3D Molecular Conformer Modeling for Novel MS/MS Spectrum Prediction
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Solution Overview
Problem
Current computational methods for automated compound identification from MS/MS spectra are limited, especially for novel compounds that have not been previously characterized, leading to a significant portion of MS/MS spectra remaining unidentified in applications like metabolomics and environmental chemistry.
Innovation Solution
A deep neural network, Mol3DNet, is developed to predict molecular properties using an elemental operation on 3D molecular conformers, incorporating MolConv for efficient feature extraction and transformation invariant operations, enabling accurate prediction of MS/MS spectra and other chemical properties through transfer learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional computational methods are used for automated compound identification from MS/MS spectra, then the method is simple and easy to implement, but the identification accuracy is limited especially for novel compounds
Solution Approach 1:
The patent replaces traditional rule-based and database-matching computational methods with a deep neural network system (Mol3DNet) that uses 3D molecular conformer processing and convolutional operations to predict MS/MS spectra, thereby improving identification accuracy for novel compounds while accepting increased system complexity
Solution Approach 2:
The patent transitions from 2D molecular representations to 3D molecular conformers with spatial coordinates, enabling the neural network to capture three-dimensional structural information that improves prediction accuracy for novel compound identification
2Measurement precision
If a deep neural network with multiple layers is used to extract molecular features, then the prediction accuracy of molecular properties is improved, but the computational complexity and training time increase
Solution Approach 1:
The patent performs preliminary generation of 3D molecular conformers and pre-processing of molecular input data before neural network processing, organizing atomic coordinates and features into structured point sets that reduce computational burden during the actual prediction phase
Solution Approach 2:
The patent divides the molecular feature extraction process into multiple convolutional layers, each extracting features at different levels of abstraction, allowing the network to build complex representations systematically while maintaining computational efficiency through modular processing
3Measurement precision
If 3D molecular conformers are used as input for prediction, then the prediction performance for molecular properties is significantly improved, but the data processing complexity increases
Solution Approach 1:
The patent extracts essential 3D geometric features and atomic properties from molecular structures, organizing them into standardized point set representations that contain only the critical information needed for prediction, thereby reducing processing complexity while maintaining high prediction performance
Solution Approach 2:
The patent transforms molecular data into standardized point set formats with specific parameter structures (atomic coordinates, atom types, features), enabling consistent processing across different molecular inputs and simplifying the overall data processing pipeline despite the complexity of 3D conformer analysis
Data Source
AI summary
Disclosed herein are methods and systems for the prediction of molecular properties from molecular 3-dimensional (3D) conformers. The method comprises receiving the compound information: generating a 3D molecular input point set from the compound information, wherein each atom point of the 3D molecular input point set comprises x, y, z-coordinates and one or more attributes: convoluting the 3D molecular input point set to generate a layer: generating one or more additional layers by repeating the convolution step: encoding the chemical compound by stacking the generated layers; and generating a report comprising one or more predicted properties of the encoded chemical compound.