Odor Prediction Tool Using ML for Receptor Binding
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Solution Overview
Problem
Current technologies lack an effective method to predict and understand olfactory perceptions, as the relationship between odorant molecules and olfactory receptor interactions is complex and not easily reducible to simple representations.
Innovation Solution
An odor prediction tool that uses machine learning models to predict odorant-receptor interactions by generating odorant and olfactory receptor weight vectors based on molecular and protein features, allowing for the prediction of olfactory perceptions associated with an odorant molecule.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to represent odorant-receptor interactions, then the representation is simple, but the prediction accuracy of olfactory perceptions is poor
Solution Approach 1:
The patent replaces traditional mechanical/chemical binding models with a deep learning-based computational model. The system uses neural networks to process molecular structures and protein features, generating predictions of odorant-receptor interactions and olfactory percepts. This substitution enables accurate prediction while managing complexity through algorithmic approaches rather than physical measurements.
Solution Approach 2:
The patent transforms the representation of odorant-receptor interactions from binary binding models to continuous probability distributions. The system outputs likelihood scores for receptor activation and percept predictions, allowing for nuanced representation of interaction strength. This parameter transformation enables more accurate predictions while maintaining computational tractability.
2Reliability
If multiple molecules activate the same receptor and multiple receptors are activated by the same molecule, then the representation captures biological reality, but the relationship becomes intricate and difficult to reduce to simple representations
Solution Approach 1:
The patent segments the complex odorant-receptor-percept relationship into distinct computational stages: (1) processing molecular structure features, (2) processing receptor protein features, (3) predicting interaction probabilities, and (4) generating percept predictions. This segmentation allows the system to handle many-to-many relationships systematically while maintaining biological accuracy.
Solution Approach 2:
The patent introduces intermediate representations including odorant embeddings, receptor embeddings, and interaction probability vectors. These intermediaries bridge the gap between molecular structures and perceptual outputs, allowing the complex relationship to be represented through structured computational steps rather than direct mapping.
3Measurement precision
If deep learning models are used to predict odorant-receptor interactions, then the prediction capability improves, but the computational resources and model complexity increase
Solution Approach 1:
The patent performs preliminary computations by pre-processing molecular structures into standardized feature representations and pre-training the deep learning model on existing odorant-receptor data. This preliminary action reduces the computational burden during actual prediction, as the model only needs to process new inputs through the pre-trained network rather than training from scratch.
Data Source
AI summary
Systems and methods for determining predicted olfactory perception are provided. In particular, a method comprises receiving an input indicating an odorant, generating an odorant vector representing the odorant, generating an olfactory receptor vector, and determining one or more predicted olfactory percepts associated with the odorant based on the odorant vector and the olfactory receptor vector.