solvent/ref peaks excluded, quality assessed.

If err: Poor S/N / severe baseline distortion → note limitation + cautious. Flag peaks indistinguishable from noise.

Step 2: 1H Chemical Shifts

Assign each 1H → environment using shift ranges:

  1. Tabulate: Per peak → shift (ppm), multiplicity, J (Hz), rel int.
  2. Classify by shift:
Range (ppm)EnvironmentExamples
0.0--0.5Shielded (cyclopropane, M-H)Cyclopropyl H, metal hydrides
0.5--2.0Alkyl (CH3, CH2, CH)Saturated aliphatic chains
2.0--4.5Alpha to heteroatom/unsaturation-OCH3, -NCH2, allylic, benzylic
4.5--6.5Vinyl / olefinic=CH-, =CH2
6.5--8.5AromaticArH
9.0--10.0Aldehyde-CHO
10.0--12.0Carboxylic acid-COOH
0.5--5.0 (broad, exchangeable)OH, NHAlcohols, amines, amides
  1. Count H: Integration ratios rel to formula → # protons per signal. Normalize simplest whole-# ratio.
  2. Exchangeable protons: Signals disappear on D2O shake (OH, NH, COOH) = exchangeable. Record presence + shift.

→ Table of 1H signals w/ shift, multiplicity, J, integration (# H), prelim env assignment.

If err: Integration doesn't sum to expected → check overlapping, broad peaks hidden in baseline, wrong formula.

Step 3: Coupling Patterns + J-Values

Extract connectivity from splitting:

  1. Multiplicities: s, d, t, q, dd, etc. Complex m → estimate # coupling partners.
  2. Measure J: Extract Hz. Match reciprocal (if H_A ↔ H_B J = 7.2, H_B shows same J to H_A).
  3. Classify J:
J Range (Hz)Coupling Type
0--3Geminal (2J) or long-range (4J, 5J)
6--8Vicinal aliphatic (3J)
8--10Vicinal with restricted rotation
10--17Vicinal olefinic cis (6--12) or trans (12--18)
0--3Aromatic meta
6--9Aromatic ortho
  1. Map coupling networks: Group mutually coupled protons → spin systems. Each = connected frag.
  2. Roof effect: AB-type → inner lines of doublets more intense → chemical shift proximity.

→ All J measured + matched reciprocally, spin systems ID'd, coupling types classified.

If err: Multiplets too complex for first-order → note higher-order pattern. Overlapping / strongly coupled (δν/J < 10) → non-first-order requires simulation.

Step 4: 13C + DEPT

Determine C types + count:

  1. Count distinct 13C signals: Compare # peaks vs formula. Fewer → symmetry.
  2. Classify by shift:
Range (ppm)Carbon TypeExamples
0--50sp3 AlkylCH3, CH2, CH, quaternary C
50--100Alpha to O or N-OCH3, -OCH2, anomeric C
100--150Aromatic / vinyl=CH-, ArC
150--170Heteroaromatic / enol / imineC=N, C-O aromatic
170--185Carboxyl / ester / amide-COOH, -COOR, -CONR2
185--220Aldehyde / ketone-CHO, >C=O
  1. DEPT editing: DEPT-135 (CH + CH3 up, CH2 down, quaternary absent) + DEPT-90 (CH only) → # attached H per C.
  2. DBE: DBE = (2C + 2 + N - H - X) / 2. Compare # π bonds + rings implied.

→ Every 13C signal classified by type (CH3, CH2, CH, C) + env, DBE consistent w/ observed groups.

If err: No DEPT → infer H attachment from HSQC (Step 5). C count ≠ formula → coincident signals / quaternary Cs in noise.

Step 5: 2D NMR

Build connectivity using 2D exps:

  1. COSY (1H-1H): Which H 2-3 bonds apart. Map cross-peaks → confirm+extend spin systems Step 3.
  2. HSQC (1H-13C 1-bond): Assign each H → directly bonded C. Links 1H + 13C unambiguously.
  3. HMBC (1H-13C long-range): 2-3 bond H-C. Critical for connecting frags across quaternary C, heteroatoms, carbonyls w/o direct H-C.
  4. NOESY/ROESY (through-space): H's spatially close (<5 Å) regardless bonding. → Stereochem + conformational.
  5. Build frag connectivity: HMBC → connect COSY spin systems → larger frags. Each HMBC cross-peak = 2-3 bond H-C path.

→ Connectivity map linking spin systems into coherent framework + stereochem from NOE where avail.

If err: 2D incomplete / ambiguous → note tentative connections. Multiple proposals poss. Prioritize HMBC → bridges gaps COSY can't.

Step 6: Propose + Validate Structure

Assemble frags → complete proposal:

  1. Assemble: Connect frags Steps 2-5 using HMBC + DBE constraints.
  2. Check formula: Proposed matches formula exactly (atom count, DBE).
  3. Back-predict shifts: For proposed → predict 1H + 13C shifts. Compare observed; deviations > 0.3 ppm (1H) / > 5 ppm (13C) → re-examine.
  4. Verify all correlations: Every COSY, HSQC, HMBC explained. Unexplained → error / impurity.
  5. Alternatives: Multiple structures fit → list distinguishing exps / correlations.
  6. Stereochem: NOE + J analysis (Karplus for dihedral) + known conformational prefs → relative + (where poss) absolute.

→ Single best-fit proposal w/ all NMR accounted, or ranked candidates + plan to distinguish.

If err: No single structure → check: mixture (extra peaks non-integer int), dynamic processes (broad peaks from conformational exchange), paramagnetic impurities (anomalous broadening). Re-examine formula if multiple equally viable.

Check

  • Solvent + ref peaks ID'd + excluded
  • Every 1H signal → shift region, multiplicity, J, integration
  • J reciprocal (matched between partners)
  • 13C classified by DEPT multiplicity + shift
  • DBE calc + consistent w/ proposed
  • 2D (COSY, HSQC, HMBC) all explained
  • Proposed matches formula exactly
  • Back-predicted shifts agree w/ observed within tolerance
  • Stereochem via NOE / J where applicable

Traps

  • Ignore solvent peaks: Common solvents → signals overlap analyte. Always ID + exclude residuals, water, grease.
  • Force 1st-order on 2nd-order: Strongly coupled nuclei (small Δshift rel J) → distorted multiplets, can't interpret w/ simple n+1. Roof effects + non-binomial intensity → indicators.
  • Overlook exchangeable: OH + NH may be broad, shift w/ conc/temp, absent in protic solvents. D2O shake → clarifies.
  • Assume all 13C visible: Quaternary Cs → long relax times + low int. May be absent short-acquisition. HMBC often only way to detect.
  • Misinterpret HMBC artifacts: HMBC → 1-bond artifacts (mis-assigned long-range) + weak 4-bond. Cross-check w/ HSQC → filter 1-bond leakthrough.
  • Neglect symmetry: Fewer 13C peaks than formula → symmetry element. Account before proposing.

  • interpret-ir-spectrum — func groups → constrain NMR structure
  • interpret-mass-spectrum — formula + frag for cross-val
  • interpret-uv-vis-spectrum — chromophores + conjugation extent
  • interpret-raman-spectrum — complementary vibrational → symmetric modes
  • plan-spectroscopic-analysis — select + sequence techniques pre-acquisition

GitHub 저장소

pjt222/agent-almanac
경로: i18n/caveman-ultra/skills/interpret-nmr-spectrum
0
agentsagentskillsai-assisted-developmentclaude-codeskillsteams

연관 스킬

llamaguard

기타

LlamaGuard는 폭력 및 혐오 발언 등 6가지 안전 범주에서 LLM 입력과 출력을 조정하기 위한 Meta의 70-80억 파라미터 모델입니다. 94-95% 정확도를 제공하며 vLLM, Hugging Face 또는 Amazon SageMaker를 사용해 배포할 수 있습니다. 이 기술을 사용하여 AI 애플리케이션에 콘텐츠 필터링 및 안전 가드레일을 손쉽게 통합하세요.

스킬 보기

cost-optimization

기타

이 Claude Skill은 리소스 적정화, 태깅 전략, 지출 분석을 통해 개발자들이 클라우드 비용을 최적화할 수 있도록 지원합니다. AWS, Azure, GCP에서 클라우드 비용을 절감하고 비용 거버넌스를 구현하기 위한 프레임워크를 제공합니다. 인프라 비용을 분석하거나, 리소스를 적정화하거나, 예산 제약을 충족해야 할 때 사용하세요.

스킬 보기

quantizing-models-bitsandbytes

기타

이 스킬은 bitsandbytes를 사용하여 LLM을 8비트 또는 4비트 정밀도로 양자화하며, 최소한의 정확도 손실로 50-75%의 메모리 감소를 달성합니다. 제한된 GPU 메모리에서 더 큰 모델을 실행하거나 추론을 가속화하는 데 이상적이며, INT8, NF4, FP4와 같은 형식을 지원합니다. 이 스킬은 HuggingFace Transformers와 통합되어 QLoRA 학습 및 8비트 옵티마이저를 가능하게 합니다.

스킬 보기

dispatching-parallel-agents

기타

이 Claude Skill은 3개 이상의 독립적인 문제를 동시에 조사하고 해결하기 위해 다중 에이전트를 배치합니다. 공유 상태나 의존성 없이 해결 가능한 무관련 장애 시나리오에 맞게 설계되었습니다. 핵심 기능은 병렬 문제 해결로, 각 독립 문제 영역마다 하나의 에이전트를 할당하여 효율성을 극대화합니다.

스킬 보기