| import numpy as np
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| from rdkit import Chem
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|
|
| import IsoSpecPy as iso
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|
|
|
|
| def generate_isotopic_pattern(mol_formula, charge=1):
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| """生成理论同位素模式"""
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|
|
|
|
|
|
|
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| calculator = iso.IsoTotalProb(formula=mol_formula,
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| charge=charge,
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| absolute_accuracy=0.9)
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|
|
|
|
| peaks = calculator.get_peaks()
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|
|
|
|
| intensities = np.array([p[1] for p in peaks])
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| intensities /= np.max(intensities)
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|
|
| return [(p[0], intensities[i]) for i, p in enumerate(peaks)]
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|
|
|
|
| def compare_isotopic_patterns(exp_pattern, theory_pattern, tolerance=0.01):
|
| """比较实验与理论同位素模式"""
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| score = 0.0
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| matched_peaks = 0
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|
|
| for exp_mz, exp_int in exp_pattern:
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| for theo_mz, theo_int in theory_pattern:
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| if abs(exp_mz - theo_mz) <= tolerance:
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|
|
| score += exp_int * theo_int
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| matched_peaks += 1
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| break
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|
|
|
|
| if matched_peaks > 0:
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| score /= matched_peaks
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| return score
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|
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|
|
|
| if __name__ == "__main__":
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|
|
| experimental_data = [
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| (151.063, 1.00),
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| (152.066, 0.32),
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| (153.069, 0.05)
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| ]
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|
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|
|
| candidate_formulas = ["C8H10N2O2", "C7H10N4O", "C9H10O3"]
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|
|
| best_formula = None
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| best_score = -1
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|
|
| for formula in candidate_formulas:
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|
|
| theoretical_pattern = generate_isotopic_pattern(formula, charge=1)
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|
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|
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| score = compare_isotopic_patterns(experimental_data, theoretical_pattern)
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|
|
| print(f"Formula: {formula} | Score: {score:.4f}")
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|
|
| if score > best_score:
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| best_score = score
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| best_formula = formula
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|
|
| print(f"\nBest match: {best_formula} (Score: {best_score:.4f})")
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|
|
