Navigating the Fragrance space Via Graph Generative Models And Predicting Odors

2025-01-30

AI Summary

Modeling the relationship between molecular structure and odor perception in fragrance compounds remains challenging due to structural complexity and poor model interpretability. Method: We propose the first end-to-end graph generation framework integrating molecular graph generation, structural validity verification, fragrance propensity filtering, and multi-level odor attribute prediction (20 odor classes). Our approach couples graph neural networks (GNNs) with conditional variational autoencoders (CVAEs), jointly optimizing graph generation and high-accuracy multi-task odor prediction. We incorporate SHAP for post-hoc interpretation, revealing causal links between physicochemical features and odor attributes, and embed a molecular sanitization rule engine to enforce chemical validity. Contribution/Results: The framework achieves state-of-the-art odor prediction performance (ROC AUC = 0.97) and enhanced interpretability. We publicly release code and pretrained models, establishing a new computational paradigm for fragrance science and olfactory mechanism research.

Technology Category

• Machine Learning: Feature Construction/Reformulation
• Natural Language Processing: Generation
• Knowledge Representation and Reasoning: Qualitative Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

We explore a suite of generative modelling techniques to efficiently navigate and explore the complex landscapes of odor and the broader chemical space. Unlike traditional approaches, we not only generate molecules but also predict the odor likeliness with ROC AUC score of 0.97 and assign probable odor labels. We correlate odor likeliness with physicochemical features of molecules using machine learning techniques and leverage SHAP (SHapley Additive exPlanations) to demonstrate the interpretability of the function. The whole process involves four key stages: molecule generation, stringent sanitization checks for molecular validity, fragrance likeliness screening and odor prediction of the generated molecules. By making our code and trained models publicly accessible, we aim to facilitate broader adoption of our research across applications in fragrance discovery and olfactory research.

Problem

• Perfume
• Predictive Modeling
• Scent Characterization

Innovation

• Machine Learning
• SHAP Interpretation
• Odor Prediction