Olfactory Profile Prediction Using Multi-Model Fusion
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Solution Overview
Problem
The study of olfactory perception has been limited due to the complexity of interactions between olfactory receptors and volatile molecules, and the scarcity of comprehensive olfactory datasets, making it challenging for AI to predict olfactory profiles accurately.
Innovation Solution
The use of multiple representations of molecules, such as textual and graph-based representations, in conjunction with machine-learning models, allows for a more complete composite representation of molecules, enhancing the prediction accuracy of olfactory profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single molecular representation (textual or graph-based) is used for prediction, then the model is simpler and faster to train, but the prediction accuracy is limited due to incomplete molecular information
Solution Approach 1:
The patent combines multiple molecular representations (textual SMILES strings and graph-based molecular structures) into a unified prediction framework. Two separate machine learning models process each representation type, and their predictions are merged through a combination strategy (weighted averaging or stacking) to produce the final olfactory profile prediction, thereby achieving more complete molecular information utilization while managing complexity through modular architecture
Solution Approach 2:
The patent creates a composite prediction system that integrates multiple types of molecular representations analogous to composite materials. Just as composite materials combine different substances to achieve superior properties, the system combines textual and graph-based representations to achieve more accurate and comprehensive olfactory predictions than any single representation could provide alone
2Measurement precision
If comprehensive olfactory datasets are used for training, then the prediction accuracy improves, but the data scarcity and labeling complexity increase the difficulty of model training
Solution Approach 1:
The patent employs partial action by training separate models on different subsets of the training data corresponding to different molecular representations. Each model is trained on the portion of data relevant to its representation type, and the combined predictions achieve comprehensive coverage without requiring each individual model to process the entire dataset, thereby reducing training complexity while maintaining accuracy
Solution Approach 2:
The patent segments the training process into distinct components: one model is trained on textual representations and another on graph-based representations. This segmentation allows each model to specialize in its representation type, making training more manageable and efficient while the combination of both models provides comprehensive prediction coverage
Data Source
AI summary
Aspects of the present disclosure relate to an apparatus for predicting an olfactory profile of a molecule, the apparatus comprising memory circuitry, machine-readable instructions, and processor circuitry to execute the machine-readable instructions to obtain a first representation of the molecule and a second representation of the molecule, process the first representation using at least one first machine-learning model to obtain a first predicted olfactory profile of the molecule, process the second representation using at least one second machine-learning model to obtain a second predicted olfactory profile of the molecule, process the first predicted olfactory profile and the second predicted olfactory profile, or a combined version of the first predicted olfactory profile and the second predicted olfactory profile, using a third machine-learning model, the third machine-learning model being trained to output a third predicted olfactory profile of the molecule.