PRIMITIVE TRANSDUCTION ARCHITECTURE FOR MOLECULAR STRUCTURE RECONSTRUCTION FROM LC-MS/MS SPECTRA

Description PRIMITIVE TRANSDUCTION ARCHITECTURE FOR MOLECULAR STRUCTURE RECONSTRUCTION FROM LC-MS/MS SPECTRA This article presents a computational framework for reconstructing molecular structures from liquid chromatography–tandem mass spectrometry (LC-MS/MS) data. The proposed methodology, designated PAST-Mol (Primitive Architecture Spectral Transduction for Molecular Reconstruction), organizes the reconstruction process through a sequence of spectral observation, latency, relational analysis, structural generation, refinement, memory, coverage, branching, recovery, ranking, and computational audit. The framework treats the mass spectrum as an observed representation of molecular structure and establishes a computational transition between spectral data and molecular graphs. It combines spectral relationships, candidate retrieval, de novo molecular generation, structural refinement, and ranking procedures to produce possible molecular representations in SMILES format. The proposed architecture includes mechanisms for handling spectral ambiguity, incomplete information, alternative structural hypotheses, candidate recovery, and computational verification. Its evaluation is designed to consider structural validity, exact structure matching, molecular formula accuracy, spectral similarity, graph similarity, top-k performance, candidate coverage, recovery rate, and Mean Reciprocal Rank at 25 (MRR@25). The work distinguishes between the proposed hypothesis, mathematical formulation, computational implementation, experimental observations, and subsequent interpretation. The architecture is presented as a testable computational framework rather than as an established chemical law or a guarantee of unique molecular reconstruction. The proposed methodology is intended for research in computational chemistry, mass spectrometry, molecular structure elucidation, inverse problems, artificial intelligence, and structural transduction systems.