Molecular Olfactory Prediction via Surface Point Segmentation
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Solution Overview
Problem
Existing techniques for predicting olfactory properties of molecules do not effectively consider local properties of molecular surfaces, leading to inaccuracies in odor prediction and perception.
Innovation Solution
A computer-based method that utilizes a spatial surface representation of molecules to select surface points, obtain local physicochemical properties, and input this data into a Machine-Learned model to predict olfactory properties, including odor primaries and scent intensity, enabling the generation of desired odors and molecular structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional molecular representation methods are used, then the prediction process is simple, but the accuracy of olfactory property prediction deteriorates due to ignoring local surface properties
Solution Approach 1:
The patent segments the molecular surface into multiple discrete points, where each point represents local physicochemical properties. This segmentation allows the model to capture local surface characteristics that were previously ignored, directly improving prediction accuracy while maintaining computational tractability through structured data organization.
Solution Approach 2:
The patent applies local quality by assigning specific physicochemical properties (such as electronegativity, hydrophobicity, and curvature) to each surface point individually. This enables the model to consider local variations in molecular surface properties, resolving the contradiction between simple representation and accurate local feature capture.
2Reliability
If global molecular properties only are considered, then the computational model remains simple, but the reliability of odor prediction deteriorates due to lack of local surface information
Solution Approach 1:
The patent transitions from considering only global molecular properties to incorporating local surface point properties as an additional dimension of information. Each surface point contributes multiple physicochemical features, enriching the input space and improving prediction reliability without requiring a fundamentally new model architecture.
Solution Approach 2:
The patent creates a composite representation by combining global molecular descriptors with local surface point properties. This composite feature vector integrates multiple levels of molecular information, enhancing prediction reliability while using standard machine learning techniques to process the combined data.
3Measurement precision
If detailed local surface properties are captured, then the accuracy of olfactory feature prediction improves, but the amount of data processing required increases
Solution Approach 1:
The patent extracts only the most relevant physicochemical properties from each surface point (such as curvature, electronegativity, and hydrophobicity), rather than processing all possible molecular descriptors. This selective extraction maintains high prediction accuracy while reducing the dimensionality and processing requirements of the input data.
Data Source
AI summary
There are provided system and method of predicting data related to olfactory properties of a molecule characterized by a chemical structure. The method comprises: upon obtaining data informative of a spatial surface representation (SSR) of molecule corresponding to the chemical structure thereof, selecting on SSR a plurality of N surface points; for each selected surface point, obtaining local data informative of spatial location on SSR and local physicochemical properties of the selected surface point, thus giving rise to a surface points representation (SPR); inputting data informative of SPR into a Machine-Learned (ML) model trained to provide, in accordance with SPR, prediction data related to at least one olfactory property; and receiving, as an output of the ML model, prediction data related to the at least one olfactory property of the molecule. There are also provided system and method of predicting molecular chemical structure enabling one or more olfactory properties.