用图生成模型高效探索香味分子，还能预测气味特征。
Navigating the Fragrance space Via Graph Generative Models And Predicting Odors
- 基于图神经网络生成新香味分子并验证有效性
- 气味预测准确率达AUC 0.97，可关联理化特性
- 支持可解释性分析，适合香料研发与嗅觉研究
我们探索了一系列生成建模技术，以高效导航和探索气味及更广泛的化学空间。不同于传统方法，我们不仅生成分子，还预测其气味可能性（ROC AUC达0.97），并分配可能的气味标签。通过机器学习将气味可能性与分子理化特性相关联，并利用SHAP方法展示函数的可解释性。整个流程包含四个关键阶段：分子生成、严格的分子有效性验证、香味可能性筛选以及生成分子的气味预测。通过公开代码和训练好的模型，我们旨在促进该研究在香料发现和嗅觉研究中的广泛应用。
原文摘要 · Abstract (English)
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.
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